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Functional Magnetic Resonance Study of the Central Effects of Acute Acupuncture on Glucose Levels and Core Body Temperat...

Permanent Link: http://ufdc.ufl.edu/UFE0041181/00001

Material Information

Title: Functional Magnetic Resonance Study of the Central Effects of Acute Acupuncture on Glucose Levels and Core Body Temperature in Men
Physical Description: 1 online resource (93 p.)
Language: english
Creator: Von Deneen, Karen
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: acupuncture, addiction, glucose, obesity, overweight, pws, satiety, temperature, yinlingquan, zusanli
Veterinary Medicine -- Dissertations, Academic -- UF
Genre: Veterinary Medical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Obesity is a major health problem worldwide and acupuncture is an effective treatment modality for this condition. This study focused on how acupoints ST 36 (Zusanli) and SP 9 (Yuan Ling Quan) and their sham acupoints acutely act on the limbic system via dopamine to affect satiety, glucose (Glu) blood levels and core body temperature (CBT) in healthy, overweight, adult, non-dieting Chinese males who have abstained from eating 12 hours prior to the functional magnetic resonance imaging (fMRI) experiment. Results for Glu and CBT indicated no significance (p > 0.05) in both inter- and intragroup comparisons due to variable individual responses to treatment. Hunger survey feedback was significant (p < 0.05) between the acupuncture (ACU) and sham control (min SHAM) groups. Soreness or Deqi, was the only significant (p < 0.05) intergroup sensation. Connectivity analysis utilized the amygdala (AMY) and hypothalamus (HYP) as regions of interest (ROIs). Common overlapping regions for both ACU and min SHAM using the AMY as the ROI were the putamen, caudate, parahippocampus, hippocampus (HIPP), insula, cingulate, and Brodmann areas (BA) 6, 21, 22, 24, 25, 30, 31, 34, 36, 40, and 47. The common overlapping regions for both ACU and min SHAM using the HYP as the ROI were the HIPP, putamen, thalamus, and BA 28, 32, 38, 40, and 47. These areas are involved in dopamine, basal metabolic rate, heart rate, cognition and satiety regulation. This project will be of great importance in helping understand how acupuncture can be a safe, inexpensive, and highly effective treatment modality for weight control.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Karen Von Deneen.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Liu, Yijun.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-06-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041181:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041181/00001

Material Information

Title: Functional Magnetic Resonance Study of the Central Effects of Acute Acupuncture on Glucose Levels and Core Body Temperature in Men
Physical Description: 1 online resource (93 p.)
Language: english
Creator: Von Deneen, Karen
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: acupuncture, addiction, glucose, obesity, overweight, pws, satiety, temperature, yinlingquan, zusanli
Veterinary Medicine -- Dissertations, Academic -- UF
Genre: Veterinary Medical Sciences thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Obesity is a major health problem worldwide and acupuncture is an effective treatment modality for this condition. This study focused on how acupoints ST 36 (Zusanli) and SP 9 (Yuan Ling Quan) and their sham acupoints acutely act on the limbic system via dopamine to affect satiety, glucose (Glu) blood levels and core body temperature (CBT) in healthy, overweight, adult, non-dieting Chinese males who have abstained from eating 12 hours prior to the functional magnetic resonance imaging (fMRI) experiment. Results for Glu and CBT indicated no significance (p > 0.05) in both inter- and intragroup comparisons due to variable individual responses to treatment. Hunger survey feedback was significant (p < 0.05) between the acupuncture (ACU) and sham control (min SHAM) groups. Soreness or Deqi, was the only significant (p < 0.05) intergroup sensation. Connectivity analysis utilized the amygdala (AMY) and hypothalamus (HYP) as regions of interest (ROIs). Common overlapping regions for both ACU and min SHAM using the AMY as the ROI were the putamen, caudate, parahippocampus, hippocampus (HIPP), insula, cingulate, and Brodmann areas (BA) 6, 21, 22, 24, 25, 30, 31, 34, 36, 40, and 47. The common overlapping regions for both ACU and min SHAM using the HYP as the ROI were the HIPP, putamen, thalamus, and BA 28, 32, 38, 40, and 47. These areas are involved in dopamine, basal metabolic rate, heart rate, cognition and satiety regulation. This project will be of great importance in helping understand how acupuncture can be a safe, inexpensive, and highly effective treatment modality for weight control.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Karen Von Deneen.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Liu, Yijun.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-06-30

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041181:00001


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1 FUNCTIONAL MAGNETIC RESONANCE STUDY OF THE CENTRAL EFFECTS OF ACUTE ACUPUNCTURE ON GLUCOSE LEVELS AND CORE BODY TEMPERATURE IN MEN By KAREN MILADA von DENEEN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2009

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2 2009 Karen Milada von Deneen

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3 To the research group in the Life Science Research Center of XiDian University and Medical Image Processing Group of the Institute of Automation in the Chinese Academy of Sciences; Beijing University of Chinese Medicine; the Yijun Liu laboratory in the Department of Psychiatry; faculty and staff at the College of Veterinary Medicine; and Jesus C hrist

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4 ACKNOWLEDGMENTS I want to first and foremost thank the faculty, students, and staff of the Life Science Research Center of XiDian University Medical Image Processing Group of the Institute of Automation in the Chinese Academy of Sciences and t he Beijing University of Chinese Medicine for their financial support, resources, and assistance in making this project possible. I want to specifically thank the Yijun Liu laboratory in the Department of Psychiatry, the College of Veterinary Medicine especially Sally OConnell, and Dr. Mary Browns lab. I want to also thank my family, friends, Dr. Frank Skidmore, Phil Winters, and mostly God for getting me through this

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 7 LIST OF FIGURES .......................................................................................................... 8 LIST OF ABBREVIATIONS ............................................................................................. 9 ABSTRACT ................................................................................................................... 12 CHAPTE R 1 LITERATURE REVIEW .......................................................................................... 14 Introduction ............................................................................................................. 14 Background ............................................................................................................. 16 Acupuncture Overview ..................................................................................... 16 Acupuncture Effects on Physiology .................................................................. 17 Acupuncture Affects Satiety ............................................................................. 17 Acupuncture Treats Obesit y ............................................................................. 18 Causes of General Obesity .............................................................................. 19 Prader Willi Syndrome Background: Causes and Symptoms ........................... 20 Food Addiction Background: Causes and Symptoms ...................................... 22 Shared Anatomical Areas in the Brain .............................................................. 24 Hypothalamic Regulation of Appetite/Afferent Hormonal Signaling .................. 25 Endocrinology of Obesity .................................................................................. 27 Orexigenic and Anorexigenic Pathways ........................................................... 29 Food Cues: Intern al and External Appetite Triggers ......................................... 30 Reward System Hypothesis ............................................................................. 38 Neuroimaging Studies ...................................................................................... 40 Treatments for PWS and FA ............................................................................ 42 2 AIMS, MATERIALS AND METHODS ..................................................................... 47 Aims ........................................................................................................................ 47 Rationale ................................................................................................................. 47 Subjects .................................................................................................................. 48 Experimental Design ............................................................................................... 49 Physiological Measurements .................................................................................. 49 Treatment Methods ................................................................................................. 50 fMRI Parameters ..................................................................................................... 51 Preprocessing of Data and Analysis ....................................................................... 51

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6 3 RESULTS ............................................................................................................... 54 Parametric Test Statistics ....................................................................................... 54 Nonparametric Test Statistics ................................................................................. 55 Physiological Data .................................................................................................. 5 7 Neuroimaging Data ................................................................................................. 60 4 DISCUSSION AND CONCLUSION ........................................................................ 72 Discussion .............................................................................................................. 72 Conclusion .............................................................................................................. 75 Study Limitations .................................................................................................... 76 LIST OF REFERENCES ............................................................................................... 77 BIOGRAPHICAL SKETCH ............................................................................................ 93

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7 LIST OF TABLES Table page 2 1 Inclusion and exclusion criteria used to select subjects for this experiment. ...... 48 3 1 Overlapping areas during acupuncture treatment from functional connectivity analysis using the amygdala as the region of interest. ....................................... 64 3 2 Overlapping areas during minimal sham acupuncture treatment from functional connectivity analy sis using the amygdala as the region of interest. ... 66 3 3 Overlapping areas during acupuncture treatment from functional connectivity analysis using the hypothalamus as the region of int erest. ................................ 68 3 4 Overlapping areas during minimal sham acupuncture treatment from functional connectivity analysis using the hypothalamus as the region of interest. ............................................................................................................... 70 3 5 Common overlapping areas during acupuncture and minimal sham acupuncture treatments from functional connectivity analysis using the amygdala and hypothalamus as the regions of interest. ..................................... 71

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8 LIST OF FIGURES Figure page 3 1 Measurement of corrected average core body temperature (CBT) in degrees Celsius shown before (PreAcu), during (Acu) and after (Post Acu) acupuncture (ACU) or minimal sham (min SHAM) treatments in overweight adult Chinese males (n = 10 for ACU and n = 9 f or min SHAM). ........................ 57 3 2 Measurement of uncorrected av erage blood glucose (Glu) shown before (Pre Acu), during (Acu) and after (Post Acu) real acupuncture (ACU) or minimal sham (min SHAM) treatment in overweight adult Chinese males (n=10 for ACU and n = 9 for min SHAM). ........................................................... 58 3 3 Averaged major Deqi sensations (soreness, numbness, fullness, coolness, warmth, sharp pain, dull pain, and heaviness) comparison between real acupuncture (ACU) and minimal sham (min SHAM) treatments in overweight adult Chinese males (n=10 in ACU and n = 9 min SHAM). ................................ 59 3 4 Averaged hunger sensation comparison between real acupuncture (ACU) and minimal sham (min SHAM) treatments in overweight adult Chinese males (n=10 in ACU and n = 9 in min SHAM). ................................................... 60 3 5 Blood oxygen level dependent significant brain regions from an amygdalarelated functional connectivity analysis compa ring real acupuncture (ACU) versus minimal sham (SHAM) treated individuals. ............................................. 62 3 6 Blood oxygen level dependent significant brain regions from a hypothalamus related functional connectivity analysis comparing real acupuncture (ACU) versus minimal sham (SHAM) treated individuals. ............................................. 63

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9 LIST OF ABBREVIATION S ACC Anterior cingulate cortex Ach Acetylcholine ACU Real acupuncture AgrP Agouti related protei n AMY Amygdala ARC Arcuate nucleus ARN Appetite regulating network BA Brodmann area BLA Basolateral amygdala BMA Basomedial amygdala BMI Body mass index BMR Basal metabolic rate BOLD Blood oxygen level dependent CART Cocaineamphetamine regulating transcr ipt CBT Core body temperature CN Caudate nucleus CNS Central nervous system CRH Corticotropin releasing hormone DA Dopamine DCT Discrete cosine transform analysis DLPFC Dorsolateral prefrontal cortex EAS Electroacupuncture stimulation EPI Echoplanar imaging FA Food addiction

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10 fMRI Functional magnetic resonance imaging FOV Field of view FWHM F ull width at half maximum Gaussian kernel analysis GABA Gamma aminobutyric acid GCM Granger causality method analysis GH Growth hormone Glu Glucose HIPP Hippocampus HYP Hypothalamus IAPS International Affective Picture System ILA Infralimbic area LA Lateral amygdala LHA Lateral hypothalamus MC4 Melanocortin4 receptor MCH Melanin concentrating hormone MFD Mesolimbic frontocortical dopamine system MNI Montreal Neurological Institute mOFC Medial orbitofronal cortex mPFC Medial prefrontal cortex mPVN Magnocellular paraventricular nucleus nACC Nucleus accumbens NCCAM National Center for Complementary and Alternative Medicine NIH National Institute of Health NPY Neuropeptide Y OFC Orbitofrontal cortex

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11 ORX Orexin PET Positron resonance imaging PL Paralimbic area POMC Pro opiomelanocortin pPVN Parvocellular paraventricular nucleus PVN Paraventricular nuclei PWS Prader Willi syndrome ROIs Regions of interest SCA Seed voxel correlat ion analysis SHAM Minimal sham acupuncture TCM Traditional Chinese Medicine THC Delta 9 tetrahydrocannabinol VMH Ventromedial hypothalamus VMPFC Ventromedial prefrontal cortex vPFC Ventral prefrontal cortex WFU Wake Forest University MSH Alpha melanocytestimulating hormone

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12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FUNCTIONAL MAGNETIC RESONA NCE STUDY OF THE CENTRAL EFFECTS OF ACUTE ACUPUNCTURE ON GLUCOSE LEVELS AND CORE BODY TEMPERAT URE IN MEN By Karen Milada von Deneen December 2009 Chair: Yijun Liu Major: Veterinary Medical Sciences Obesity is a major health problem worldwide and acupunc ture is an effective treatment modality for this condition. This study focused on how acupoints ST 36 (Zusanli) and SP 9 (Yuan Ling Quan) and their sham acupoints acutely act on the limbic system via dopamine to affect satiety, glucose (Glu) blood levels and core body temperature (CBT) in healthy, overweight, adult, nondieting Chinese males who have abstained from eating 12 hours prior to the functional magnetic resonance imaging ( fMRI ) experiment. Results for Glu and CBT indicated no significance ( p > 0.0 5) in both inter and intragroup comparisons due to variable individual responses to treatment. Hunger survey feedback was significant ( p < 0.05) between the acupuncture (ACU) and sham control (min SHAM) groups. Soreness or Deqi, was the only significant ( p < 0.05) intergroup sensation. Connectivity analysis utilized the amygdala (AMY) and hypothalamus (HYP) as regions of interest (ROIs). Common overlapping regions for both ACU and min SHAM using the AMY as the ROI were the putamen, caudate, parahippocampus hippocampus ( HIPP ) insula, cingulate, and Brodmann areas ( BA ) 6, 21, 22, 24, 25, 30, 31, 34, 36, 40, and 47. The common overlapping regions for both

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13 ACU and min SHAM using the HYP as the ROI were the HIPP, putamen, thalamus, and BA 28, 32, 38, 40, and 47. These areas are involved in dopamine, basal metabolic rate, heart rate, cognition and satiety regulation. This project will be of great importance in helping understand how acupuncture can be a safe, inexpensive, and highly effective treatment modality for weight control.

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14 CHAPTER 1 LITERATURE REVIEW Introduction Acupuncture, an ancient eastern therapeutic technique, is emerging as an important modality of complementary medicine in Western countries ( Li et al. 1992; Knight et al. 2001; Ng et al. 200 4). The scope of my acupuncture research is new, exploratory, and is in the early stages of development that specifically addresses human healthrelated issues. A variety of symptoms can be treated by acupuncture clinically (Anonymous NIH, 1998). However, the mechanism of how acupuncture works has not been clearly defined. Acupoint specificity has been under much scientific debate. For example, the visual cortex was activated by laser acupuncture at points BL 67 (Zhi Yin) (Siedentopf et al. 2002), LI 4 (Hegu), ST 36 (Zusanli), BL 60 (Kunlun), and BL 67 (Litscher et al. 2004). Li et al. (2003) induced visual cortex activation at four visionimplicated acupoints (BL 60, BL 65 (Shugu), BL 66 (Zutonggu), and BL 67). Acupuncture at K 3 (Taixi) activated the auditory cortex (Parrish et al. 2005). It was reported that PC 6 (Neiguan) was used to diminish nausea (Knight et al. 2001; Yoo et al., 2004; Bai et al., 2009a ). Ng et al. (2004) showed that stimulation at HN 3 (Yintang), NH 8 (Yingxiang), and ST 36 was use d to treat persistent allergic rhinitis in children. ST 36 and SP 6 (Sanyinjiao) are valid for visceral disorders (Li et al. 1992). Patients with lateral epicondylitis or tennis elbow can be treated at GB 34 (Yanglingquan) and ST 36 (Tsui et al. 2002). O n the other hand, Gareus et al. (2002) did not detect significant bloodoxygen level dependent (BOLD) signal changes in the visual cortex when needling GB 37 (Guangming). Kong et al. (2009) indicated that electroacupuncture stimulation (EAS) induced no sig nificant changes in the occipital cortex at BL 60, GB 37, and an adjacent non acupoint.

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15 Recently, Cho et al. (2006) retracted their early research results, stating there is no acupoint specificity. Most importantly, acupuncture has been used to specificall y treat obesity and weight related issues (for recent review see Cho et al. 2009). The development of imaging techniques, such as positron resonance imaging (PET) and functional magnetic resonance imaging (fMRI), has provided new tools for us to obtain a noninvasive appreciation of the anatomy and physiological function involved during acupuncture in humans and animals (Lewith et al. 2005; Qin et al., 2008; Bai et al., 2009 b ). We currently use fMRI to answer questions relating to acupoint specificity and effectiveness with respect to obesity and the physiology of metabolism. Recent research has not addressed this area in overweight individuals. We believe the physiology and response to acupuncture differs between obese versus overweight individuals. Henc e, u sing the International Affective Picture System (IAPS), we can further evaluate the hunger signal and response to food stimuli using fMRI as well. Obesity and factors leading to being obese or overweight are an enigmatic aspect of scientif ic research on a global scale. It has been shown that obesity ranks second to tobaccorelated deaths at about 300,000 deaths per year (Gold 2003 ). Numerous studies in the past decade have attempted to shed light on not only resolving but also preventing obesity in both animals and humans ( Kumanyika and Obarzanek 2003; Calle and Kaaks 2004 ; Gill et al. 2005 ; Seidell et al. 2005 ; Rubin et al. 2007; Galani and Schneider 2007). Two of the most common genetic and neurophysiological causes of obesity, Prader Willi Syn drome (PWS) and food addiction (FA) respectively, will be discussed in greater detail. PWS was chosen as a well defined genetic model to support the necessity of this project since it may help explain certain neurophysiological

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16 mechanisms that af fect appet ite and FA which eventually lead to obesity, especially in children and young adults. It is ultimately better than an animal model since it can be directly studied and applied to human obesity conditions. Both PWS and FA are driven by similar feeding behaviors, obsessive thoughts and compulsions, and hormones. Currently, the mechanisms of these two disorders are still not well understood. This dissertation present s a general overview of the characteristics, causes, endocrinology, neuroanatomy, hypotheses, treatments and preventative measures of PWS and FA. PWS provides good genetic modeling for the mechanisms of obesity and is a good candidate for acupuncture treatment Background Acupuncture Overview Acupuncture involves stimulating different anatomical points in the body that exert different desired neurophysiological effects on the appropriate organ system based on Traditional Chinese Medicine (TCM) that has been implemented for over 3000 years. There are 14 meridians along which Qi or energy flows. There are well over 360 acupoints along these meridians (Cho et al. 2002). When these acupoints are stimulated, the desirable sensation known as Deqi is felt, indicating the release of opioid peptides. There are various sensations of Deqi reported such as soreness, numbness, tingling, and so on ( Park et al. 2005; MacPherson and As ghar, 2006 ; Hui et al. 2007). Acupuncture was not introduced into the United States until the 1970s. It was not until 1996 when the National Institute of Health (NIH) and National C enter for Complementary and Alternative Medicine (NCCAM) concluded that acupuncture was efficacious for treating conditions such as nausea, arthritis, and others (Anonymous NIH, 1998). T he controversy regarding acupoint specificity continues, hence

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17 neuroim aging studies have been crucial in addressing this issue. PET and fMRI studies started in 1997 (Cho et al. 1997). There have been no direct publications that correlated clinical outcomes in pathological conditions with induced acupuncture changes in the brain. The trial design and data interpretation have been problematic in acupuncture research (Bai et al, 2009b) We devised our control technique b ased on a study by Kleinhenz et al. (1999) who used the Streitber ger needle method. Acupuncture Effects on Ph ysiology The most important aspect of this study will be to mechanistically depict how and why acupuncture ( ACU ) and minimal sham ( min SHAM ) affect glucose (Glu) homeostasis and hypothalamic regulation of core body temperature ( CBT ) and basal metabolic rat e ( BMR ) Sun et al. (2007) studied how ghrelin centrally and peripherally affects Glu homeostasis. Ghrelin causes the release of growth hormonereleasing peptides and neuropeptide Y (NPY) as well as increases appetite (Kojima et al. 1999). It has been shown ACU decreases ghrelin, hence appetite decreases as shown by our unpublished preliminary results. Other key neurocircuits that control glucose metabolism are best reviewed by Rother et al. (2008). It is expected ACU will affect liver gluconeogenesis via insulin its mediators and gastrointestinal afferents centrally carry ing information regarding energy intake. Acupuncture Affects Satiety One focus of this study is to delineate the neurohormonal pathways associated with the hunger response. Pissios and M aratos Flier (2007) proposed that central serotonin affects glucose homeostasis since inhibition of serotonin reuptake decreases appetite. Apparently the arcuate nucleus proopiomelanocortin ( ARC POMC) neurons respond to serotonin as well as leptin and Glu, which are affected by ACU treatment

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18 (Cabioglu et al. 2006). Low leptin and other adipokine levels during fasting stimulate food intake and decrease BMR (for full review see Ahima and Lazar 2008). Leptin controls Glu and lipid metabolism via AMP activat ed protein kinase and stearoyl coenzyme A desaturase 1 in liver and muscle (Ahima et al. 2000), which may be targeted by ACU treatment. Brain regions involved in satiety that may be involved in this study are inferior parietal lobes, dorsolateral prefront al cortex ( DLPFC) and ventromedial prefrontal cortex ( VMPFC ) (James et al. 2004). Our lab has shown that there is a delayed hypothalamic response to reach satiety in o bese individuals, hence it would be interesting to determine if that is the case in overweight individuals. Acupuncture Treats Obesity Obesity research has recognized that there are many factors contributing to this devastating disorder. Much debate has surfaced amongst scientists to develop models which might be useful in understanding why obesity has become a major health problem and epidemic. Numerous studies have shown that manual ACU (Lacey et al. 200 3) as well as EAS (Hsu et al. 2005 a,b ; Cabioglu and Ergene, 2006; Cabioglu et al. 2006; Lee et al. 2006) are effective means for weig h t loss and weight control. One of our initial studies may contribute to help address areas in the brain activated by ACU that may suppress appetite and prevent weight gai n by decreasing food intake. Another unpublished pilot study addressed how overweight individuals respond to visual stimuli before and after ACU or min SHAM treatments. James et al. (2004) showed activation in the insula, prefrontal cortex ( PFC ) amygdala ( AMY ) thalamus, nucleus accumbens ( nACC) and ventral basal ganglia in hungry subject s when viewing food versus nonfood related IAPS pictures, which we hope to see as well in future studies. Data from reward pathways showed that photographs of rich, fattening food induce significantly

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19 greater activation than nonfood object photographs in the left and right striatum (ventral striatum, putamen, caudate), as well as the midbrain (including the ventral tegmental area), left AMY, and left orbitofrontal ( OFC ) (Schur et al. 2009). These pathways integrate aspects of motivation for feeding with hypothalamic inputs on the state of energy balance (Kelley et al. 2005). Causes of General Obesity Joranby et al. (2005) defines obesity as an imbalance between energy input and energy expenditure. Obesity has been one of the most common health disorder s affecting both modern and developing countries. Although it is wellknown that overeating is the leading cause of obesity, the overall etiology is not well explained. Moreover, overeating has been classified as an addiction that comes from a variety of p sychological as well as physiological causes. Another factor is the effect of emotions associated with the reward system of the brain (Joranby et al. 2005). There exist other numerous theories that are beyond the scope of this dissertation as to why this occurs, such as deviant physiological processes involv ed in eating and homeostasis. It is wellrecognized that the hypothalamus (HYP) is the key component for maintaining homeostasis in the body, and is responsive to sig nals that regulate food intake. I f these signals are aberrant in any way, this could lead to delayed feelings of satiety in the individual (Joranby et al. 2005). The intrinsic and extrinsic signaling pathways wil l be discussed in more detail. Sahu and Kalra (1993) best describe which neurop eptides regulate food intake. These will be discussed in further detail Aside from the physiological explanations of obesity, the socioeconomic environment plays a c rucial role in eating behavior. For instance, social and gender

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20 roles, mass westernization and technological advances, and the media have all contributed to the growing population of overweight individuals. Becker et al. (2004) showed that there were three main factors that are responsible for obesity. First, many underdeveloped countries are g oing through westernization and are mi micking western eating habits. Second, the cultural portrayal of obesity as being undesirable and unsuccessful is changing eating habits, but n ot necessarily for the better. Finally, the roles of men and women have caused a shi ft in eating patterns as well. With more women being in the work force and often times seen as breadwinners in the family, this may lead to the entire family eating irregular, non nutritious, fast food meals. A lthough there are various genetic dis orders leading to obesity, Prader Willi syndrome ( PWS ) is one of its key genetic causes that will be discussed below. Prader Willi Syndrome Background: Causes and Symptoms Individuals with the rare disorder, PWS, are identified as being genetically overwei ght since childhood. About 70% of cases are caused by a paternal genetic deletion on chromosome 15 (15q1113), while 25% are from a maternal unipar ental disomy of chromosome 15. The remaining 15% of PWS cases result from certain imprinting defects, which have a 50% risk potential to recur in future offspring (Glenn et al. 1997; Nichols and Knepper 2001, B enarroch et al ., 2007). There is a loss of specific brain genes such as MKRN3, MAGEL2, NDN, SNURF SNRPN, and sno RNA that are misrouted or lost resulting in abnormal cortical development in PWS (Pagliardini et al. 2005). These genetic anomalies can be detected by DNA methylation analysis and in situ hybridization of the alleles (Benarroch et al ., 2007). Human subjects with PWS are characterized as having dolichocephaly, almondshaped eyes, small mouth, hands, and feet, decreased muscle mass and tone

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21 (Cassidy 1997), infantile hypotonia, early onset of obesity due to central dysfunction (around 18 to 36 months of age), hypogonadia, short stature (Goldstone, 2004), and show major disturbances in appetite, sleep, breathing and metabolism regulation, such as delayed satiety, premature return of hunger after eating a meal, seeking and hoarding food and foodrelated objects, and ingesting inanimate items (Miller et al. 2007a) as well as excessive daytime drowsiness, poor ventilation, hypercapnia, and dental caries (N ixon and Brouillette, 2002). Overall, many systems are affected by PWS such as the central nervous system (CNS), gastrointestinal, urogenital, cardi ovascular, respiratory, and dermatologic resulting in numerous medical conditions and disorders (Benarroch et al ., 2007). Anatomically, PWS individuals have speech and language impairments as a result of perisylvian abnormalities including ventriculomegaly sylvian fissure polymicrogyria, and incomplete sylvian fissure/insula closure (Miller et al. 2007a,b,c ) One particular defect, the failure of growth over the insula and underdevelopment of the frontal, temporal, and parietal opercula may be caused by t he lack of paternally expressed genes relating to cortical development. Insular malfunction is noted in PWS individuals since they have poorly functioning pain perception and autonomic control (Goldstone, 2004). This may be explained by incomplete insular closure leading to differences in white matter connectivity between the cortex and the insula (Miller et al. 2007a). Miller et al. (2007a) hypothesized that the aberrant appetite in this condition resulted in irregular reward processing of food stimuli in brain pathways involving the HYP frontal cortex, insula, and limbic/ paralimbic areas. P ost mortem results have shown a decreased number of cells in the paraventricular nucl eus (PVN) (Swabb et al.

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22 1995). This is crucial evidence for explaining why satiet y is difficult to attain, since the PVN is the hunger center of the brain that controls appetite (Kalra and Kalra, 2004 b ). W hat is most interesting about this condition is that PWS hyperphagia is not responsive to pharmaceutical treatment (Holland et al. 1993). Food Addiction Background: Causes and Symptoms Addiction is classically defined as a chronic relapsing problem caused by various fundamental factors that encourage craving for certain substances, such as food, in order to obtain a state of heightene d pleasure, energy, or excitement (Tartar et al., 1998). An example of this would be carbohydrate cravers that have learned to consume high carbohydrate foods to improve their mood caused by a drop in serotonin levels (Wurtman and Wurtman, 1995). A study by Spring et al. (2008) showed convincing evidence of this phenomenon. Most food addiction is the result of loss of control, impulsive and/or compulsive behavior that results from emotional and environmental conditions and a psy chological dependence on food. Eating behaviors are similar to those of other addictions since both affect the levels of dopamine (DA) in the mesolimbic dopami nergic system (Mogenson, 1982). DA D2 receptors have a high prevalence of Taq I A allele (Noble et al. 1994), meaning that t his allele has been linked with low levels of these receptors in obese in dividuals (Noble et al. 1991). T hese patients use food to raise their DA levels, even through positive rei nforcement (Noble et al. 1997). It has been shown that the activity of DA in the brain can be related to abnormal eating behavior (Jimerson et al. 1992). One study showed that the predisposition to food addiction in offspring was caused by feeding rat mothers junk food consisting of fatty, sugary, and salty snacks

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23 during pregnancy and lactation. Rat offspring showed increased weight gain BMI compared to controls, while their mothers displayed bingeing and overeating of junk food (Bayol et al. 2007). Thus, these findings may be applied to pregnant womens diets in order for them to have healthy children with normal appetites and weight. Avena and colleagues (2004; 2005; 2008) found evidence for sugar addiction in rats, since it was a good animal model to describe why certain people crave sweets or other delicious foods, and why it is difficult for them to wean themselves from such an eating behavior. Sugar has been found to be an addictive substance since it releases opioids and DA, which are characteristi c of addiction neurochemicals. T he group classified sugar as an addictive substance because it follows the typical addiction pathway that consists of bingeing, withdrawal, craving, and cross sensitization. The definitions of the components of this system are as follows: bingeing consists of unusually large bouts of intake (Colant uoni et al. 2001); withdrawal is indicated by signs of anxiety and behavioral depression (Colantuoni et al. 2002); craving is measured during sugar abstinence as enhanced by responding to sugar (Avena et al. 2005); and cross sensitization results f rom sugar to drugs of abuse (Avena et al. 2004). B ingeing is also defined as escalation of intake with a high proportion of intake at one time, usually after a period of voluntary abstinence or forced deprivation (Avena et al. 2008). It consists of se nsitization and tolerance, which are necessary for the initiation of any form of addiction (Koob and Le Moal 2005). Withdrawal has been known to be caused by alterations in the opioid sy stem (Colantuoni et al. 2002). It consists of two parts, in which DA decreases and acetylcholine ( A c h ) is released from the nAC C When sugar was analyzed with regards to withdrawal symptoms, it was

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24 capable of producing DA, Ac h, and opioids similar to most narcotic s ubstances (Avena et al. 2008). It is marked by anxiety (F ile et al. 2004) and depression (Avena et al. 2008). Craving usually happens after a prolonged period of abstinence and is better defined by increased efforts to obtain a substance of abuse or its associated cues as a result of dependence and a bstinence (Avena et al. 2008). Cross sensitization is predominantly defined as an increased locomotor response to a different drug or substance (Avena et al. 2008). All of these definitions play a major role in helping define and classify food as an addictive substance in comparison to t he criteria for drug dependence (Haddock and Dill 2000) On the contrary, Haddock and Dill (2000) explained that the addictions model of obesity and eating disorders was flawed and could not be compared to that of drugs. They q uoted various studies (Wilson, 1991; Parham 1995 ; Wilson, 1999) to state that food was not a psychoactive substance. However, the studies used to support their arguments were outdated and the studies by Avena et al. (2004; 2005; 2008) showed that sugar it self can be classified as an addictive substance. Shared Anatomical Areas in the Brain The brain areas involved in satiety include the HYP orbitofrontal cortex (OFC), insula, inferotemporal cortex, nAC C ventromedial prefrontal cortex (vmPFC), limbic and paralimbic regions. T hey are supposed to be involved in reward, arousal, motivation, memorization, and emotional responses to food and eating ( Tataranni and Delparigi 2003). The OFC has been found to link food and rewarding experiences (ODoherty 2004), thus if lesions are found in this region, this may result in hyperphagia in PWS patients (Miller et al. 2006). In most o bese subjects, food is more palatable and enjoyable through the postcentral gyrus of the left and right parietal cortex (Wang et al.

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25 2 002). The nAC C and ventral tegmental area (VTA) are known for reinforcing behavior such as feeding (Gold, 2003). It is a key area in the reward system, which will be described later, since it encourages foodseeking behavior, learning incentive, motivation, satiety, and stimuli processing (Bassareo and Di Chiara, 1999). In animal models, damage to the ventromedial hypothalamus (VMH) leads to hyperphagia and increased appetite, while damage to the lateral hypothalamus (LH A ) causes hypophagia and decreased ap petite (Liu and Gold, 2003). However, the dorsal striatum is most important in the motivation of consuming food. In a study by Volkow et al. (2002), when DA deficient mice were treated with DA in the dorsal striatum, feeding behavior was restored. These animals also chose more palatable food over that which was not. M ice that were not given DA in the nAC C were capable of initiating feeding behavior; those that were treated with DA in the nA C C chose more palatable foods over nonpalatable ones, even though t hey had no motivation to eat enough food to maintain nor mal function and to stay alive. In another study, DA agonists were used to increase the portion size of meals and length of feeding, while long term administration of DA increased body mass and feeding behavior (Schwartz et al. 2000). W hen methylphenidate was administered to human subjects as a DA agonist into the striatum, those that got the placebo did not show any increase in appetite. Thus, the ventral striatum was localized as the site for appet i te control (Wang et al. 2004). These studies are critical to localize the areas of the brain that can be identified and used to treat PWS and FA. Hypot halamic Regulation of Appetite/A fferent Hormonal Signaling The HYP is a ke y component to feeding behavior, thus it is important to look into its regulation in more detail. There have been five individual areas identified in the HYP

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26 that regulate feeding behavior and m etabolism (Kalra et al., 1999). Medial areas of the HYP control food intake and energy homeostasis. These regions obtain important information from referring organs and systems that are involved in nutrient and metabolite consumption and distribution, as well as involvement in hyperphag ia and obesity (Berthoud, 2002). Ghrelin and leptin have been known to target the HYP i n regulating feeding behavior. Leptin activates its receptors so that NPY, orexin endorphin, and alpha melanocytestimulating hormone ( MSH ) can decrease appetite stimu lation (Kalra and Kalra, 2004b). Leptin has been shown to have an impor tant role in appetite control. L eptin can suppress ghrelin expression at the level of the NPY neurons (Bagnasco et al. 200 2a,b). T he roles of leptin and ghrelin feedback on the appetite regulating network ( ARN ) are crucial for energy homeostasis and appetite (Kalra et al., 2003). If there is a drop in leptin levels in the blood, the ARN is s timulated to release orexigenic NPY, agouti related protein ( AgrP ) and gamma aminobutyric acid ( GABA) along with an inhibition of anorexigenic MSH (Kalra and Kalra, 2004b). This can be best summarized by ErlansonAlbertsson (2005). T here has been interesting evidence explaining how leptin regulation is affected by factors predisposing an individual to obesity. If an animal consumes too many energy laden calories and is inactive, this promotes hyperleptinemia and fat accumulation in the body (Kalra et al., 1999). Leptin transport and production in the HYP is limited, and excess leptin in blood circulation is unable to control appetite (Bag nasco e t al., 2002a,b; Kalra and Kalra, 2002). This may be a significant factor to look into regarding overweight individuals

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27 Endocrinology of O besity Defining the endocrinology behind obesity is crucial to understanding FA and overeating behavior. Most childhoo d obesity is the result of genetic defects in leptin and its receptor, POMC pro hormone convertase 1, melanocortin4 receptor (MC4), and ghrelin genes (Farooqi and ORahilly 2000). This is why PWS is the first and foremost genetic model for obesity that demonstrates leptin resistance as one of the primary causes of obesity. In PWS cases leptin levels are increased causing an inability to produce an anorexigenic effect (Proto et al., 2007), while numerous studies have indicated other hormonal and metaboli c disorders that may be associated with hyperphagia including impaired growth hormone (GH) secretion and low insulin production ( Zipf et al., 1983; Goldstone et al. 2001a,b ; Cummings et al. 2002; Goldstone et al., 2002; Goldstone et al. 2004 ). People wi th FA who are obese may possibly have leptin resistance as well that leads to overeating (Liu and Gold, 2003). Hyperphagia is primarily due to continuous stimulation of NPY rec eptors (Kalra and Kalra 1996). An imbalance of NPY signaling at a local level ( ARC and PVN) results in unregulated e ating (Kalra and Kalra, 2004b). The neurotransmitter GABA has also been known to enhance feeding behavior via its receptors or directly in the ARC, causing decreased melanocortin signaling to the PVN, which in turn resu lts in hyp erphagia (Cowley et al. 2001). I t is possible that mutations or disturbances of MSH and other peptides involved in satiety can lead to hyperphagia and obesity (Kalra et al., 1999). The key neurotransmitter of addiction, DA, has site specific action regulating the intake of food; it reinforces the effects of foo d (Salamone et al. 199 7). DA is necessary to begin the meal process (Meguid et al. 2000). It acts upon the pre fornical area, VMH, and ARC to reduce the consumption of food and prevent hyperphagia, which in turn is

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28 affected by leptin, insulin, and other hormones (Baskin et al. 1999). It may be inferred that disruptions in DA production and/or structure may predispose certain individuals to addictive behaviors and obesity. There has been some interesting work done by Solinas and Goldberg (2005) regarding how cannabinoid and opioid interactions affect motivational effects of food reinforcement, and increase appetite and food consumption. D elta 9 tetrahydrocannabinol (THC) and morphine increased the reinforcement effects of food. M u receptors are involved in the effects of THC and cannabinoid1 receptors and those are involv ed in the actions of morphine. M orphine and THC have orexigenic effects that promote appetite and food consumption. The reason for this is that high amounts of endocannabinoids (Gonzalez et al. 1999; Howlett 2 002) and high levels of opioid peptides are located in the HYP (Mansour et al ., 1987). The main concept is that THC and morphine not only responded to food stimuli, but also enhanced palatability of the food (Cooper 2004). A bnormal hypothalamic function accounts for a variety of eating disorders, leading to hyperglycemia, which in turn causes other endocrinological problems (Liu and Gold, 2003). This may be explained by one dietary exampl e where fructose was consumed. Fructose promotes insulin production b ut blocks i ts release (Sato et al. 1996). Insulin is known to inhibit feeding by increasing leptin which in turn leads to w eight gain (Saad et al. 1998). Hence, this would be a good model to explain why FA individuals are overweight and are addicted to high carbohydrate foods contai ning high fructose corn syrup. A great comparison of peptides involved in appetite control and

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29 how high fat and carbohydrate diets affect them is portrayed by ErlansonAlbertsson (2005). Orexigenic and Anorexigenic Pathways I t is necessary to discuss the interactive pathways that r egulate appetite and cravings. The ARN has appetite enhancing and reducing circuits that are located in the ARCPVN axis of the HYP It is affected by signaling from the LH A and VMN (Kalra and Kalra 2 004b ). These particular pathways have their components synthesized in the ARC and are targeted at the parvocellular PVN (pPVN) and magnocellular PVN (mPVN), which may provide insight on the mechanisms of overweight subjects The release of these neurochemi cals is regulated primarily by the VMN and LH A (Kalra et al. 1999). Kalra and colleagues (1999) showed that if there was a disruption between these two sites, then the affected individual would overeat and gain weight, as seen in PWS and FA. This suggests that the VMN is responsible for inhibiting signals to the ARC. C ertain areas in the LH A that express ORX or MCH increase NPY release, thereby stimulating appetite. Thus, if there is nonstop stimulation of NPY receptors, then the satiety signal to the HYP is inhibited resulting in continuous eating (Kalra et al. 1999), which is a typical symptom in overweight individuals What is interesting is that despite this happening, there is no known receptor downregulation f or NPY (Kalra and Kalra 1996). It was shown that during the absence or decrease of food intake, NPY levels increased in the ARC in order to stimulat e appetite (Kalra et al ., 1999). The neurotransmitter GABA has also been known to stimulate appetite by its receptor activ ation or by administeri ng NPY. GABA by itself can decrease melanocortin signaling to the PVN in order to stimulate appetite (Cowley et al. 2001). Another orexigenic peptide, AgrP, enhances eating by antagonizing MC4 which are responsible

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30 for curbing appetite (Kalra et al. 1999). Ghrelin has been shown to increase appetite by increasing NPY signaling (Kalra et al. 2003). LH A neurons express ORX and MCH and ARC neurons coexpress NPY ; A g rP and GABA are the key components of the hypothalamic orexigenic pathway (Kalra and Kalra, 2004b). Manipulation of this pathway can provide novel insight for the treatment of obesity in general. A norexigenic pathways are responsible for controlli ng the inhibition of appetite. The melanocortin pathways crucial component is the ARCPVN axis where POMC MSH while cocaineand amphetamine regulating transcript (CART) act s upon PVN to curb appetite (Kalra and Kalra, 2004 b ). Inhibition of feeding is MSH. D uring the hunger state, POMC gene ex MSH release decreases as well (Kalra and Kalra, 2004b ). Another anorexigenic pathway consists of corticotropin releasing hormone ( CRH) neurons in the PVN, which release CRH due to stress to inhibit NPY induced food intake. Fee ding regulation by anorexigenic neurochemical signals consists of the links between NPY and POMC and between NPY and CRH (Morley, 1987) Food C ues: Internal and External Appetite Triggers Food cues and motivation are crucial aspects of food intake. Most environmental cues result from Pavlovian conditioning that can overrule satiety and enhance food ingestion in the network associated with feeding including the AMY, LH A and mPFC Other motivational cues paired with eating during the hung er state can easily override satiety and promote eating in sated rats, which results from signals via the forebrain and LHA (Petrovich and Gallagher 2007). In the developed and developing world, people are constantly surrounded by food cues to enhance consumption of not ne cessarily healthy foods. There are diverse

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31 examples of how food cues affect a specific group of individuals. This section will focus on how food cues affect those with obesity as compared to normal humans based on the animal model. Certain brain circuits and networks are responsible for cueinduced eating, appetite induction, as well as specific food cravings. Kalra and Kalra (2004a ) provide a thorough overview of pathways regulating appetite and cravings. T he ability of foodrelated cues and a foodassoci ated environment to induce eating in healthy humans can shed light on why PWS individuals overeat and become overweight I n the animal model, the brain regions consisting of the basolateral amygdala ( BLA), mPFC, and LHA act as a network to regulate eating by learned, motivational cues. In neuroimaging studies, the AMY has been shown to be pivotal in its role in cueenhanced eating (Arana et al., 2003); it has been suggested that the AMY is involved in appetite activation and maintenance in humans (Tataranni et al., 2003; Gottfried et al., 2003; Killgore et al., 2003; Kringelbach et al., 2003; Hinton et al., 2004). The OFC is also involved in foodrelated cues (Arana et al., 2003). The mPFC is crucial for eating due to environmental cue pressure (O'Doherty, 2004). The AMY shares tight connections with the HYP (Petrovich et al., 2001). Activations of the AMY and medial orbitofrontal cortical area (mOFC) occur when fooddeprived individuals are shown food items relative to nonfood items, and greater activations are seen when food items are viewed (Arana et al., 2003; Hinton et al., 2004). On the other hand, normal eating signaling results from a response to decreased energy, but it can be triggered from environmental or learned cues, which can alter the motivat ion for food con sumption. Petrovich et al. (2001) presented findings associated

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32 with major connections formed between the BLA and LH A These are responsible for processing learned cues in order to forego the satiety signal and promote eating in sated rats. The BLA shares anatomical connections with the HYP in order to control feeding behavior (Elmquist et al., 1999; DeFalco et al., 2001; Petrovich et al., 2001). A portion of the BLA, which originates in the basolateral nucleus, directly innervates the LHA ( Petrovich et al., 2001). It sends vital projections to the LHA (Petrovich et al., 2001), which forms part of the feeding circuit associated with the initiation of feeding (Elmquist et al., 1999). The BLA LHA system is crucial for allowing learned cues to o verride satiety signals and stimulate eating during satiation. It was shown that the BLA LHA system is specifically associated with controlling eating via learned signals, because it does not regulate baseline eating or the rate at which rats gain weight w hen fed ad libitum This was primarily due to an associative process in which food ingestion was directed by a cue paired previously with food but not an unpaired one (Petrovich and Gallagher, 2007). W hen research animals are presented with food cues, they consume more food despite being sated. Visual cues for foods that have a higher incentive value produce greater activation in the AMY than foods that were recently eaten to satisfy hunger signals (Gottfried et al., 2003). In general, craved or highly palatable foods will activate the AMY regardless of sated state (Hinton et al., 2004). In a recent study by Petrovich et al. (200 7 ), cellu lar activation markers in the vmPFC neurons were activated following exposure to a newly conditioned cue that stimulated eating in sated rats. When neurotoxic lesions were created in the vmPFC, this caused impaired food consumption as a result of conditioned motivational cues.

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33 Thus, the vmPFC has a significant role in appetite influenced by motivational cues. B rain lesions did not affect eating in the pretest baseline sessions or the rate at which rats gained weight when fed freely R ats with selective bilateral neurotoxic lesions in the central nucleus showed enhanced feeding when a conditioned stimulus was applied ( Petrovic h et al., 2007). L esions in the BL A basomedial (BM A ), and lateral (LA) nuclei of the AMY as well as the LHA result in decreased food consumption and nonresponsiveness to appetite stimulation cues (Petrovich et al. 2007). This part of the AMY controls fo od consumption via extrinsic cues ( Petrovich et al., 2001). Given its important role in goal oriented behavior (ODoherty, 2004), the omPFC could play a pivotal role in regulating the impulse to eat in response to highly appetitive cues in PWS and overweig ht subjects. From a neurohormonal perspective, glutamate is believed to be the neurotransmitter responsible for transmitting information between the areas depicted above, although the exact mechanism is still not understood (Swanson and Petrovich, 1998). G lutamatergic mechanisms within the LHA have been shown to promote feeding in sated rats (Duva et al., 2001). It is then plausible that potential feeding mechanisms involve direct glutamatergic connections from the BLA to LHA, although the exact LHA neurons involved in this process remain unidentified. I t may be safe to assume that BLA outputs could influence LHA subsystems required for feeding initiation. G roups of LHA neurons express two recently discovered neuropeptides, MCH and orexin, which are regulated by the hunger satiety state and are linked to initiation of feeding (Elmquist et al., 19 99). Leptin and NPY have an opposite effect on food ingestion. Leptin inhibits feeding while NPY promotes eating (Schwartz et al., 2000). L earned cues can utilize

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34 BLA to activate NPY or inhibit leptin The BLA LHA junction must be intact in order to initiate food cuerelated eating (Kelley, 2004) Hunger caused by food cues is an adaptive mechanism for survival, but at the same time, learned cues can serve as a harmful force to promote overindulgence in food despite satiety. These particular learned cues can overcome specific satiety signals in order to promote continued eating (De Castro, 1997). This may be the situation in PWS and overweight patients. Metabolic factor s and nonhomeostatic signals control motivational eating. Despite no clear definition of food cravings, specifically in animal models (Weingarten and Elston, 1990), cravings for food in humans can be elicited by food cues and are often associated with hedonic overeating ( Jansen, 1998; Fedoroff et al., 2003; Sobik et al., 2005; Tiggemann and Kemps, 2005). R egions of the PFC may also participate in brain networks involved in cueinduced drug cravings. Other regions overlapping the vmPFC are also activated by chocolateand nicotineassociated contextual cues in rats (Schroeder et al., 2001). Additional studies are needed to determine if this rat model parallels the role of the mOFC in human appetite and cravings. T he lateral OFC is not needed for food c onsumption, but the ventral areas within the rat vmPFC could represent a functional counterpart for the mOFC in humans ( Ongr and Price, 2000). DA also plays a critical role in food consumption stimulated by unpredictable cues ( Roitman et al., 2001). DA efflux within the vmPFC resulting from signal induced satiety was correlated with decreased consumption of high caloric sweet and fatty foods; this may be the case in overweight individuals H uman OFC activation decreased in response to an olfactory cue of food eaten to satiety but not to an odor of uneaten food

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35 (ODoherty et al., 2000). This may be a key point as to why food addicted overweight individuals continue to overeat despite satiety. Neophobia, a species specific adaptive response to novel food, suggests a role for the intact vmPFC in regulation of unlearned adaptive feeding responses, as seen in the Petrovich gr oups (2007) study which showed the eff ects of lesions in this region. R ats with neurotoxic lesions of the prelimbic area (PL) were given a choice between familiar lab chow and a novel, preferred food in an unfamiliar, open field environment. Rats with les ions had an increased tendency to consume the novel food more and tended to eat the familiar food less as compared to controls. T he amount consumed of the two foods was similar for rats with lesions than those without lesions. Food neophobia in rats was increased (Petrovich et al., 2007) and decreased with mPFC lesions. Lesions were located in the PL and infralimbic area (ILA) (Petrovich et al., 2007), along with damage to the adjacent ventral PL and mOFC regions. This may suggest different roles of the subregions within the vmPFC in food neophobia. The vmPFC seems to be crucial in controlling eating impulses based on environmental cues. A dysfunctional vmPFC could mechanistically depict feeding behavior in PWS or overweight humans relevant to overeating, a ppetite, cues and cravings. Explicit food associated cues during the hunger state will enhance food consumption despite subsequent satiety. O ther studies link the nAC C and dopaminergic brain systems to motivation and food reward (for reference reviews see Cardinal et al., 2002; Berridge and Robinson, 2003; Phillips et al., 2003; Kelley, 2004; Wise, 2004). Currently, there are no valid studies showing whether DA is necessary for cuepotentiated eating. Thus, individualized subcircuits may be integrated into a vast,

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36 combined system depending on the underlying processes controlling motivation to consume food or to seek out highly desired foods via external cue activation. More research is needed to elucidate the precise mechanisms of food consumption through d ifferent aspects of l earning (Petrovich et al., 2007), despite a lack of a definition of food craving in animal models (Weingarten and Elston, 1990). Even human cravings have been defined with uncertainty especially in overweight individuals. C raving for f ood can be induced by exposure to food cues (Tiggemann and Kemps, 2005) and cue elicited craving is associated with binge eating (for reviews see Jansen, 1998; Sobik et al., 2005). Cue induced eating could be considered binging, since Petrovich et al. (20 07) showed that sated rats consume more food pellets in a short period of time. A recent human study (Fedoroff, 2003) showed that in diet restricted eaters, food cues elicited specific cravings for the cued food, as opposed to a general desire to partake i n non craved food. A s the craving for the desired food increased, the restricted dieters consumed more of the cued food (Fedoroff, 2003). B rain systems and mechanisms that dictate food reward learning were correlated with drug addiction (Cardinal and Ever itt, 2004; Volkow and Wise, 2005). Contextual cues, used to stimulate eating, are also very powerful cues in drug addiction craving and relapse (Crombag and Shaham, 2002). Koob and Le Moal (2005) argued that drug addiction is the dark side of the reward neurocircuitry in the form of impulsive to compulsive behavior much like that displayed in PWS. Wang et al. (2004) conceptually reviewed the similarity between obesity and drug addiction using neuroimaging techniques

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37 The main role of the external feeding environment in food intake depicted by an animal model might be relevant to PWS and overweight human eating. In fact, the environment in which food is consumed has been changing over the past 30 years in the United States. Increasingly disproportional food portions are served and eaten in distinct environments such as restaurants and fast food places (Nielsen et al., 2002). Advertising on television further elicits food cues encouraging even normal weight children and adults to seek out food despite lack of hunger signals. On the other hand, external food cues can be depicted in a much simpler fashion than the internal cues described above. In PWS patients, obsession and preoccupation with food, lack of satiation, and incessant food seeking are typical behav iors as compared to normal obese humans ( Holm et al., 1993; Dimitropoulos et al., 2000; Lindgren et al., 2000; Ogura et al., 2008). PWS adults show preference for sweet or high carbohydrate foods over any other type of food. This is sometimes the case in n ormal obese individuals (Ogura et al., 2008; Singh et al., 2008). PWS patients will often eat the most desirable foods first, such as sweet, high caloric foods, and the least preferred foods last. Oftentimes, this is a ritualistic procedure in which the PW S afflicted individual will gather the food and line it up in order of preference and ingest it sequentially (Singh et al., 2008). Since PWS cases are often highly affected by visual cues, even more so than normal overweight adults, environmental cues are of greater pertinence. For instance, passing by a bakery or restaurant, or even seeing sweet or highly palatable foods on television, will cause an enormous increase in craving and appetite despite satiety in PWS individuals as compared to normal overweight ones. Oftentimes, PWS patients will have tantrums and aberrant behavior after seeing or smelling delicious, inviting food

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38 (Singh et al., 2008), which is highly uncommon in nonPWS people. It would almost appear that in PWS, food cues (visual) have a very high emotional attachment and significance that leads to bingeing episodes (Simmons et al., 2005). The key to these disorders is that anything that alters the satiety and hunger signalin g may involve binging on food. In FA, the act of eating itself brings pleasure but in PWS, eating is pleasureless (Liu and Gold 2003). Overall, much progress has been done in order to fully understand the anatomical, functional, and neurohormonal mechanisms of PWS and FA which would help explain the ultimate causes of obesity in the general population. Food induced hyperphagia uses the hypothalamic neural network and afferent signaling to control appetite, but it still does not define if this mechanism is due to low energy stores. New therapeutic options especially ACU, ca n then be utilized to curb food cravings and help cont rol weight in human individuals. Reward System Hypothesis In drug related addictions, it has been shown that the ventral striatum and midbrain were associated with immediate rewards and the HIPP res ponded to reward consequences. T he globus pallidus, thalamus, and subgenual cingulate were associated with immediate rewards, while the caudate, insula, and vPFC responded to reward consequences (Ell iott et al., 2000). Th e mesolimbic reward system is a common pathway that substances of abuse, such as food and drugs, follow in order to reinforce craving behavior (Tartar et al., 1998). R eward processing is linked to addiction and is processed only if it can promote the addicted individual in pursuit of the addict ion. This then explains why addicts are more prone to seeking rewards, such as food, rather than facing the consequences of the reward behavior (Joranby et al. 2005). The fundamental idea of the reward system lies in the fact that there must be an emotional

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39 state connected with the addiction. The stronger the emotional link, the stronger the addiction. There exist a couple of primary c ircuits for the reward system. The first one involves a reciprocal connection between the prefrontal areas of the brain and the AMY The second is the limbic system that links the AMY with the HYP and septal nuclei. The Papez limbic system also joins the HYP with the HIPP and t halamus (Joranby et al. 2005). T he reward system hypothesis states that appetizing food and addictiv e behaviors compete for reward regions ( such as the nAC C ) in the brain. The act of overeating and obesity can lead to decreasing food reward and addiction (Kleiner et al. 2004). A nticipating and ingesting appetizing food causes an increase in DA levels in the nAC C (He rnandez and Hoebel 1988). Obesity is a reward deficiency syndrome (Blum et al. 1996 a,b ) since DA D2 receptors are mediators of reinforcement and compulsiveness, and o bese subjects were found to have lower levels of these receptors in the striatum (Wang et al. 2001). T he common pathway for addiction involves the mesolimbic frontocortical dopamine (MFD) system, which is a reward pathway that controls eating behavior. Addictive behaviors release DA in the reward pathway causing almost immedia te positive reinforcement (Hodgkins et al. 2003). I ncreased activation in the somatic parietal areas in FA individuals suggests that enhanced activity in these regions involves sensory processing of food, which may make food even more rewarding (Wang et al. 2003). Morris (2001) showed that the state of hunger can be influential on the memory of foodrelated s timuli in fasting individuals. In the study by Joranby et al. (2005), they found that the activity of the brain was regulated depending on the stimul us it received. T he right anterior OFC had a variable response to all stimuli despite hunger, while the right posterior OFC had different responses only with food-

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40 related stimuli during hunger. T he posterior area was associated with general rewards, while the anterior part was associated with abstract and goal oriented rewards. As mentioned earlier, studies done in sugar dependent rats would help defin e the reward hypothesis (Avena et al., 2005; Rada et al., 2005; Avena et al., 2008 ; Bayol et al. 2007). It is wellknown that certain drugs of abuse release more DA in the nAC C; Rada et al. (2005) found that it was the same in rats eating highly palatable food, especially sugar. They found that sugar dependent rats had a delayed Ac h response for satiety, imbib ed more sugar, and r eleased more DA than controls. I t was questionable if sugar had a similar mechanism to opioids on satiation and t he lowering of Ac h in the nAC C It was found that sugar indeed promoted satiety causing the release of Ach (Rada et al., 20 05). Just one event of showing palatable food is enough to increase DA, which has been shown to be correlated to taste recognition (Di Chiara 2002). R ats that were known to consume a large quantity of sugar had a delayed release of Ach resulting in overeating of sugar (Rada et al., 2005). This may explain why FA individuals may be addicted to certain palatable foods that cause a delayed, prolonged increase in Ach levels. Neuroimaging Studies N euroimaging studies, such as done by our lab, have shown that aberrant eating behaviors and obesity have altered the brain chemistry (Liu and Gold, 2003) as well as the anatomy (Miller et al. 2007a,b,c) of af fected PWS and FA individuals. In an fMRI study by Shapira et al. (2005), PWS patients had delayed blood oxy gen level dependent (BOLD) responses in the HYP during rest and after Glu ingestion in the frontal cortex after viewing food pictures. This was probably due to defects in the HYP resulting in abnormal reward processing that led to calorie overloading. It i s well known in fMRI that

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41 the frontal cortex is involved in linking food and other rewarding objects with hedonism (ODoher ty 2004). I t was found that o bese individuals ate more food than controls when food cues were present during a fooddirected reinfor cement task (Johnson, 1974). Wang et al. (2004 ) found that when subjects viewed delicious food, the anterior insula and right OFC, brain regions that are involved in the DA system, were activated. Another interesting study looked at dissociated responses in the AMY and OFC using bottom up and top down approaches (Wright et al. 2008). The bottom up phenomena is stimulus driven and the topdown one is task driven, meaning that emotional evaluation pertaining to obesity is controlled by different neural syst ems as depicted by fMRI. Wright and colleagues (2008) showed that the AMY is associated with bottom up processing and the OFC and vmP FC is linked with the topdown approach. Regarding obesity, this can explain how emotions about food are associated with t he OFC, and how satiety and the reward hypothesis are corr elated with the vmPFC and ACC. T his information is useful in what areas of the brain need to be targeted for future treatment and intervention. H yperphagia is thought to be a contributing factor to increased caloric intake and hence obesity. Interestingly, there have been many unverified animal and human models that have sought to mechanistically identify the sole causes of aberrant appetite control leading to weight gain. PWS is a biological model for hyperphagia and the reward system utilized to explain human obesity using fMRI. Neuroimaging would be the most logical tool in precisely locating the brain regions responsible for controlling appetite and for being the reward centers of food addiction ( Tataranni and DelParigi, 2003). I n past studies, using foodrelated pictures or other visual means to elicit brain

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42 responses has been a standard method of determining valid mechanisms that delineate the path to obesity (Jansen, 1998; Ernst and Epstein, 2002; Killgore et al., 2003; Hawk et al., 2004; Kemps et al., 2004; Simmons et al., 2005; Sobik et al., 2005; Beaver et al 2006; Cornier et al., 2007; Rolls and McCabe 2007; Stoeckel et al., 2008). Hence, the fMRI supported hypothesis that PWS is a naturally occurring human model for food addiction or loss of control of eating or absence of satiety would be crucial for further ACU studies. In the end, what remains is how logical and effective past, present, and future research can aid and treat abnormal ea ting behavior and brain responses to internal and external food cues in individuals afflicted with obesity. Treatments for PWS and FA With respect to the obesity issue, t here are two common types of nonmedicinal methods to decreasing body weight and/or im proving the health condition o f the individual. The first one is the undieting approach which discourages the use of food restriction or dieting due to its ineffectiveness and possible health risks (Foster, 2001). The second type is isolated dieting in whi ch one consumes less of a particular type of food or food group such as seen in the Adkins diet where carbohydrates are almost completely eliminated from the diet. A restriction diet can also be combined with supplements or specific weight reducing herbs or even acupuncture (Foster 2001). Besides altering the endocrinological makeup of overweight individuals via drug therapies, alternative and complementary approaches could play a major role in the intervention and possible prevention of obesity. FA is e asier to prevent than PWS. Initially, a comprehensive medical and psychological evaluation should be done on each patient to determine the root of the problem. With FA, decreasing access to highly palatable and addicting foods is necessary. On the other hand, restriction to all foods

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43 and small inanimate objects for PWS patients is a necessity. Management includes 24 hour or constant supervision, planned physical activity, a strict diet ( divided into structured, portioned meals at set times, and a static, predictable way of life (Benarroch et al., 2007). Encouraging both afflicted groups to exercise or do other enjoyable activities will discourage them from their usual eating behaviors, as well as maintaining a highly controlled eating environment and food regimen with strict, co nsistent and reinforced rules. T reatment of PWS and FA is a group effort that requires major lif estyle changes and dedication. O ne novel treatment for obesity, FA, and PWS is acupuncture. The TCM explanation given for obesity or increased appetite for the affected individuals was described as having excess heat in the gastrointestinal system, a deficiency of the energy known as Qi in the spleen and stomach, or simply a generalized deficiency in Qi. That was one reason w hy we chose our acupoints.One example of a TCM formula for treating obesity consists of the acupoints Neiguan (SP 6), Fenglong (ST40), Liangmen (ST 21), Guanyuan (R 4), Zusanli (ST 36), Tianshu (ST 25) and Quchi (LI 11) (Li, 1999). We used ST36 and SP 9, which were easily accessible in the scanner Another form of acupuncture, auricular acupuncture, has had some good results in promoting weight loss (Stux and Pomeranz, 1987). Some common auricular points for us to consider us ing in future studies are the hunger, stomach, and shenmen points that are indicated to promote satiety and cause sedation/analgesia respectively (Huang et al., 1996). These points are often stimulated using press needles, staples, or beads (Dung, 1986). A cupuncture may be utilized part icularly in FA subjects to improve their mood, alleviate stress, and lessen depression in order to control appetite (Akil et al., 1984).

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44 This can be achieved by the release of various neurochemicals during acupuncture treat ment (Hans and Terenius, 1982) th at could possibly affect our physiological results. In one example, Sun and Xu (1993) utilized traditional ear and body acupuncture methods over a 3 month period. Interestingly, the controls only received the herb Oenothera erythrosepala (evening primrose oil), while the treatment group showed greater weight loss ( P <0.01). Auricular EAS has also been a fascinating method for weight loss, such as in the study by Shafshak (1995). He found that when auricular electroacu puncture was given to overweight women 5 times a week for 3 weeks along with a low calorie diet, the weight loss was significant ( P <0.05). In another randomized study by Steiner et al. (1983), there were 4 experimental groups consisting of real acupuncture, sham acupuncture, eating behavior alter ation only, an d controls. The results concluded that weight loss was best using real auricular and body acupuncture and/or behavior modification as compared to sham acupuncture and controls ( P <0.05). Other recent studies have all shown great success in usi ng alternative modalities for weight loss (Hsu et al. 2005a,b ; Cabi oglu et al. 2006 ; Lee et al. 2006). We may implement such methods in future studies. E xperimental treatments in animals may have some practical application in treatment and prevention of obesity. It has been shown in mice that targeting the DA system with DA agonists promote decreased appetite and food consump tion (Scislowski et at., 1999). There is a possibility such drugs can be marketed for use in human medicine and our studies Anot her suggested experimental treatment is the aid of central leptin gene therapy (Bagnasco et al., 2002 a ). In a study by Kalra and Kalra (2002), an injection of

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45 recombinant adenoassociated virus vector encoding leptin into the HYP of prepubertal and adult r ats resulted in weight gain and suppressed diet induced obesity. The explanation was that it promoted loss of fatty deposits caused by a decrease in NPY and incr ease in MCH and thermogenesis. This is a novel approach that may not be sui table for humans at this point. D isrupting NPYergic signaling at multiple loci without affecting normal hypothalamic function would be ideal, but more research needs to be done in this area (Kalra and Kalra, 2004a). Another experimental method is based on the theory that Ac h inhibits feeding via M1 receptors if a muscarinic agonist, arechol ine, is injected into the nACC. This can be reversed by using an M1 antagonist pirenzapine (Rada and Hoebel., 2000). I t would be interesting to determine if arecholine would be a safe and effective method to prevent hyperphagia in overweight patients. However, there is no safe and effective pharmaceutical agent to specifically treat hyperphagia except possibly fenfluramine, which has been known to have cardiopulmonary side effects (Selikowi tz et al. 1990). Some studies showed that taste aversion was a very useful therapy in which Ach levels were increased while decreasing DA levels (Mark et al. 1995), using D fenfluramine with phentermine to control appetite using a similar mechanism (Rada and Hoe bel, 2000). Others have found that baclofen, a GABA B agonist, is useful in overeating fatty foods (BudaLevin et al. 2005). Current methods of treatment for PWS include supplementation with GH before 18 months of age in order to decrease body fat and increase lean mus cle mass (Carrel et al., 2004). Other treatments utilize naloxone (an opioid antagonist) to block the opioid system and rimonabant (a CB1 receptor antagonist) to block the cannabinoid system;

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46 these systems have been shown to reinforce feeding behavior, and when used together, they act synergistically to treat obesi ty (Berry and Mechoulam, 2002). There are other possible cannabinoid agonists and antagonists or acupoints with those properties that may potentially be used to treat FA and other eating disorders (Solinas and Goldberg 2005). B oth PWS and FA have similar morbidities associated with them such as developing obesity, type 2 diabetes, orthopedic abnormalities, sleep apnea, gallbladder stones, hepatic lipidosis, insulin resistance dysmetabolic syndrome, renal disease, cardiovascular disease, certain cancers, depression, anxiety disorders, sleep disturbances, and a slew of others (Miller et al., 2006). Therefore, PWS is a good genetic model to study these various obesity related di seases and help us with our ACU studies in the overweight population.

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47 CHAPTER 2 AIMS, MATERIALS AND METHODS Aim s To compar e the acute effects on Glu le vels and CBT via fMRI in overw eight Chinese males using ACU versus min SHAM protocols To determine areas of functional connectivity in the brain following ACU and min SHAM acupuncture. To determine how ACU and min SHAM acupuncture affect the hunger state in fooddeprived human subjects. Rationale We hypothesize that Glu levels will decrease and CBT will increase during ACU treatment and decrease post treatment due to increased insulin production as well as other hormonal interplay via vagal stimulation due to central stimulation of the thermoregulat ory center in the HYP affecting BMR. We expect to see activ ation due to acupuncture effects in the HYP (VMH / LHA ), in sula, mPFC and ventral striatal regions nACC, caudate nucleus (CN), putamen, and globus pallidus), brai n stem, PVN, ARC cerebral cortex, su bcortical structures ( AMY, HIPP cerebellum, and thalamus) inferior parietal lobes, DLPFC/VMPFC insula, and ventral basal ganglia. The VMH, LHA and ventral striatal regions are known to regulate glycometabolism (Morton et al. 2006), while the HYP and brainstem are CNS centers that affect gastric function (Wu et al. 1999). The cerebral cortex, PVN, ARC, and subcortical brain structures are involved in cognitive function (Fuster 2002). The inferior parietal lobes, DLPFC, VMPFC, insula and ventral basal ganglia are involved in satiety (see review by Ahima and Ant wi 2008). We expect to see hunger decrease after ACU acupuncture only (rather than min SHAM)

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48 due to neurophysiological effects of acupuncture which are used to suppress hypothalamic activity. Subjects The study was performed on 19 right handed volunteer C hinese males aged 2145 years (10 for ACU treatment and 9 for min SHAM) who had no history of major neurologic and psychiatric disease. All subjects were acupuncture nave and gave written informed consent as approved by West China University of Medical Sc ience. All subjects were in accordance with the Declaration of Helsinki All patients were free to withdraw at any time from the study without obligation. Table 2 1 shows inclusion and exclusion criteria used to recruit subjects. Table 21 Inclusion and exclusion criteria used to select subjects for this experiment. Inclusion criteria Exclusion criteria Right handed adult (age 21 45 years) Chinese males Left handed non Chinese males or females (age <21 and >45 years) BMI >18 and <30 Normal weight/BMI No n smoker Smokers Non dieter (regular diet and exercise program in past 3 months) On a weight loss program in past 3 months ) Not on prescription or non prescription medication especially anti depressants and appetite suppressants Taking antidepressants or a ppetite suppressing drugs (i.e. loperamide) Never experienced acupuncture (acupuncture nave) Had major acupuncture treatment in the past, especially recently Healthy (no neurological and endocrine problems) Neurological or endocrine d isorders Not claustrophobic Claustrophobic 12 hour fast prior to experiment Ate within 12 hours of the experiment

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49 Experimental Design Subjects were recruited and prescreened based on a standard questionnaire. Subjects were randomly assigned by a computer program to groups A and B (the acupuncturist is the only nonblinded individual in the research group). Group A received standard ACU treatment. Group B was treated with min SHAM Session I Experiment I (which included group A) and II (which included group B) consist ed of the following protocols. Physiological Measurements Height (cm) and weight (kg) were measured for each subject in order to calculate the body mass index ( BMI ) A brief chest and heart auscultation was performed for each patient. Prior to scan, initial CBT was measured sublingually with an Omron electronic thermometer (MC 142L). Initial Glu was taken from the left index finger and was measured via OneTouch Ultra Blood Glucose Monitoring System (Lifescan; Johnson & Johnson Company). Instrument used glucose oxide (>0.8 IU) and buffer (0.05 mg). Range was 20600 mg/dL or 1.133.3 mmol/L. Accuracy was a slope of 0.986, y intercept = 5.5 mg/dL, and CC = 0.984. Preci sion was 1.63.2% for blood and 2.44.4% for the control. Blood pressure was measured via Omron electronic blood pressure monitor (HEM 645). Sensitivity was 4 mmHg ( 5% accuracy ) with a range of 0299 mmHg. The hunger survey was then conducted asking the patient to evaluate his hunger on a standard scale from 0 (no hunger) to 10 (starvation). After the 21 min scan, CBT, Glu (from right index finger), and a hunger survey were conducted. The patient was asked to evaluate the Deqi sensations he felt during the treatment. A standard scale (0 being no sensation and 10 being most intense sensation felt) was used to evaluate Deqi sensations listed. When the anatomic scan

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50 and post scan were done, the final CBT, Glu (left middle finger), and a hunger survey were c onducted. Subjects were asked if they think they received real or sham acupuncture. Treatment Methods After a 5 min prescan, the certified acupuncturist set up for either ACU or min SHAM procedure, depending on random patient assignment. Scan began when needles were in at time 0 min. For ACU, 4 acupoints were used bilaterally, ST 36 and SP 9. ST 36 is 3 cun below ST 35 (Dubi), which is in the depression lateral to the patellar ligament on the lower border of the patella when the knee is flexed, and 1 cun la teral to the anterior crest of the tibia. When the knee is flexed, SP 9 is located along the posterior border of the upper tibia. For min SHAM, the SHAM acupoints were located 2 cun lateral and dorsal to ST 36 and 2 cun medial to SP 9 on the same plane bil aterally. Acupuncturist used paramagnetic (0.18 mm x 40mm) needles for both ACU and min SHAM. For ACU, after a 1 min pause, the acupuncturist inserted needles vertically to a depth of 23 cm and rotated needles in a tonifying and reducing technique cloc kwise and counterclockwise at a rate of 60 times per minute or 2 Hz in an alternating bilateral diagonal manner at 30 sec intervals for a total of 2 min. The subject was allowed to raise his right index finger if the Deqi sensations were painful. The lower legs were covered to mask the treatment choice. Scan continued for 21 min. For min SHAM, after a 1 min pause, the acupuncturist inserted needles to a depth of 23 cm and immediately removed them, but pretended to rotate the needles as described for the A CU procedure. The lower legs were covered to mask the treatment choice. After treatments, a 7 min anatomic scan and a 9 min post scan were conducted.

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51 fMRI Parameters The functional MRI experiment was performed using a 3.0 Tesla Signa (GE) MR with a standard head coil. The images covered the entire brain and were parallel to the AC PC line. Functional images were acquired with a singleshot gradient recalled echo planar imaging (EPI) sequence (TR/TE: 2000ms/30ms, field of view (FOV): 240mm240mm, matrix siz e: 6464, flip angle: 90 in plane resolution: 3.75mm3.75mm, slice thickness: 5mm thick with no gaps, 43 sagittal slices). A set of T1 weighted highresolution structural images was collected (TR/TE: 5.7ms/2.2 ms, FOV: 256mm256 mm, matrix size: 256256, flip angle: 7, in plane resolution: 1mm x 1mm, slice thickness: 1mm with no gaps). Preprocessing of Data and Analysis The first 5 time points were discarded to avoid the instability of the initial MRI signal. The fMRI runs were intensity scaled to yield a whole brain mode value of 1,000. Data sets were preprocessed using SPM5 (www.fil.ion.ucl.ac.uk/spm). Images were realigned to the first image. If translation and rotation was > 1 mm in any direction or > 1 degree, the subject was excluded. The images were then normalized to a Montreal Neurological Institute (MNI) template and resampled to 3mm x 3mm x 3mm. Resting data used a bandpass filter of 0.01 0.1 Hz. Finally, the images were smoothed using 6mm x 6mm x 6mm. For regions of interest (ROIs), the HYP and AMY were chosen initially. The first 0.5 min of data were omitted and 8.5 min of the ACU data were extracted. The HYP (7 grey voxels) and AMY mask s (2 grey voxels) w ere selected using WFU (Wake Forest University) Atlas software. The data was smoothed with a 12mm full width at half maximum (FWHM) Gaussian kernel for the discrete cosine transform ( DCT ) analysis.

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52 The DCT analysis was followed by steps depicted in Liu et al. (2009) The discrete cosine bias set contained 60 regressors spanning the frequency of 0 0.1Hz. Statistical parametrical maps were constructed by computing F contrasts, which compared the effect of signal fluctuations in the range of 0.01 0.1Hz. Statistical parametrical maps were created under the threshold P < 0.005 (corrected for multi ple comparisons) at the first level. The final overlapping mask was created by multiplying the binary values of the individual mask in each group. Finally, the conjunction analysis of the two group masks was applied to detect inter group similarities of sp atial patterns, which was adopted as the ROI for the functional connectivity analysis. T he ROI was applied for further functional connectivity analysis. First, the data were processed with a bandpass filter of 0.01 0.1 Hz. The data sets were then spatially smoothed with 6 mm FWHM Gaussian kernel. Second, linear regressions were used to remove several spurious variances along with their temporal derivatives: head motion parameters, signals from a region centered in the white matter, and a region centered in the cerebrospinal fluid. Third, correlation maps were created by computing the correlation coefficients between the BOLD time course from the seed region, and the BOLD time course from all of the other brain voxels. Finally, correlation coefficients were c onverted to an approximately normal distribution using Fishers z transformation. At the secondlevel analysis, a two sample t test was applied to evaluate the baseline scan of the two acupoint groups before ACU or min SHAM Finally, the test for differenc es of brain networks between the two groups was evaluated using a two sample t test. All contrasts had a threshold at P < 0.005 (uncorrected) and a cluster size > 3 voxels. Seed voxel correlation analysis ( SCA ) was then applied for connectivity results. SCA i s based on extracting the BOLD

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53 time course from a predefined seed region and calculating cross correlation coefficients with all other voxels in the brain (Liu et al., 2009).

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54 CHAPTER 3 RESULTS Parametric Test Statistics There was an outlier present for s ubject 067 (CBT 1 = 35.34) which was substituted with the average of the remaining values (36.31) and that value was used in the following calculations. Using Repeated Measures ANOVA, there was no significant within group difference ( p >0.05) found in ACU and min SHAM acupuncture groups for CBT and GLU. There were no significant group differences ( p >0.05) seen in the intergroup analysis using the same test. These results point out that despite visual trends seen in CBT and Glu in the two different groups, no significance was found (see F igures 3 1 and 3 2 ). There were individual differences that are of interest. In CBT, all ACU patients had a higher CBT during acupuncture which was lower at the completion of the treatment except for patients 028 (CBT values : 36.25, 36.21, 36.43) who had a BMI of 19.76, and 045 (CBT values: 36.59, 36.54, 36.46) whose BMI was 26.89. On the other hand, individual min SHAM patients differed in CBT outcomes. In general, there was slight increase or no change from before, during or after treatment. Patient 020 showed a major decrease in CBT (CBT values: 37.12, 36.67, 36.71). Patients 029 (CBT values: 36.61, 36.62, 36.56), 046 (CBT values: 36.87, 36.89, 36.91), and 055 (CBT values: 36.93, 36.94, 36.77) had a slight increase followed by a decrease post treatment. Patients 066 (CBT values: 36.42, 37.34, 37.23) and 087 (CBT values: 36.82, 36.96, 36.98) had major continuous increases in CBT after treatment (Figure 3 1) In most ACU individuals, G lu decreased during treatment and post tr eatment except in patient 018 (G lu values: 4.3, 4.3, 4.2) who remained relatively the same. In

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55 patients 045 (G lu values: 5.5, 5.8, 6.7) and 089 (G lu values: 4.2, 4.3, 4.9) there was a major increase during and after treatment. Patient 054 (G lu values: 5.3, 4.7, 5.6) had a decrease during treatment followed by an increase in G lu Patient 067 (G lu values: 4.8, 5.2, 4.8) showed an increase only during treatment. On the other hand, min SHAM patients showed a decrease in G lu during treatment except patient 020 ( G lu values: 4.6, 4.7, 4.7) who had a slight increase during treatment. Patients 055 (G lu values: 4.9, 4.6, 6) and 068 (G lu values: 5.5, 4.8, 6.2) showed major G lu increases at the end of treatment (Figure 3 2) Nonparametric Test Statistics The original c ategorical Deqi data was best analyzed using a MannWhitney rank sum test based on the analysis done by Park et al. (2005). The results indicated that out of the 12 different sensations, only soreness, numbness, and fullness were significant ( p <0.05, 1 tai led). The remaining sensations were insignificant ( p >0.05, 1 tailed) when compared between ACU and min SHAM acupuncture Deqi. A two sample Kolmogorov Smirnov rank sum test was conducted to determine if there were intergroup differences between the ACU and min SHAM group Deqi sensations. Only soreness was found to be significantly different ( p <0.05, 2 tailed) between the two groups (see F igure 33 ). For individual Deqi data in the ACU group, there were no reportings of warmth, tingling, itching, aching, pres sure or heaviness sensations. For soreness, the highest score was 8.5 and the lowest was 0. For numbness, the highest score was 7 and the lowest was 0. For fullness, the highest was 8 and the lowest was 0. Only one patient felt coolness (score of 2). For s harp pain, the highest score was 7 and the lowest was 0. Only one

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56 patient felt dull pain (score 10). For other sensations reported, one patient felt pain for 1 min after needle insertion. On the other hand, in the SHAM group, there were no reported sensati ons of warmth, dull pain, tingling, itching, aching, and pressure. For soreness, the highest score was 3 and the lowest was 0. For numbness, the highest score was 3 and the lowest was 0. For fullness, the highest score was 4 and the lowest was 0. Only one patient reported coolness (score of 4). For sharp pain, the highest score was 4 and the lowest was 0. For heaviness, the highest score was 2 and the lowest was 0. For other sensations felt, patients 026, 047, 053, and 055 were sore for 1 sec after needle i nsertion and felt that the needle insertion was painful (Figure 3 3) For the ordinal repeated measures hunger data, we used the Kruskal Wallis rank sum test to determine that there was no significant withingroup difference between hunger 1, 2, and 3, an d no interaction between time and group ( p >0.05). However, the intergroup comparison showed a significant difference between ACU and min SHAM hungers ( p <0.05) (see F igure 3 4 ). For individual hunger data in the ACU group, most patients had a slight increas e or no change during treatment except for patients 017 (HUNGER values: 4, 6, 7), 027 (HUNGER values: 3.5, 9, 9), 028 (HUNGER values: 5.5, 7, 7.5), and 067 (HUNGER values: 3, 6, 7) who showed dramatic increases in hunger values during and after treatment. Patient 018 (HUNGER values: 7, 5, 6) had a steady decrease during and after treatment. On the other hand, in the SHAM hunger group, most patients showed a steady increase or no change in hunger values except patient 020 (HUNGER values: 5, 3, 1) who had a s teady decrease in hunger values (Figu re 3 4)

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57 Physiological Data Figure 3 1. Measurement of corrected average core body temperature (CBT) in degrees Celsius shown before (PreAcu), during (Acu) and after (Post Acu) acupuncture (ACU) or minimal sham (mi n SHAM) treatments in overweight adult Chinese males (n = 10 for ACU and n = 9 for min SHAM). There was no significant difference between groups as shown by the student t test ( p >0.05) despite a visual increase in CBT during both treatments. Standard error s bars are shown for each time point

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58 Figure 3 2. Measurement of uncorrected average blood glucose (Glu) shown before (Pre Acu), during (Acu) and after (Post Acu) real acupuncture (ACU) or minimal sham (min SHAM) treatment in overweight adult Chinese males (n=10 for ACU and n = 9 for min SHAM). There was no significant difference between or within the two groups as shown by a student t test ( p >0.05) despite a visual decrease in blood Glu during treatments. Standard errors bars are shown for each time poi nt

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59 Figure 33 Averaged major Deqi sensations (soreness, numbness, fullness, coolness, warmth, sharp pain, dull pain, and heaviness) comparison between real acupuncture (ACU) and minimal sham (min SHAM) treatments in overweight adult Chinese males (n= 10 in ACU and n = 9 min SHAM). Significant intragroup differences were only found between soreness, numbness, and fullness ( p Whitney Rank sum test). Soreness was significant in an inter group comparison ( p ; 1 tailed; Kolmogorov Smirnov Rank Sum test ). Standard errors bars are shown for each sensation.

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60 Figure 3 4. Averaged hunger sensation comparison between real acupuncture (ACU) and minimal sham (min SHAM) treatments in overweight adult Chinese males (n=10 in ACU and n = 9 i n min SHAM). Significant inter group differences were found before (Hunger 1), during (Hunger 2), and after (Hunger 3) treatment ( p Wallis Rank Sum test). There was no interaction between time and ACU or min SHAM groups. Standard errors bars are shown for each time point Neuroimaging Data Using Talaira ch coordinates and SPM5, the AMY and HYP were chos en as ROIs for the functional connectivity results (see methods section and Figures 3 5 and 3 6). SCA results showed overlapping areas from functional connectivity analysis listed in

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61 Tables 3 1 to 3 4 For the AMY connectivity analysis during ACU the major over lapping regions are listed in Table 31. The key regions were the uncus putamen, caudate, paraHIPP, HIPP, lateral/ medial globus pallidus, insula, cingulate, substantia nigra and BA 2, 3, 4, 5, 6, 9, 21, 22, 24, 25, 28, 29, 30, 31, 34, 35, 36, 37, 38, 40, 44, 45, and 47 For the AMY connectivity analysis during min SHAM, the major overlapping regions are listed in Table 32 The most important areas were the caudate, cingulate, insula, putamen, HIPP, para HIPP, AC C and BA 6, 7, 10, 11 21, 22, 24, 25, 30, 31, 32, 34, 35, 36, 40, 41, 42, 43, and 47. For the HYP connectivity analysis during ACU, the major overlapping regions are listed in Table 33 The main areas were the HIPP paraHIPP, AMY, putamen, thalamus, ACC, and BA 2, 6, 8, 9, 10, 24, 28, 32, 34, 36, 38, 40, 44, 45, 46, and 47. For the HYP connectivity analysis during min SHAM, the major overlapping regions are listed in Table 34 The key areas were the HIPP substantia nigra, insula, caudate, putamen, thalamus, and BA 17, 21, 28, 32, 35, 38, 39, 4 0, and 47. Comparisons of overlapping regions between the ROIs during ACU and min SHAM produced interesting results (see Table 3 5 ) C ommon overlapping regions for both ACU and min SHAM using the AMY as the ROI were the putamen, caudate, paraHIPP, HIPP, insula, cingulate, and BA 6, 21, 22, 24, 25, 30, 31, 34, 36, 40, and 47. The common overlapping regions for both ACU and min SHAM using the HYP as the ROI were the HIPP, putamen, thalamus, and BA 28, 32, 38, 40, and 47.

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62 Figure 3 5 Blood oxygen level dependent (BOLD) significant brain regions from an amygdalarelated functional connectivity analysis comparing real acupuncture (ACU) versus minimal sham (SHAM)treated individuals. Epoch of treatment lasted for 9 min. Results from the conjunction analysis were based on the discrete cosine transform ( DCT ) group results of the two acupoints. The overlapping areas are the putamen, insula, parahippocampus, hippocampus, hypothalamus, and cerebellum. T value scales located on bottom of picture ( p <0.05)

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63 Figure 3 6 Blood ox ygen level dependent (BOLD) significant brain regions from a hypothalamus related functional connectivity analysis comparing real acupuncture (ACU) versus minimal sham (SHAM) treated individuals. Epoch of treatment lasted 9 min. Results from t he conjunction analysis were based on the discrete cosine transform ( DCT ) group results of the two acupoints. The overlapping areas are the putamen, insula, parahippocampus, hippocampus, amygdala, anterior cingulate cortex (ACC), thalamus, and pons. T valu e scales located on bottom of picture ( p <0.05)

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64 Table 3 1 Overlapping areas during acupuncture treatment from functional connectivity analysis using the amygdala as the region of interest Talairach X Talairach Y Talairach Z Direction Region Area t Value 27 10 20 Left cerebrum Parahippocampal gyrus Hippocampus 6.35 27 4 20 Right cerebrum Uncus Amygdala 8.13 24 4 20 Left cerebrum Uncus Amygdala 6.84 48 4 20 Left cerebrum Middle temporal gyrus Brodmann area 21 4.51 24 27 16 Left cerebrum Parahippocampal gyru s Brodmann area 35 5.25 33 15 17 Right cerebrum Parahippocampal gyrus Hippocampus 7.39 30 2 18 Left cerebrum Parahippocampal gyrus Brodmann area 34 5.74 27 8 18 Right cerebrum Inferior fr ontal gyrus Brodmann area 34 5.02 6 30 33 14 Right cerebrum Parahippocampal gyrus Brodmann area 47 4.12 27 33 14 Right cerebrum Parahippocampal gyrus Brodmann area 36 3.50 21 30 14 Left cerebrum Parahippocampal gyrus Brodmann area 36 6.90 24 15 14 Left cerebrum Parahippoc ampal gyrus Brodmann area 28 3.51 30 2 15 Left cerebrum Parahippocampal gyrus Brodmann area 34 5.90 30 5 15 Right cerebrum Parahippocampal gyrus Brodmann area 38 5.24 27 8 16 Right cerebrum Inferior frontal gyrus Brodmann area 47 4.53 30 8 16 Left cerebrum Inferior frontal gyrus Brodmann area 47 5.55 27 36 11 Right cerebrum Parahippocampal gyrus Brodmann area 37 4.43 9 18 9 Left brainstem Substania n igra 3.88 36 11 11 Right cerebrum Inferior frontal gyrus Substania n igra 4.23 2 1 32 6 Left cerebrum Parahippocampal gyrus Brodmann area 30 4.04 9 21 7 Left brainstem Brodmann area 28 3.57 12 18 7 Right brainstem Substania n igra 4.31

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65 Table 3 1 continued Talairach X Talairach Y Talairach Z Direction Region Are a t Value 18 11 8 Right cerebrum Lentiform nucleus Putamen 6.20 3 11 8 Left cerebrum Anterior cingulate Putamen 4.33 18 11 8 Left cerebrum Lentiform nucleus Brodmann area 25 3.73 21 12 4 Right cerebrum Lentiform nucleus Lateral g lobus p allidus 4 .79 24 12 4 Left cerebrum Lentiform nucleus Medial g lobus p allidus 4.43 27 9 5 Left cerebrum Lentiform nucleus Lateral g lobus p allidus 4.80 24 3 5 Left cereb rum Lentiform nucleus Brodmann area 22 5.02 18 12 2 Left cerebrum Lentiform nucleus C audate h ead 4.20 9 43 8 Left cerebrum Posterior cingulate Brodmann area 29 3.80 42 2 8 Right cerebrum Insula Brodmann area 44 4.5 3 59 4 14 Right cerebrum Precentral gyrus Brodmann area 6 4.63 59 7 16 Left cerebrum Inferior frontal gyrus Brodmann ar ea 6 4.59 42 34 21 Left cerebrum Insula Brodmann area 44 5.65 45 2 22 Left cerebrum Inferior frontal gyrus Brodmann area 45 4.92 45 4 27 Left cerebrum Inferior Frontal gyrus Brodmann area 9 5.66 45 7 34 Left cerebrum Precentral gyrus Brodmann ar ea 40 3.96 9 16 39 Right cerebrum Cingulate gyrus Brodmann area 3 3.56 12 7 39 Left cerebrum Cingulate gy rus Brodmann area 24 6.08 9 9 45 Left cerebrum cingulate gyrus Brodmann area 31 7.47 9 4 44 Right cerebrum Cingulate gyrus Brodmann area 31 5.58 3 21 48 Left cerebrum Medial frontal gyrus Brodmann area 5 3.69 24 26 57 Left cerebrum Precentral gyrus Brodmann area 3 3.79 27 32 60 Left cerebrum Postcentral gyrus Brodmann area 2 7.78 21 26 59 Left cerebrum Postcentral gyrus Brod mann area 3 6.02 33 23 59 Left cerebrum Precentral gyrus Brodmann area 4 4.33 9 12 59 Right cerebrum Medial frontal gyrus Brodmann area 4 3.79

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66 Table 32. Overlapping areas during minimal sham acupuncture treatment from functional connectivity anal ysis using the amygdala as the region of interest. Talairach X Talairach Y Talairach Z Direction Region Area t Value 24 27 16 Left cerebrum Parahippocampal gyrus Brodmann area 35 3.71 30 15 14 Left cerebrum Parahippocampal gyrus Hippocampus 6.31 30 12 15 Right cerebrum Parahippocampal gyrus Hippocampus 3.78 21 2 15 Right cerebrum Parahippocampal gyrus Brodmann area 34 6.23 24 30 11 Left cerebrum Parahippocampal gyrus Brodmann area 36 4.52 24 2 13 Left cerebrum Parahippocampal gyrus Brodmann area 34 7.82 36 17 13 Right cerebrum Inferior frontal gyrus Brodmann area 47 4.54 18 32 8 Right cerebrum Parahippocampal gyrus Brodmann area 35 4.00 3 49 13 Left cerebrum Medial frontal gyrus Brodmann area 11 3.65 6 52 13 Right cerebrum Medial frontal gyrus Brodmann area 11 4.24 15 35 6 Right cerebrum Parahippocampal gyrus Brodmann area 30 4.93 18 32 6 Left cerebrum Parahippocampal gyrus Brodmann area 30 4.41 6 11 8 Right cerebrum Anterior cingulate Brodmann area 25 4.27 24 8 5 Left cerebrum Lentiform nucleus Putamen 5.99 21 11 6 Right cerebrum Lentiform nucleus Putamen 4.92 9 11 6 Left cerebrum Caudate Caudate h ead 4.04 12 14 6 Right cerebrum Caudate Caudate h ead 4.73 6 46 7 Right cerebrum Medial frontal gyrus Brodmann area 10 3.50 59 6 2 Left cerebrum Superior temporal gyrus Brodmann area 21 3.75 56 11 3 Right cerebrum Superior temporal gyrus Brodmann area 22 4.64 12 46 5 Right cerebrum Anterior cingulate Brodmann area 32 3.70 59 0 8 Left cerebrum Precentral gyrus Brodmann area 6 3.69 56 17 12 Left cerebrum Transverse temporal gyrus Brodmann area 41 3.86 62 17 12 Left cerebrum Transverse tem poral gyrus Brodmann area 42 3.91

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67 Table 3 2 continued Talairach X Talairach Y Talairach Z Direction Region Area t Value 3 12 45 Right cerebrum Paracentral lobule Brodmann area 31 5.53 3 12 45 Left cerebrum Paracentral lobule Brodmann area 31 3.75 3 8 44 Left cerebrum Medial frontal gyrus Brodmann area 32 4.05 3 9 56 Right cerebrum Medial frontal gyrus Brodmann area 6 4.81 62 52 16 Left cerebrum Superior temporal gyrus Brodmann area 22 4.25 48 17 17 Left cerebrum Postcentral gyrus Brodmann area 43 3.60 48 22 20 Left cerebrum Insula 4.39 6 22 24 Left cerebrum Anteri or cingulate Brodmann area 24 3.58 6 19 29 Left cerebrum Cingulate gyrus Brodmann area 32 4.16 62 30 37 Left cerebrum Inferior parietal lobule Brodmann area 40 3.89 3 12 42 Right cerebrum Cingulate gyrus Brodmann area 24 4.41 3 12 42 Left cerebrum Cingulate gyrus Brodmann area 24 3.75 3 11 41 Right cerebrum Cingulate gyrus Brodmann area 32 6.35 15 49 66 Right cerebrum Postcentral gyrus Brodmann area 7 3.62

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68 Table 33 Overlapping areas during acupuncture treatment from functional connect ivity analysis using the hypothalamus as the region of interest. Talairach X Talairach Y Talairach Z Direction Region Area t Value 24 30 14 Left cerebrum Parahippocampal gyrus Brodmann area 36 3.68 24 7 15 Left cerebrum Parahippocampal gyrus Amyg dala 3.91 30 1 15 Right cerebrum Parahippocampal gyrus Amygdala 3.65 33 5 15 Right cerebrum Superior temporal gyrus Brodmann area 38 3.89 27 5 15 Right cerebrum Parahippocampal gyrus Brodmann area 34 3.75 27 5 15 Left cerebrum Parahippocampal gyrus Brodmann area 34 3.72 30 14 16 Right cerebrum Inferior f rontal gyrus Brodmann area 47 3.79 24 30 11 Left cerebrum Parahippocampal gyrus Brodmann area 36 3.56 18 15 12 Right cerebrum Parahippocampal gyrus Brodmann area 28 4.01 30 27 9 Right cerebrum Sub gyral Hippocampus 4.84 30 27 9 Left cerebrum Sub gyral Hippocampus 3.90 24 21 9 Left cerebrum Parahippocampal gyrus Brodmann area 28 3.99 45 37 9 Right cerebrum Middle frontal gyrus Brodmann area 47 4.33 42 37 9 Right cerebrum Middle frontal gyrus Brodmann area 47 3.61 27 3 5 Right cerebrum Lentiform nucleus Putamen 3.63 24 6 5 Left cerebrum Lentiform nucleus Putamen 6.46 15 23 1 Right cerebrum Thalamus Ventral p osterior m edial nucleus 3.51 50 17 1 Right cerebrum Inferior frontal gyrus Brodmann area 47 3.69 45 47 2 Right cere brum Inferior frontal gyrus Brodmann area 10 3.76 6 47 2 Right cerebrum Anterior cingulate Brodmann area 32 3.65 6 49 2 Right cerebrum Anterior cingulate Brodmann area 10 3.65 1 2 9 3 Right cerebrum Thalamus Ventral l ateral nucleus 5.33 3 32 1 Left cerebrum Anterior cingulate Brodmann area 24 4.37 21 23 7 Right cerebrum Thalamus Ventral p osterior l ateral nucleus 5.09 6 47 3 Right cerebrum Anterior cingulate Brodmann area 32 4.82 6 50 3 Right cerebrum Medial frontal gyrus Brodmann area 10 4.48 59 15 8 Right cerebrum Precentral gyrus Brodmann area 44 4.81 56 18 7 Left cerebrum Inferior frontal gyrus Brodmann area 45 4.06

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69 Table 3 3 continued Talairach X Tala irach Y Talairach Z Direction Region Area t Value 9 33 15 Right cerebrum Anterior cingulate Brodmann area 24 3.68 45 39 15 Right cerebrum Middle frontal gyrus Brodmann area 46 3.83 6 39 15 Left cerebrum Anterior cingulate Brodmann area 32 4.02 3 33 18 Right cerebrum Anterior cingulate Brodmann area 32 3.67 48 42 17 Right cerebrum Middle frontal gyrus Brodmann area 46 5.70 3 22 24 Left cerebrum Anterior cingulate Brodmann area 24 3.54 36 22 32 Right cerebrum Middle frontal gyrus Brodmann area 9 4.91 3 34 34 Left cerebrum Medial frontal gyrus Brodmann area 6 3.92 24 22 38 Left cerebrum Middle frontal gyrus Brodmann area 8 3.53 6 28 37 Right cerebrum Medial frontal gyrus Brodmann area 6 4.16 56 27 43 Right ce rebrum Postcentral gyrus B rodmann area 2 4.2 2 3 22 40 Right cerebrum Cingulate gyr us Brodmann area 32 3.51 6 28 40 Right cerebrum Medial frontal gyrus Brodmann area 8 5.86 56 36 46 Left cerebrum Inferior parietal Lobule Brodmann area 40 3.85 3 37 42 Left cerebrum Medial frontal gyrus Brodmann area 8 8.19

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70 Table 34. Overlapping areas during minimal sham acupuncture treatment from functional connectivity analysis using the hypothalamus as the region of interest. Talairach X Talairach Y Talairach Z Direction Region Area t Value 36 5 18 Left cerebrum Superior temporal gyrus Brodmann area 38 3.97 24 11 16 Left cerebrum Inferior frontal gyrus Brodmann area 47 4.70 18 18 12 Left cerebrum Parahippocampal gyrus Brodmann area 35 7.59 33 18 12 Left cerebrum Parahi ppocampal gyrus Hippocampus 3.81 18 15 12 Left cerebrum Parahippocampal gyrus Brodmann area 28 5.58 12 21 9 Right brainstem Substania n igra 6.13 9 18 9 Left brainstem Substania n igra 4.68 56 15 9 Left cerebrum Middle temporal gyrus B rodmann area 21 3.49 42 6 7 Left cerebrum Sub Gyral Brodmann area 21 3.95 42 12 4 Right cerebrum Insula 3.66 6 93 2 Right cerebrum Lingual gyrus Brodmann area 17 4.47 9 9 3 Right cerebrum Caudate Caudate h ead 3.94 18 9 3 L eft cerebrum L entiform nucl eus Putamen 4.49 6 15 1 L eft cerebrum Caudate Caudate h ead 4.54 12 8 6 Left cerebrum Thalamus Ventral l ateral nucleus 5.92 12 6 6 Left cerebrum Thalamus Ventral a nterior nucleus 3.57 45 75 12 Left cerebrum Middle temporal gyrus Brodmann area 39 4.56 12 20 12 Left cerebrum Thalamus Medial d orsal nucleus 4.50 15 20 12 Left cerebrum Thalamus Lateral p osterior nucleus 3.89 12 17 15 Left cerebrum Thalamus Lateral d orsal nucleus 5.27 6 16 30 Left cerebrum Cingulate gyrus Brodmann area 32 3.85 53 53 41 Right cerebrum Inferior parietal l obule Brodmann area 40 4.30

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71 Table 35. Common overlapping areas during acupuncture and minimal sham acupuncture treatments from functional connectivity analysis using the amygdala and hypothalamus as the regions of interest. Amygdala region of interest Hypothalamus region of interest Caudate Thalamus Putamen Putamen Parahippocampus Hippocampus Hippocampus Brodmann areas 28, 32, 38, 40, 47 Insula Cingulate Brodmann areas 6, 21, 22, 24, 25, 30, 31, 34, 35, 36, 40, 47

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72 CHAPTER 4 DISCUSSION AND CONCLUSION Discussion As mentioned in the literature review, acupoint specificity has been studied and questioned. We specifically chose ST 36 and SP 9 based on their functions in TCM and acupuncturist recommendation and approval. However, it was noted by another experienced acupuncturist that for our specific hypotheses and goals, other acupoints could have been chosen instead. For glucose metabolism and thermoregulation, acupoints LI 4, LIV 3, GV 3, GV 4, LI 10, and ST 36 bilaterally were recommended. For HYP activation and hunger suppression, acupoints GB 34, LIV 3, LI 4, SP 6, and SP 9 bilaterally were suggested. Most importantly, ST 36 and SP 9 are crucial points in obesity and weight l oss acupuncture studi es as summarized by Cho et al. ( 2009) Choosing appropriate acupuncture controls is difficult in this field of study, hence the choice of our control method was unique with noteworthy results. Kleinhenz and peers (1999) used the Strei tberger needle method, which mimics needle penetration in real acupuncture but does not fully penetrate the skin layers. We combined this method with the standard sham placebo acupuncture protocol. Surprisingly, all subjects believed they received real acu puncture, thus this proves we used an effective control method Our neurophysiological results were rather unexpected. Most of our results were insignificant although visually, it could be perceived that Glu decreased during ACU and min SHAM, and CBT incr eased during ACU and min SHAM. Some significance was found in certain Deqi sensations and hunger ( p < 0.05; Kruskal Wallis Rank Sum test and MannWhitney test respectively). Based on previous studies, fasting plasma levels

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73 in obese subjects show ed that lev els of ghrelin, adiponectin, CCK, and NPY decrease d, while leptin levels increase d (Cabioglu and Ergene, 2006; Pissios and Maratos Flier, 2007). Unlike the findings in obese individuals, we are led to believe that the specificity of the acupoints chosen ca used a release in ghrelin during ACU to stimulate appetite, hence increasing hunger in our overweight population. This is different compared to other studies that show ACU suppresses appetite by increasing serotonin levels (Wenhe and Yucun, 1981) and promotes satiety in the HYP (Shiraishi et al ., 1995). The VMH, LHA and ventral striatal regions are known to regulate glycometabolism (Morton et al. 2006), while the HYP and brainstem are CNS centers that affect gastric function (Wu et al., 1999). ST 36 is supp osed to promote satiety, regulate intestinal motility, cause sedation (Cabioglu and Ergene, 2006), and increase excitability of the satiety center in the VMH (Zhao et al ., 2000). Acupuncture at this point and SP 9 caused a significant increase in hunger in our subjects, which would be useful to treat anorexic patients or individuals receiving chemotherapy. Zusanli has also been reported to increase motility in individuals with hypoactive intestines and vice versa (Li et al ., 1992). Stimulation of ST 36 also increases the amplitude and frequency of gastric peristalsis in normal individuals, shortening the gastric emptying time and delaying the contractions (Li et al ., 1992). Therefore, it would have been useful to auscultate the subjects intestines and stomach before, during, and after ACU or min SHAM procedures to verify these findings. Studies have shown that mere electrical stimulation of the VMH caused increased Glu uptake via the sympathetic nervous system in skeletal muscle without increasing plasma insulin concentration (Minokoshi et al., 1994; Lang et al., 1995). In

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74 our study, ACU stimulation could be correlated to the electric a l stimuli by causing Glu to decrease during treatment due to ACU having a central effect on the overall physiology The ARC a nd VMH may even be activated centrally to uptake Glu (Morton et al., 2006) via the effects of ACU. If we used EAS or prolonged manual ACU treatment in our experiment, we would probably have significant physiological results It is also necessary to look at deactivations and activations in specific brain areas during rest, stimulation, and post stimulation in order to verify this assumption in future studies. ACU treatment in our study had no statistically significant effect on Glu, hence it may be a time ef fect only; as hunger increases, Glu decreases then increases. A possible interaction may be occurring, or this difference is probably contributed to hormonal interplay and acupuncture stimulation of the CNS. The AMY and HYP were chosen as ROIs for the simple reason that the various regions of the HYP are involved in appetite control and thermoregulation. The AMY is often linked with the HYP (Joranby et al., 2005). As shown in F igures 3 5 and 3 6, there was a significant difference in the spatial patterns of the distinct brain regions between the two treatment groups Functional connectivity describes the temporal synchrony or correlation of the BOLD fMRI signal from two or more anatomically separated brain regions (Friston et al., 1993). Therefore, the spati al and temporal patterns of brain responses would be modulated by the sustained effects of ACU versus min SHAM. We derived the functional connectivity networks from the temporal pattern during the states during and after stimulation associated with the ROI s and the overlapping regions. This is along the lines of our hypotheses that the mPFC, cerebral cortex, cerebellum, DLPFC, and VMPFC would be activated due to the acute ACU effects affecting

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75 primarily satiety and some cognitive functions. With respect to the connectivity analyses, primary somatosensory, motor function, visual stimulation, language, limbic system (pain) and cognitive function centers were involved in both ACU and min SHAM. This was expected since the sensation of the needle and the surroun ding environment stimulated all subjects brain areas such as noted in BA 2 47. One important observation was that with activations in the AMY, ACC, and BA 9, 20, and 36, the subjects were thinking about food, Deqi sensations and /or hunger. Therefore, ther e was a direct correlation between behavioral data and the functional connectivity results. Conclusion Based on connectivity results with the AMY and HYP, it can be assumed that the mode of action for ACU and min SHAM is mediated by the limbic system spec ifically the neurotransmitter DA DA is known to increase heart rate and blood pressure ( Benes, 2001), hence it would affect CBT in our subjects DA also has a role in pain processing (Flores et al., 2004), which would explain Deqi or sensations felt during ACU stimulation. This conclusion is based on ACU activation of the insula (responsible for homeostasis), ACC, HYP, putamen, globus pallidus, substantia nigra, and HIPP The ACC is of great importance in this study since it is involved in blood pressure r egulation and heart rate, but it also shares direct connection with the AMY, HYP, nACC, and insula (Bush et al., 2000). As for the physiological data, it can be inferred that the reason for the variability amongs treatment groups was due to the fact that A CU is tailored to the unique physiology of each individual despite having a homogeneous experimental population.

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76 Study Limitations Limitations in most acupuncture studies regard obtaining individuals that meet the inclusion criteria and adequate sample siz e. A large group size is needed to do a power analysis in multiple sessions to capture the activation and deactivation patterns evoked by acupuncture stimulation at particular acupoints. Confounding factors include Deqi mixed with pain, artifactual activat ion, appropriate controls, patient anxiety and anticipation of pain or discomfort from acupunc ture treatment. Hui et al. (2007) described this in their study regarding the influence of patient sensations on fMRI BOLD signal changes. I t was difficult to obt ain an ideal overweight population in our study as well In the S ichuan Province ( China ), the BMI of overweight individuals was much lower than in other areas or countries. It could be due to the diet (hot, spicy food) and lifestyle (genetic hypertension). As discussed in another one of our studies, individual differences in response to acupuncture should be taken into account as seen by the variable results in our study population. More subjects are needed to verify our pilot study results as well as impl ementing a different experimental design.

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77 LIST OF REFERENCES Ahima RS, Antwi DA. Brain regulation of appetite and satiety. Endocrinol and Metabol Clinics 2008;4:37. Ahima RS, Lazar MA. Adipokines and the peripheral and neural control of energy balance. Mol Endocrinol 2008;22(5):102331. Ahima RS, Saper CB, Flier JS, Elmquist JK. Leptin regulation of neuroendocrine systems. Front Neuroendocrinol 2000;21:263307. Akil H, Watson SJ, Young E, Lewis ME, Khachaturian H, Walker JM. Endogenous opioids: biology and function. Annu Rev Neurosci 1984; 7:223 5. Anonymous, NIH. Consensus Conference. Acupuncture. JAMA 1998;280:151824. Arana FS, Parkinson JA, Hinton E Dissociable contributions of the human amygdala and orbitofrontal cortex to incentive mot ivation and goal selection. J Neurosci 2003;23(29):96328. Avena NM, Carrillo CA, Need ham L, Leibowitz SF, Hoebel BG. Sugar dependent rats show enhanced intake of unsweetened ethanol. Alcohol 2004; 34:203 9. Avena NM, Long KA, Hoebel BG. Sugar dependent r ats show enhanced responding for sugar after abstinence: evidence of a sugar deprivation effect. Physiol Behav 2005;84(3):35962. Avena NM, Rada P, Hoebel BG. Evidence for sugar addiction: behavioral and neurochemical effects of intermittent, excessive su gar intake. Neurosci Biobehav Rev 2008;32(1):2039. Bagnasco M, Dube MG, Kalra PS, Kalra SP. Evidence for the existence of distinct central appetite and energy expenditure pathways and stimulation of ghrelin as revealed by hypothalamic sit e specific lept i n gene therapy. Endocrinol 2002a ; 143:440921. Bagnasco M, Kalra PS, Kalra SP. Ghrelin and leptin pulse dis charge in fed and fasted rats. Endocrinol 2002b ; 143:7269. Bai L, Yan H, Li L, Qin W, Chen P, Liu P, Gong Q, Liu Y, Tian J. Neural specificity of ac upuncture stimulation at pericardium 6: evidence from an fMRI study. J Magn Reson Imag 2009 a ; In Press. Bai L Qin W, Tian J Liu P Li L Chen P Dai J, Craggs JG von Deneen KM Liu Y Time varied characteristics of acupuncture effects in fMR I studi es. H uman Brain Mapping 2009b; 3 0 ( 11):344560.

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78 Baskin DG Figlewicz Latteman D. Insulin and leptin: dual adiposity signals to the brain for the regulation of food intake and body weight. Brain Res 1999; 848:11423. Bassareo V Di Chiara G. Modulation of feeding induced activation of mesolimbic dopamine transmission by appetitive stimuli and its r elation to motivational state. E urop J Neurosci 1999; 11:438997. Bayol S A, Farrington SJ, Stickland NC. A maternal junk food diet in pregnancy and lactation promotes a n exacerbated taste for junk food and a greater propensity for obesity in rat offspring. Brit J Nutr 2007 ; 98(4):84351. Beaver JD, Lawrence AD, van Ditzhuijzen J Individual differences in reward drive predict neural responses to images of food. J Neurosci 2006;26(19):51606. Becker AE, Keel P, AndersonFye EP, Thomas JJ. Genes and/or jeans?: genetic and socio cultural contributions to risk for eating disorders. J Addict Dis 2004; 23(3):81103. Benarroch F, Hirsch HJ, Gens til L, Landau YE, Gross Tsur V. Prader Willi syndrome: medical prevent ion and behavioral challenges. Child Adolesc Psychiatric Clin N Am 2007; 16(3): 695 708. Benes, FM. Carlsson and the discovery of dopamine. Trends Pharm Sci 2001;22(1):467. Berridge KC, Robinson TE. Parsing reward. T rends Neurosci 2003;26(9):50713. Berry EM, Mechoulam R. Tetrahydrocannabinol and endocannabinoids in feeding and appetite. Pharmacol Ther 2002; 95:18590. Berthoud H R. Multiple neural systems controllin g food intake and body weight. Neurosci Biobehavior Rev 2002; 26:393 428. Blum K, Cull JG, Braverman ER, Comings DE. Rewar d deficiency syndrome. Am Scientist 1996a ; 84:132 45. Blu m K, Sheridan PJ, Wood RC The D2 dopamine receptor gene as a determinant of reward deficiency syndrome. J R Soc Med 1996b ;89(7) :396 400. BudaL evin A, Wojnicki FH, Corwin RL. Baclofen reduces fat intake under bingetype conditions. Physio Behav 2005; 86(12):17684 Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex Trends Cogn Sci 2000; 4 (6 ):215 22.

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79 Cabioglu MT, Ergene N, Tan U. The treatment of obesity by ac upuncture. Intern J Neurosci 2006;116:16575. Cabioglu MT, Ergene N. Changes in serum leptin and beta endorphin levels with weight loss by electroacupuncture and diet restriction in ob esity treatment. Am J Chin Med 2006;34(1):111. Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer 2004; 4:57991. Cardinal RN, Everitt BJ. Neural and psychological mechanisms underlying appetitive learning: links to drug addiction. Curr Opin Neurobiol 2004;14(2):15662. Cardinal RN, Parkinson JA, Hall J Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci Biobehav Rev 2002;26(3):32152. Carre l AL Moerchen V, Myers SE. Growth hormone improves mobility and body composition in infants and toddl ers with Prader Willi syndrome. J Pediatr 2004; 145:7449. Cass idy SB. Prader Willi syndrome. J Med Genet 1997; 34:917 23. Cho ZH, Chung SC, Jones JP, Par k JB, Park HJ, Lee HJ, Wong EK, Min BI. New findings of the correlation between acupoints and corresponding brain cortices using functi onal MRI. National Acad Sci 1997;26703. Cho ZH, Chung SC, Lee HJ, Wong EK, Min BI. Retraction. New findings of the correlation between acupoints and corresponding brain cortices using functional MRI. Proc Natl Acad Sci USA 2006;103:10527. Cho ZH, Lee JS, Thabane L, Lee J. Acupuncture for obesity: a systematic review and meta analysis. Intl J Obesity 2009;33:18396. Cho ZH, Oleson TD, Alimi D, Niemtzow RC. Acupuncture: the search for biologic evidence with functional magnetic resonance imaging and positron emission tomography techniques. J Altern Complement Med 2002;8(4):399401. Colantuoni C, Rada P, McCarthy J, Patten C, Av ena NM, Chadeayne A, Hoebel BG. Evidence that intermittent, excessive sugar intake causes endogenous opioid dependence. Obesity Research 2002; 10:47888. Colantuoni C, Schwenker J, McCarthy J, Rada P, Ladenheim B, Cadet JL, Sc hwartz GJ, Moran TH, Hoeb el BG. Excessive sugar intake alters binding to dopamine and muopioid receptors in the brain. Neuroreport 2001; 12:354952.

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80 Cornier MA, V on Kaenel SS, Bessesen DH Effects of overfeeding on the neuronal response to visual food cues. Am J Clin Nutr 2007;86(4):96571. Cowley MA, Smart JL, Rubinstein M, Cerdan MG, Diano S, Horvath TL, Cone RD, Low MJ. Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus. Nature 2001; 411:4804. Crombag HS, Shaham Y. Renewal of drug seeki ng by contextual cues after prolonged extinction in rats. Behav Neurosci 2002;116(1):16973. Cummings DE, Clement K, Purnell JQ. Elevated ghrelin le vels in Prader Willi syndrome. Nat Med 2002; 8:643 4. De Castro JM. How can energy balance be achieved by f ree living human subjects? Proc Nutr Soc 1997;56(1A):114. DeFa lco J, Tomishima M, Liu H Virus assisted mapping of neural inputs to a feeding center in the hypothalamus. Science 2001;291(5513):260813. Di Chiara G. Nucleus accumbens shell and core dopam ine: differential role in behavior and addiction. Beha v Brain Res 2002; 137:75 114. Dimitropo ulos A, Feurer ID, Roof E Appetitive behavior, compulsivity, and neurochemistry in Prader Willi syndrome. Ment Retard Dev Disabil Res Rev 2000;6(2): 125 30. Dung HC. Role of the vagus nerve in weight reduction through auricular acupuncture. Am J Acupuncture 1986; 14:249 54. Duva MA, Tomkins EM, Moranda LM, Kaplan R, Sukhaseum A, Jimenez A, Stanley BG. Reverse microdialysis of N methyl D aspartic acid into the later al hypothalamus of rats: effects on feeding and other behaviors. Brain Res 2001;921(12):12232. E lliott R, Friston KJ, Dolan RJ. Dissociable neural resp onses in human reward systems. J Neurosci 2000; 20:615965. Elmquist JK, Elias CF, Saper CB. From lesi ons to leptin: hypothalamic control of food intake and body weight. Neuron 1999;22(2):22132. ErlansonAlbertsson C. How palatable food disrupts appetite regulation. Basic Clin Pharm Toxicol 2005; 97:6173. Ernst MM, Epstein LH. Habituation of responding for food in humans. Appetite 2002;38(3):22434.

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81 Farooqi IS, ORahilly S. Recent advances in the genetics of severe childhood obesity. Arch Dis Child 2000;83:314. Fedoroff I, Polivy J, Herman CP. The specificity of restrained versus unrestrained eaters' responses to food cues: general desire to eat, or craving for the cued food? Appetite 2003;41(1):713. File SE, Lippa AS, Beer B, Lippa MT. Unit 8.4 Animal tests of anxiety. In: Crawley JN et al. (eds), Current Protocols in Neurosc ience, John Wiley & Sons, Inc.: Indianapolis 2004. Flores JA, El Banoua F, GalnRodrguez B, Fernandez Espejo E. Opiate anti nociception is attenuated following lesion of large dopamine neurons of the periaqueductal grey: critical role for D1 (not D2) dopamine receptors.Pain 2 004;110(12):20514. Foster GD. Non dieting approaches. In: Brownell KD, Fairburn CG (eds) Eating disorders and obesity: a comprehensive handbook. Gulford: NY 2001. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal component analysis of large (PET) data sets. J Cereb Blood Metab 1993;13:514. Fuster JM. Frontal lobe and cog nitive development. J Neurocytol 2002;31:37385. Galani C, Schneider H. Prevention and treatment of obesity with lifestyle interventi ons: review and metaanalysis. IntJ Public Health 2007; 52(6):34859. Gareus IK, Lacour M, Schulte AC, Hennig J. Is there a BOLD response of the visual cortex on stimulation of the visionrelated acupoint GB 37? J Mag Res Imag 2002;15:22732. Gill T, King L, Caterson I. Obesity prevention: necessary and possible. A structured approach for effective planning. Proc Nutr Soc 2005; 64:255 61. Glenn CG, Driscoll DJ, Thomas PY. Genomic imprinting potential function and mechanisms revealed by the Prader Willi and Angelman sy ndromes. Mol Hum Reprod 1997; 3:321 32. Gold, MS. Introduction. Psych Annals 2003; 33(2):5 21. Goldstone A P, Brynes AE, Thomas EL. Resting metabolic rate, plasma leptin concentrations, leptin receptor expression, and adipose tissue measured by wholebody m agnetic resonance imaging in women with Prader Willi syndrome. Am J Clin Nutr 2002; 75:468 75.

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82 Goldstone AP, Patterson M, Kalingag N. Fasting and post prandial hyper ghrelin emia in Prader Willi syndrome is partially explained by hypoinsulinemia, and is not due to peptide YY 336 deficiency or seen in hypothalamic obesity due to craniopharyngioma. J Clin Endocrinol Metab 2001a ; 90:2681 90. Goldstone AP, Thomas EL, Brynes AE. Visceral adipose tissue and metabolic complications of obesity are reduced in Prader Willi syndrome female adults: evidence for novel influences on body fat distribution. J Clin Endocrinol Metab 2001b ; 86:4330 8. Goldstone AP. Prader Willi syndrome: advances in genetics, pathophysiology and treatment. Trends Endocrinol Metab 2004;15(1):1220. Gonzalez S, Manzanares J, Berrendero F, Wenger T, Corchero J, Bisogno T. Identification of endocannabinoids and cannabinoid CB(1) receptor mRNA in the pitu itary gland. Neuroendocrinol 1999; 70:137 45. Gottfried JA, O'Doherty J, Dolan RJ. Encoding predi ctive reward value in human amygdala and orbitofrontal cortex. Science 2003;301(5636):11047. Haddock CK, Dill PL. The effects of food on mood and behavior: implications for the addictions model of obesity and eating disorders. Food as a Drug The Haworth Press, Inc. 2000; 1747. Hans J S, Terenius L. Neurochemical basis of acupuncture analgesia. Ann Rev Pharmacol Toxicol 1982; 22:193 220. Hawk LW, Baschnagel JS, Ashare RL Craving and startle modification during in vivo exposure to food cues. Appetite 20 04;43(3):28594. Hernandez L, Hoebel BG. Feeding and hypothalamic stimulation increase dopamine turnover in the accumbens. Physiol Behav 1988;44(45):599606. Hinton EC, Parkinson JA, Holland AJ Neural contributions to the motivational control of appeti te in humans. Eur J Neurosci 2004;20(5) :141118. Holland AJ, Treasure J, Caskeran P. Measurement of excessive appetite and metabolic cha nges in Prader Willi syndrome. Int J Obes 1993; 17:527 32. Holm VA, Cassidy SB, Butler MG Prader Willi syndrome: consensus diagnostic criteria. Pediatrics 1993;91(2):398402. Howlett AC. The cannabinoid receptors. Prostaglandins Other Lipid Mediat 2002; 68 69:61931.

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83 Hsu CH, Hwang K C, Chao C L, Chang H H, Chou P. Electroacupuncture in overweight women: a randomized, co ntrolled pilot study. Journal of Womens Health 2005a ; 14(5):43440. Hsu CH, Hwang K C, Chao C L, Lin J G, Kao S T, Chou P. Effects of electroacupuncture in reducing weight and waist circumference in overweight women: a randomized crossover trial. Intl J of Obesity 2005b ;29:137984. Huang MH, Yang RC, Hu SH. Preliminary results of triple ther apy for obesity. Int J Obes Relat Metab Disord 1996; 20:830 36. Hui KK Nixon EE Vangel MG Liu J Marina O Napadow V Hodge SM Rosen BR Makris N Kennedy DN. Ch aracterization of the "deqi" response in acupuncture. BMC Complement Altern Med 2007;7:33. James GA, Gold MS, Liu Y. Interaction of satiety and reward response to food stimulation. J Addict Dis 2004;23(3):2337. Jansen A. A learning model of binge eating: cue reactivity and cue exposure. Behav Res Ther 1998 ;36(3):25772. Jimerson DC, Lesem MD, Kaye WH. Low serotonin and dopamine metabolite concentrations in cerebrospinal fluid from bulimic patients with frequent binge episodes. Arch Gen Psychiatry 1992; 49:1328. Johnson WG. Effect of cue prominence and subject weight on hu man food directed performance. J Personality Soc Psych 1974; 29:843 8. Joranby L, Pineda KF, Gold MS. Addiction to food and brain reward systems. Sex Addict Compulsivity 2005,12:20117 Kalra SP, Dube MG, Pu S Interacting appetiteregulating pathways in the hypothalamic regulation of body weight. Endocr Rev 1999;20(1):68100. Kalra SP, Kalra PS. NPY and cohorts in regulating appetite, obesity and metabolic syndrome: benef icial effect s of gene therapy. Neuropeptides 2004a; 38:20111. Kalra SP, Kalra PS. Nutritional infertility: t he role of the interconnected hypothalamic neuropep tide Y galaninopioid network. Front Neuroendocrinol 1996; 17:371401. Kalra SP, Kalra PS. Obesity and metab olic syndrome: long term benefits o f central leptin gene therapy. Prous, JR Ed. Drugs of Today. Barcelona, Spain: Pr ous Science 2002; 38:74557. Kalra SP, Kalra PS. Overlapping and interactive pathways regulating appetite and craving. J Addict Dis 2004 b ;23 (3):5 21.

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84 Kalra SP. Bag nasco M, Otukonyong EE. Rhythmic, reciproca l ghrelin and leptin signaling: new insight in the development of obesity. Regul Pept 2003; 111:111. Kelley AE, Baldo BA, Pratt WE, Will MJ. Corticostriatal hypothalamic circuitry and food motivation: integration of energy, action and reward. Physiol Behav 2005;86:773 95. Kelley AE. Memory and addiction: shared neural circuitry and molecular mechanisms. Neuron 2004;44(1):16179. Kemp s E, Tiggemann M, Woods D Reduction of food cravings thr ough concurrent visuospatial processing. Int J Eat Disord 2004;36: 3140. Killgo re WD, Young AD, Femia LA Cortical and limbic activation during viewing of highversus low calorie foods. Neuroimage 2003;19(4):138194. Kleiner KD, Gold MS, Frost Pineda K Lenz B runsman B, Perri MG, Jacobs WS. Bo dy mass index and alcohol use. J Addict Dis 2004; 23(3):10518. Kleinhenz J, Streitberger K, Windeler J. Randomised clinical trial comparing the effects of acupuncture and a newly designed placebo needle i n rotator cuff tendonitis. Pain 1999;83:235 41. Knight B, Mudge C, Openshaw S, White A, Hart A. Effect of acupuncture on nausea of pregnancy: a rando mized, controlled trial. Acog J 2001;97:1848. Kojima M, Hosoda H, Date Y, Nakazato M, Matsuo H, Kangawa K. Ghreli n is a growth hormonereleasing acylat ed peptide from stomach. Nature 1999; 402:65660. Kong J, Kaptchuk TJ, Webb JM, Kong JT, Sasaki Y, Polich GR, Vangel MG, Kwong K, Rosen B, Gollub RL. Functional neuroanatomical investigation of visionrelated acupuncture point specificity a multisession fMRI study. Hum Brain Mapp 2009; 30(1):3846. Koob GF, Le Moal M. Plasticity of reward neurocircuitry and the 'dark side' of drug addiction. Nat Neurosci 2005;8(11):14424. Kringelbach ML, O'Doherty J, Rolls ET Activation of the human orbitofrontal cortex to a liquid food stimulus is correlated with its subjective pleasantness. Cereb Cortex 2003;13(10):106471. Kumanyika SK, Obarzanek E. Pathways to obesity prevention: report of a national institutes of health workshop. Obes Res 2003; 11:126374. Lacey JM, Tershakovec AM, Foster GD. Acupunctur e for the treatment of obesity: a review of the evidence. Int J Obes 2003; 27:41927.

PAGE 85

85 Lang CH, Ajmal M, Baillie AGS. Neural control of glucose uptake by skeletal muscle after centra l administration of NMDA. Am J Physiol 1995;268: R492 7. Lee MS, Kim JH, Lim H J, Shin B C. Effects of abdominal electroacupuncture on parameters related to obesity in overweight women: a pilot study. Complement Therap in Clin Pract 2006;12:97100. Lewit h GT, White PJ, Pariente J. Investigating acupuncture using brain imaging techniques: the current state of play. Evid Based Complement Altern Med 2005;2(3):3159. Li G, Cheung RTF, Ma QY, Yang ES. Visual cortical activations on fMRI upon stimulation of t he visionim plicated acupoints. NeuroReport 2003;14:669. Li J. Clinical experience in ac upuncture treatment of obesity. J Tradit Chin Med 1999; 19:4851. Li Y, Tougas G, Chiverton SG, Hunt RH. The effect of acupuncture on gastrointestinal function and dis orders. Am J Gastroenterol 1992;87:137281. Lindgren AC, Barkeling B, Hagg A Eating behavior in Prader Willi syndrome, normal weight, and overweight control groups. J Pediatr 2000;137(1):505. Litscher G, Rachbauer D, Ropele S, Wang L, Schikora D, Fazek as F, Ebner F. Acupuncture using laser needles modulates brain function: first evidence from functional transcranial Doppler sonography and functional magnetic resonance imaging. Laser s in Medical Science 2004;19(1):611. Liu Y, Gold MS. Human functional magnetic resonance imaging of eating and satiety in eating disorders and obesity. Psych Annals 2003; 33(2):12732. Liu P Qin W Zhang Y Tian J Bai L Zhou G Liu J Chen P Dai J von Deneen KM Liu Y Combining spatial and temporal information to explore functionguide action of acupuncture using fMRI. J Magn Reson Imaging 2009;30(1):416. MacPherson H and Asghar A. Acupuncture needle sensations associated with De Qi: a classification based on experts r atings. J Altern Complement Med 2006;12(7):6637. Mansour A, Khachaturian H, Lewis ME, Akil H, Watson SJ. Autoradiographic differentiation of mu, delta, and kappa opioid receptors in t he rat forebrain and midbrain. J Neurosci 1987; 7:244564. Mark GP, W einberg JB, Rada PV, Hoebel BG. Extracellular acetylcholine is increased in the nucleus accumbens following the presentatio n of an aversively conditioned taste stimulus. Brain Research 1995; 688:184 88.

PAGE 86

86 Meguid MM Fetissov SO, Varma M. Hypothalamic dopamine and serotonin in the regulation of food intake. Nutrition 2000; 16:84357. Miller J, Kranzler J, Liu Y, Schmalfuss I, Ther iaque DW, Shuster JJ, Hatfield A, Mueller OT, Goldstone AP, Sa hoo T, Beaudet AL, Driscoll DJ. Neurocognitive findings in Prader Willi syndrome a nd early onset morbid obesity. J Pediatr 2006; 149:1928. Miller JL, Couch JA, Leonard CM, Schwenk K, Towler SD, Shuster J, Goldston e AP, He G, Driscoll DJ, Liu Y. Sylvian fissure morphology in Prader Willi syndrome and early onset morbid obesity. Genetics in Medicine 2007a; 9(8):53643. Miller JL, Couch J, Schnaulfuss I, He G. Morphological brain abnormalities detected by three dimensional magnetic resonance imaging in Prader Willi syn drome. Am J Med Genet A 2007b; 143:47683. Miller JL, James GA, Goldstone AP, Couc h JA, He G, Driscoll DJ, Liu Y. Enhanced activation of reward mediating prefrontal regions in response to food sti muli in Prader Willi syndrome. J Neu ral Neurosurg Psychiatry 2007c; 78:61519. M inokoshi Y, Okano Y, Shimazu T. Regulatory mechanism of the ventromedial hypothalamus in enhancing glucose uptake in skeletal muscles. Brain Res 1994; 649: 343 7. Mo genson GJ. Studies of the nucleus accumbens and its mesolimbic dopaminergic affects in relation to i ngestive behaviors and reward. In The Neural Basis of Feeding and Reward. Eds : Hoebel GB, Novin D. Brunswick, ME, Haer Institute 1 982; 275 506. Morley JE. N europeptide regulation of appetite and weight. Endocr Rev 1987; 8:256 87. Morris JS. Involvement of human amgydala and orbitofrontal cortex in hunger enhanced memory for food stimuli. J Neurosci 2001; 21:530410. Morton GJ, Cummings DE, Baskin DG, Barsh GS Schwartz MW. Central nervous system control of food intake and body weight. Nature 2006;443:289 95. Ng DK, Chow P, Ming S, Hong S, Lau S, Tse D, Kwong WK, Wong MF, Wong WT, Fu YM, Kwok KL, Li H, Ho JC. A doubleblind, randomized, placebocontrolled trial of acupuncture for the treatment of childhood persistent allergic rhinitis. Pediatrics 2004;114(5):12427. Nichols RD, Knepper JL. Genome organization, function and imprinting in Prader Willi and Angelman syndromes. Annu Rev Genomics Hum Genet 2001; 2:15 3 75. Nielsen SJ, Siega Riz AM, Popkin BM. Trends in food locations and sources among adolescents and young adults. Prev Med 2002;35(2):10713.

PAGE 87

87 Nixon GM, Brouillette RT. Sleep and breat hing in Prader Willi syndrome. Pediatr Pulmonol 2002; 32:20917. Noble EP, Blum K, Ritchie T. Allelic association of the D2 dopamine receptor gene with receptor binding characteristic s in alcoholism. Arch Gen Psychiatry 1991; 48:64854. Noble E P, Fitch RJ, Ritchie T. The D2 dopamine receptor gene:obesity, smoking and mood. I n Obesity Assessment: To ols, Methods, Interpretations. Ed s: St. Jeor ST, Koop CE. New York :NY, Chapman and Hall 1997; 522 33. Noble EP, Noble RE, Ritchie T. D2 dopamine receptor gene: obesity, smoking and mood. Int J Eat Disord 1994; 15:205 17. ODoherty J P. Reward representations and rewardrelated learning in the human brain: insights from neuroimaging. Curr Opin Neurobiol 2004; 14:76976. Ogura K, Shinohara M, Ohno K Frontal behavioral syndromes in Prader Willi syndrome. Brain Dev 2008;30(7):469476. O ngur D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex 2000;10(3):206 19. Pagliardini S, Ren J, Wevrick R, Greer JJ. Developmental abnormalities of neuronal structure and fu nction in prenatal mice lacking the Prad er Willi syndrome gene necdin. Am J Pathol 2005; 167:17591. Parham ES. Compulsive eating: apply ing a medical addiction model. In TB Van Itallie and AP Simopoulos Obesity: new directions in assessment and management Philadelphia, PA: The Charles Press 1995. Park J, Park H, Lee H, Lim S, Ahn K, Lee H. Deqi sensation between the acupunctureexperienced and the nave: a Korean study II. Am J Chi Med 2005;33(2):32937. Parrish TB, Schaeffer A, Catanese M, Rogel MJ. Functional magnetic resonance imaging of real and sham acupuncture. Engineering in Medi cine and Biology Magazine, IEEE 2005;24:3540. Petrovich GD, Canteras NS, Swanson LW. Combinatorial amygdalar inputs to hippocampal domains and hypothalamic behavior systems. Brain Res Brain Res Rev 2001;38(12):24789. Petrovich GD, Gallagher M. Control of food consumption by learned cues: a forebrainhypothalamic network. Physiol Behav 2007;91(4):397403.

PAGE 88

88 Petrovich GD, Ross CA, Holland PC, Gallagher M. Medial prefrontal cortex is necessary for an appetitive contextual conditioned stimulus to promote eating in sated rats. J Neurosci 2007;27(24):643641. Phillips AG, Ahn S, Howland JG. Amygdalar control of the mesocorticolimbic dopamine system: parallel pathways to motivated behavior. Neurosci Biobehav Rev 2003;27(6):54354. Pissios P and Maratos Flier E. More than satiety: central serotonin signaling and gluco se homeostasis. Cell Metabolism 2007;6:3457. Proto C, Romualdi D, Cento RM, Roma no C, Campagna G, Lanzone A. Fr ee and total leptin serum levels and soluble leptin receptors levels in two models of genetic obesity: the Prader Willi and the Down syndromes. Metabol Clin Experimental 2007; 56:107680. Qin W Tian J Bai L Pan X Yang L Chen P Dai J, Ai L Zhao B Go ng Q Wang W von Deneen KM, Liu Y F MRI connectivity analysis of acupuncture effects on an amygdalaassociated brain network Molecular Pain 2008;4(55): 1 17 Rada P, Avena NM, Hoebel BG. Daily bingeing on sugar repeatedly releases do pamine in t he accumbe ns shell. Neurosci 2005; 134:73744. Rada PV, Hoebel BG. Supraadditive effect of dfenfluramine plus phentermine on extracellular acetylcholine in the nucleus accumbens: possible mechanism for inhibition of exc essive feeding and drug abuse. Pharmacol Bioch em Behav 2000; 65:369 73. Roitman MF, van Dijk G, Th iele TE Dopamine mediation of the feeding response to violations of spatial and temporal expectancies. Behav Brain Res 2001;122(2):1939. Rolls ET, McCabe C. Enhanced affective brain representations of chocolate in cravers vs. noncravers. Eur J Neurosci 2007;26(4):106776. Rother E, Kohner AC, Bruning JC. Neurocircuits integrating hormone and nutrient signaling in control of glucose metabolism Am J Physiol Endocrinol Metab 2008;294:E8101 6. Rubin CT, Capilla E, Luu YK, Busa B, Crawford H, Nolan DJ, Mittal V Rosen CJ, Pessin JE, Judex S. Adipogenesis is inhibited by brief, daily exposure to highfrequency, extremely low magnitude mechanical signals. Pro c Natl Acad Sci USA 2007; 104(45):1787984.

PAGE 89

89 Saad MF, Khan A, Sharma A, Michael R, RiadGabriel MG, Boyadjian R, Ji nagouda SD, Steil GM, Kamdar V. Physiological insulinemia ac utely modulates plasma leptin. Diabetes 1998; 47:544 9. Sahu A, Kalra SP. Neuropeptide r egulation of feeding behavior. Neuropeptide Y. TEM 1993; 4(7):21724. Sala mone JD, Cousins MS, Snyder BJ. Behavioral functions of nucleus accumbens dopamine: empirical and conceptual problems with the anhedonia hypothesis. Neurosci Biobehav Rev 1997; 21:341 59. Sato Y, Ito T, Udaka N, Kanisawa M, N oguchi Y, Cushman SW, Satoh S. Immunohistochemical localization of facilitateddiffusion glucose transporters in rat pancr eatic islets. Tissue Cell 1996; 28:63743. Schroeder BE, Binzak JM, Kelley AE. A common profile of prefrontal cortical activation following exposure to nicotineor chocolate associated contextual cues. Neuroscience 2001;105:535 45. Schur EA, Kleinhans NM, Goldberg J, Buchwald D, Schwartz MW, Maravilla K. Activation in brain energy regulation and reward centers by food cues varies with choice of visual stimulus. Int J Obes 2009;33(6):65361. Schwartz MW Woods SC, Porte D Jr. Central nervous system control of food intake. Nature 2000; 404:66171. Scislows ki PW, Tozzo E, Zhang Y. Biochemical mechanisms responsible for the attenuation of diabetic and obsess conditions in ob/ob mice treat ed with dopaminergic agonists. Int J Overweight Relat Metab Disord 1999; 23:425 31. Seidell JC, Nooyens AJ, Visscher TL. Cost effective measures to prevent obesity: epidemiological basis and appropriat e ta rget groups. Proc Nutr Soc 2005; 64:1 5. Selikowitz M, Sunman J, Pendergast A. Fenfluramine in Prader Willi syndrome: a doublebl ind, placebocontrolled trial. Arch Dis Child 1990; 65:1124. Shafshak TS. Electroacupuncture and exercise in body weight reduc tion and their application in rehabilitating pati ents with knee osteoarthritis. Am J Chin Med 1995; 23:1525. Shapi ra NA, Lessing MC, He HG. Satiety dysfunction in Prader Willi syndrome demonstrated by fMRI. J Neurol Neurosurg Psychi atry 2005; 76:2602. Sh iraishi T Onoe M Kojima T Sameshima Y, Kageyama T Effects of auricular stimulation on feeding related hypothalamic neuronal activity in normal and obese rats. Brain Res Bull 1995; 36: 141 8

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90 Siedentopf CM, Golaszewski SM, Mottaghy FM, Ruff CC, Felber S, Schlager A. Functional magnetic resonance imaging detects activation of the visual association cortex during laser acupuncture of the foot in humans. Neurosci Letters 2002;327:536. Simmons WK, Martin A, Barsalou LW. Pictures of appetizing foods activate gustatory cortices for taste and reward. Cerebral Cortex 2005;15:16028. Singh NN, Lancioni GE, Singh AN A mindfulness based health wellness program for an adolescent with Prader Willi syndrome. Behav Modif 2008;32(2):16781. Sobik L, Hutchison K, Craig head L. Cueelicited craving for food: a fresh approach to the study of binge eating. Appetite 2005;44(3):25361. Solinas M, Goldberg SR. Motivational effects of cannabinoids and opioids on food reinforcement depend on simultaneous activation of cannabinoid and opioid systems. Neuropsychopharmacology 2005;30:203545. Spring B, Schneider K, Smith M, Kendzor D, Appelhans B, Hedeker D, Pagoto S. Abuse potential of carbohydrates for ov erweight carbohydrate cravers. Psychopharmacol 2008;197(4):63747. Steiner RP, Kupper N, Davis AW. Obesity and appetite control: comparison of acupuncture therapies and behavior modification. Proceedings: International Forum on Family Medicine Education. Society of Teachers of Family Medicine, Kansas City, MO 1983; 31326. Stoec kel LE Weller RE, Cook EW, 3rd Widespread rewardsystem activation in overweight women in response to pictures of highcalorie foods. Neuroimage 2008;41(2):63647. Stux G, Pomeranz B. Ac upuncture: textbook and atlas. Springer Verlag: Berlin 1987. Sun Q Xu Y. Simple obesity and obesity hyperlipemia treated with otoacupoint pellet pressure and body acupuncture. J Tradit Ch in Med 1993; 13:226. Sun Y, Asnicar M, Smith RG. Central and peripheral roles of ghrelin on gluc ose metabolism. Neuroendocrinol 2007; 86:21528. Swaab DF, Purba JS, Hofman MA. Alterations in the hypothalamic paraventricular nucleus and its oxytocin neurons (putative satiety cells) in Prader Willi sy ndrome: a study of five cases. J Clin Endocrinol Metab 1995; 80:573 9. Swanson LW, Petrov ich GD. What is the amygdala? Trends Neurosci 1998;21:323 31.

PAGE 91

91 Tartar RE, Ammerman RT, Ott PJ. Handbook of substance abuse. Neurobehavioral Pharmacology. NY: Premium Press 1998. Tataranni PA, Delparigi A. Functional neuroimaging: a new generation of human brain studies in obesity resear ch. Obes Rev 2003; 4:22938. Tiggemann M, Kemps E. The phenomenology of food cravings: the role of mental imagery. Appetite 2005;45(3):30513. Tsui P, Leung MC. Comparison of the effectiveness between manual acupuncture and electroacupuncture on patients with tennis elbow. Acupunct Electrother Res 2002;27:10717. Volkow ND, Wang G J, Fowler JS. Nonhedonic food motivation in humans involves dopamine in the dorsal striatum and methylphenidate amplifies this effect. Synapse 2002; 44:175 80. Volkow ND, Wise RA. How can drug addiction help us understand obesity? Nat Neurosci 2005;8(5):55560. Wang G, Volkow ND, Thanos PK. Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. J Addict Dis 2004; 23:39 53. Wang G J, Volkow ND, Fowler JS. Enhanced resting activity of the somatosensory cortex in overweight subjects. Neu roreport 2002; 13:11515. Wang GJ, Volkow ND, Logan J Brain dopamine and obesity. Lancet 2001; 357: 3547. Wang G J, Volkow ND, Thanos PK, Fowler JS Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. Psych Annals 2003; 33(2):3953. Wang GJ, Volkow ND, Thanos PK Similarity between obesity and drug addiction as assessed by neurofunctional imaging: A concept review. J Addict Dis 2004;23(3):3953. Weingarten HP, Elston D. The phenomenology of food cravings. Appetite 1990;15(3):23146. Wenhe Z, Yucun S Change in levels of monoamine neurotransmitters and their main meta bolites of rat brain after electric acupuncture treatment. Int J Neurosci 1981;15: 147 9 Wilson GT. E ating disorders and addiction. Drugs & Society 1999; 15:87101.

PAGE 92

92 Wilson GT. The addiction model of eating disorders: a critical analysis. Adv Beha v Res The rapy 1991; 13:27 72. Wise RA. Dopamine, learning and motivation. Nat Rev Neurosci 2004;5(6):48394. W Right P, Albarracin D, Brown RD, Li H, He G, Liu Y. Dissociated responses in the amygdal a and orbitofrontal cortex to bottom up and topdown compo nents of emotional evaluation. NeuroImage 2008; 39:894 902. Wu MT Hsieh JC Xiong J Yang CF Pan HB Chen YC Tsai G Rosen BR Kwong KK. Central nervous pathway for acupuncture stimulation: localization of processing with functional MR imaging of the br ain preliminary experience. Neuroradiology 1999;212(1):13341. Wurtman RJ, Wurtman JJ. Brain serotonin, carbohydrate cr aving, obesity and depression. Obes Res 1995; 3(4):477S 80S. Yoo SS, Teh EK, Blinder RA, Jolesz FA. Modulation of cerebellar activities by acupuncture stimulation: evidence from fMRI study. NeuroI mage 2004;22(2):93240. Zipf WB, ODorisio TM, Cataland S. Pancreatic polypeptide responses to protein meal challenges in overweight but otherwise normal children and overweight children with Pra der Willi synd rome. J Clin Endocrinol Metab 1983; 57:107480. Zhao M Liu Z, Su J. The timeeffect relationship of central action in acupuncture treatment for weight reduction. J Tradit Chin Med 2000;20: 26 9

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93 BIOGRAPHICAL SKETCH Karen Milada von Deneen w as born in Ostrava, Czech Republic. She came from a large, well rounded family who decided to emigrate to the United States in 1984. English was her 4th language at that time out of the 9 that she has studied so far. Ever since the age of 5, Karen wanted t o be a veterinarian and pursue academia. Her family moved all over North America and so have her academic endeavors. She graduated from Barstow High School in California as the salutatorian and chose to attend Morehead Stat e University in Kentucky where sh e obtained an associates degree in veterinary t echnology, a bachelors degree in general education, graduated from the highly competitive Honors Program and played National Collegiate Athletic Association ( NCAA ) womens tennis In 1998, Karen won the cov eted NCAA Woman of the Year and Ohio Valley Conference Scholar Athlete of the Year She then obtained her M aster of Science in a nimal sciences with an integrated minor in e quine r eproduction from Oregon State University. She was accepted to the College of Veterinary Medicine and Biological Sciences ( CVMBS) at Colorado State University to become a veterinarian It was there that she was exposed to veterinary acupuncture and alternative medicine. In 2004, Karen was recruited by the Department of Pathobiology at the University of Florida to begin her doctorate training in immunology and infectious diseases. In 2007, she transferred to the Department of Psychiatry to study neuroimaging and physiology. Her mentor enabled Karen to go to China from 2007 to 2008 to do alternative medicine research and promote the scientific method at different universities. Some of her other research orientation has been in food addict ion, Prader Willi Syndrome, and Parkinsons disease. Karen currently has a faculty position at XiDia n University in Xian, China.



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1 FUNCTIONAL MAGNETIC RESONANCE STUDY OF THE CENTRAL EFFECTS OF ACUTE ACUPUNCTURE ON GLUCOSE LEVELS AND CORE BODY T EMPERATURE IN MEN By KAREN MILADA von DENEEN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2009

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2 2009 Karen Milada von Deneen

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3 To the research group in the Life Science Research Center of XiDian University and Medical Image Processing Group of the Institute of Automation in the Chinese Academy of Sciences; Beijing University of Chinese Medicine ; the Yijun Liu laboratory in the Department of Psychiatry; faculty and staff at the College of Veterinary Medicine; and Jesus Christ

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4 ACKNOWLEDGMENTS I want to first and foremost thank the faculty, stu dents, and staff of the Life Science Research Center of XiDian University, Medic al Image Processing Group of the Institute of Automation in the Chinese Academy of S ciences, and the Beijing University of Chinese Medicine for their financial support, re sources, and assistance in making this project possible. I want to specifically thank the Yijun Liu laboratory in the Department of Psychiatry, the College of Veterinary Medicine espe cially Sally O’Connell, and Dr. Mary Brown’s lab. I want to also thank my family, friend s, Dr. Frank Skidmore, Phil Winters, and mostly God for getting me through this.

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5 TABLE OF CONTENTS page FUNCTIONAL MAGNETIC RESONANCE STUDY OF THE CENTRAL EFFECTS OF ACUTE ACUPUNCTURE ON GLUCOSE LEVELS AND CORE BOD Y TEMPERATURE IN MEN ................................ ................................................... ...... 1 ACKNOWLEDGMENTS ................................... ................................................... ............ 4 ABSTRACT .......................................... ................................................... ...................... 12 LITERATURE REVIEW ................................. ................................................... ............. 14 Introduction ...................................... ................................................... .................... 14 Background ........................................ ................................................... .................. 16 Acupuncture Overview .............................. ................................................... .... 16 Acupuncture Effects on Physiology ................. ................................................. 1 7 Acupuncture Affects Satiety ....................... ................................................... ... 17 Acupuncture Treats Obesity ........................ ................................................... .. 18 Causes of General Obesity ......................... ................................................... .. 19 Prader-Willi Syndrome Background: Causes and Sympto ms ........................... 20 Food Addiction Background: Causes and Symptoms .... .................................. 22 Shared Anatomical Areas in the Brain .............. ................................................ 24 Hypothalamic Regulation of Appetite/Afferent Hormon al Signaling .................. 25 Endocrinology of Obesity........................... ................................................... .... 27 Orexigenic and Anorexigenic Pathways .............. ............................................. 29 Food Cues: Internal and External Appetite Triggers ......................................... 30 Reward System Hypothesis .......................... ................................................... 38 Neuroimaging Studies .............................. ................................................... ..... 40 Treatments for PWS and FA ......................... ................................................... 42 AIMS, MATERIALS AND METHODS ....................... ................................................... .. 47 Aims .............................................. ................................................... ....................... 47 Rationale ......................................... ................................................... ..................... 47 Subjects .......................................... ................................................... ..................... 48 Experimental Design ............................... ................................................... ............. 49 Physiological Measurements ........................ ................................................... ....... 49 Treatment Methods ................................. ................................................... ............. 50 fMRI Parameters ................................... ................................................... ............... 51 Preprocessing of Data and Analysis ................ ................................................... .... 51 RESULTS ........................................... ................................................... ........................ 54 Parametric Test Statistics ........................ ................................................... ............ 54 Nonparametric Test Statistics ..................... ................................................... ......... 55 Physiological Data ................................ ................................................... ............... 57

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6 Neuroimaging Data ................................. ................................................... ............. 61 DISCUSSION AND CONCLUSION.......................... ................................................... .. 72 Discussion ........................................ ................................................... ................... 72 Conclusion ........................................ ................................................... ................... 75 Study Limitations ................................. ................................................... ................ 76 LIST OF REFERENCES ................................ ................................................... ............ 77 BIOGRAPHICAL SKETCH ............................... ................................................... .......... 93

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7 LIST OF TABLES Table page Table 2-1. Inclusion and exclusion criteria used to select subjects for this experiment. ....................................... ................................................... ............... 48 Table 3-1. Overlapping areas during acupuncture tre atment from functional connectivity analysis using the amygdala as the reg ion of interest..................... 64 Table 3-2. Overlapping areas during minimal sham ac upuncture treatment from functional connectivity analysis using the amygdala as the region of interest. ... 66 Table 3-3. Overlapping areas during acupuncture tre atment from functional connectivity analysis using the hypothalamus as the region of interest. ............. 68 Table 3-4. Overlapping areas during minimal sham ac upuncture treatment from functional connectivity analysis using the hypothal amus as the region of interest. ......................................... ................................................... ................... 70 Table 3-5. Common overlapping areas during acupunct ure and minimal sham acupuncture treatments from functional connectivity analysis using the amygdala and hypothalamus as the regions of interes t. ..................................... 71

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8 LIST OF FIGURES Figure page Figure 3-1. Measurement of corrected average core b ody temperature (CBT) in degrees Celsius shown before (Pre-Acu), during (Acu ) and after (Post-Acu) acupuncture (ACU) or minimal sham (min SHAM) treatm ents in overweight adult Chinese males (n = 10 for ACU and n = 9 for m in SHAM). ........................ 57 Figure 3-2. Measurement of uncorrected average bloo d glucose (Glu) shown before (Pre-Acu), during (Acu) and after (Post-Acu) real a cupuncture (ACU) or minimal sham (min SHAM) treatment in overweight adu lt Chinese males (n=10 for ACU and n = 9 for min SHAM). ............ ............................................... 58 Figure 3-3. Averaged major Deqi sensations (sorenes s, numbness, fullness, coolness, warmth, sharp pain, dull pain, and heavin ess) comparison between real acupuncture (ACU) and minimal sham (min SHAM) treatments in overweight adult Chinese males (n=10 in ACU and n = 9 min SHAM). .............. 59 Figure 3-4. Averaged hunger sensation comparison be tween real acupuncture (ACU) and minimal sham (min SHAM) treatments in ove rweight adult Chinese males (n=10 in ACU and n = 9 in min SHAM). ..................................... 60 Figure 3-5. Blood oxygen level dependent (BOLD) sig nificant brain regions from an amygdala-related functional connectivity analysis c omparing real acupuncture (ACU) versus minimal sham (SHAM)-treate d individuals. .............. 62 Figure 3-6. Blood oxygen level dependent (BOLD) sig nificant brain regions from a hypothalamus-related functional connectivity analys is comparing real acupuncture (ACU) versus minimal sham (SHAM)-treate d individuals. .............. 63

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9 LIST OF ABBREVIATIONS ACC Anterior cingulate cortex Ach Acetylcholine ACU Real acupuncture AgrP Agouti related protein AMY Amygdala ARC Arcuate nucleus ARN Appetite regulating network BA Brodmann area BLA Basolateral amygdala BMA Basomedial amygdala BMI Body mass index BMR Basal metabolic rate BOLD Blood oxygen level dependent CART Cocaine-amphetamine regulating transcript CBT Core body temperature CN Caudate nucleus CNS Central nervous system CRH Corticotropin releasing hormone DA Dopamine DCT Discrete cosine transform analysis DLPFC Dorsolateral prefrontal cortex EAS Electroacupuncture stimulation EPI Echoplanar imaging FA Food addiction fMRI Functional magnetic resonance imaging

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10 FOV Field of view FWHM Full width at half maximum Gaussian kernel ana lysis GABA Gamma-aminobutyric acid GCM Granger causality method analysis GH Growth hormone Glu Glucose HIPP Hippocampus HYP Hypothalamus IAPS International Affective Picture System ILA Infralimbic area LA Lateral amygdala LHA Lateral hypothalamus MC4 Melanocortin-4-receptor MCH Melanin concentrating hormone MFD Mesolimbic frontocortical dopamine system MNI Montreal Neurological Institute mOFC Medial orbitofronal cortex mPFC Medial prefrontal cortex mPVN Magnocellular paraventricular nucleus nACC Nucleus accumbens NCCAM National Center for Complementary and Alterna tive Medicine NIH National Institute of Health NPY Neuropeptide Y OFC Orbitofrontal cortex ORX Orexin

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11 PET Positron resonance imaging PL Paralimbic area POMC Pro-opiomelanocortin pPVN Parvocellular paraventricular nucleus PVN Paraventricular nuclei PWS Prader-Willi syndrome ROIs Regions of interest SCA Seed voxel correlation analysis SHAM Minimal sham acupuncture TCM Traditional Chinese Medicine THC Delta-9-tetrahydrocannabinol VMH Ventromedial hypothalamus VMPFC Ventromedial prefrontal cortex vPFC Ventral prefrontal cortex WFU Wake Forest University -MSH Alpha melanocyte-stimulating hormone

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12 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FUNCTIONAL MAGNETIC RESONANCE STUDY OF THE CENTRAL EFFECTS OF ACUTE ACUPUNCTURE ON GLUCOSE LEVELS AND CORE BODY T EMPERATURE IN MEN By Karen Milada von Deneen December 2009 Chair: Yijun Liu Major: Veterinary Medical Sciences Obesity is a major health problem worldwide and acu puncture is an effective treatment modality for this condition. This study f ocused on how acupoints ST 36 (Zusanli) and SP 9 (Yuan Ling Quan) and their sham acupoints acutely act on the limbic system via dopamine to affect satiety, glucose (Glu ) blood levels and core body temperature (CBT) in healthy, overweight, adult, no n-dieting Chinese males who have abstained from eating 12 hours prior to the functio nal magnetic resonance imaging (fMRI) experiment. Results for Glu and CBT indicate d no significance ( p > 0.05) in both interand intragroup comparisons due to variable i ndividual responses to treatment. Hunger survey feedback was significant ( p < 0.05) between the acupuncture (ACU) and sham control (min SHAM) groups. Soreness or Deqi, w as the only significant ( p < 0.05) intergroup sensation. Connectivity analysis utilize d the amygdala (AMY) and hypothalamus (HYP) as regions of interest (ROIs). C ommon overlapping regions for both ACU and min SHAM using the AMY as the ROI were the putamen, caudate, parahippocampus, hippocampus (HIPP), insula, cingul ate, and Brodmann areas (BA) 6, 21, 22, 24, 25, 30, 31, 34, 36, 40, and 47. The com mon overlapping regions for both ACU and min SHAM using the HYP as the ROI were the HIPP, putamen, thalamus, and

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13 BA 28, 32, 38, 40, and 47. These areas are involved in dopamine, basal metabolic rate, heart rate, cognition and satiety regulation. This project will be of great importance in helping understand how acupuncture can be a safe, i nexpensive, and highly effective treatment modality for weight control.

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14 CHAPTER 1 LITERATURE REVIEW Introduction Acupuncture, an ancient eastern therapeutic techniq ue, is emerging as an important modality of complementary medicine in Western count ries (Li et al., 1992; Knight et al., 2001; Ng et al., 2004). The scope of my acupuncture research is new, exploratory, and is in the early stages of development that specific ally addresses human health-related issues. A variety of symptoms can be treated by acu puncture clinically (Anonymous NIH, 1998). However, the mechanism of how acupunctu re works has not been clearly defined. Acupoint specificity has been under much s cientific debate. For example, the visual cortex was activated by laser acupuncture at points BL 67 (Zhi Yin) (Siedentopf et al., 2002), LI 4 (Hegu), ST 36 (Zusanli), BL 60 (Ku nlun), and BL 67 (Litscher et al., 2004). Li et al. (2003) induced visual cortex activ ation at four vision-implicated acupoints (BL 60, BL 65 (Shugu), BL 66 (Zutonggu), and BL 67) Acupuncture at K 3 (Taixi) activated the auditory cortex (Parrish et al., 2005 ). It was reported that PC 6 (Neiguan) was used to diminish nausea (Knight et al., 2001; Y oo et al., 2004; Bai et al., 2009a). Ng et al. (2004) showed that stimulation at HN 3 (Y intang), NH 8 (Yingxiang), and ST 36 was used to treat persistent allergic rhinitis in c hildren. ST 36 and SP 6 (Sanyinjiao) are valid for visceral disorders (Li et al., 1992). Pat ients with lateral epicondylitis or tennis elbow can be treated at GB 34 (Yanglingquan) and ST 36 (Tsui et al., 2002). On the other hand, Gareus et al. (2002) did not detect sig nificant blood-oxygen level dependent (BOLD) signal changes in the visual cortex when nee dling GB 37 (Guangming). Kong et al. (2009) indicated that electroacupuncture stimul ation (EAS) induced no significant changes in the occipital cortex at BL 60, GB 37, an d an adjacent non-acupoint.

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15 Recently, Cho et al. (2006) retracted their early r esearch results, stating there is no acupoint specificity. Most importantly, acupuncture has been used to specifically treat obesity and weight-related issues (for recent revie w see Cho et al., 2009). The development of imaging techniques, such as posi tron resonance imaging (PET) and functional magnetic resonance imaging (fMRI), h as provided new tools for us to obtain a non-invasive appreciation of the anatomy a nd physiological function involved during acupuncture in humans and animals (Lewith et al., 2005; Qin et al., 2008; Bai et al., 2009b). We currently use fMRI to answer questi ons relating to acupoint specificity and effectiveness with respect to obesity and the p hysiology of metabolism. Recent research has not addressed this area in overweight individuals. We believe the physiology and response to acupuncture differs betw een obese versus overweight individuals. Hence, using the International Affecti ve Picture System (IAPS), we can further evaluate the hunger signal and response to food stimuli using fMRI as well. Obesity and factors leading to being obese or overw eight are an enigmatic aspect of scientific research on a global scale. It has been shown that obesity ranks second to tobacco-related deaths at about 300,000 deaths per year (Gold, 2003). Numerous studies in the past decade have attempted to shed l ight on not only resolving but also preventing obesity in both animals and humans (Kuma nyika and Obarzanek, 2003; Calle and Kaaks, 2004; Gill et al., 2005; Seidell e t al., 2005; Rubin et al., 2007; Galani and Schneider, 2007). Two of the most common geneti c and neurophysiological causes of obesity, Prader-Willi Syndrome (PWS) and food ad diction (FA) respectively, will be discussed in greater detail. PWS was chosen as a we ll-defined genetic model to support the necessity of this project since it may help explain certain neurophysiological

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16 mechanisms that affect appetite and FA, which event ually lead to obesity, especially in children and young adults. It is ultimately better than an animal model since it can be directly studied and applied to human obesity condi tions. Both PWS and FA are driven by similar feeding behaviors, obsessive thoughts an d compulsions, and hormones. Currently, the mechanisms of these two disorders ar e still not well understood. This dissertation presents a general overview of the cha racteristics, causes, endocrinology, neuroanatomy, hypotheses, treatments and preventati ve measures of PWS and FA. PWS provides good genetic modeling for the mechanis ms of obesity and is a good candidate for acupuncture treatment. Background Acupuncture Overview Acupuncture involves stimulating different anatomic al points in the body that exert different desired neurophysiological effects on the appropriate organ system based on Traditional Chinese Medicine (TCM) that ha s been implemented for over 3000 years. There are 14 meridians along which Qi or ene rgy flows. There are well over 360 acupoints along these meridians (Cho et al., 2002). When these acupoints are stimulated, the desirable sensation known as Deqi i s felt, indicating the release of opioid peptides. There are various sensations of Deqi repo rted such as soreness, numbness, tingling, and so on (Park et al., 2005; MacPherson and Asghar, 2006; Hui et al., 2007). Acupuncture was not introduced into the United Stat es until the 1970s. It was not until 1996 when the National Institute of Health (N IH) and National Center for Complementary and Alternative Medicine (NCCAM) conc luded that acupuncture was efficacious for treating conditions such as nausea, arthritis, and others (Anonymous NIH, 1998). The controversy regarding acupoint spec ificity continues, hence

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17 neuroimaging studies have been crucial in addressin g this issue. PET and fMRI studies started in 1997 (Cho et al., 1997). There have been no direct publications that correlated clinical outcomes in pathological condit ions with induced acupuncture changes in the brain. The trial design and data int erpretation have been problematic in acupuncture research (Bai et al, 2009b). We devised our control technique based on a study by Kleinhenz et al. (1999) who used the Strei tberger needle method. Acupuncture Effects on Physiology The most important aspect of this study will be to mechanistically depict how and why acupuncture (ACU) and minimal sham (min SHAM) a ffect glucose (Glu) homeostasis and hypothalamic regulation of core bod y temperature (CBT) and basal metabolic rate (BMR). Sun et al. (2007) studied how ghrelin centrally and peripherally affects Glu homeostasis. Ghrelin causes the release of growth hormone-releasing peptides and neuropeptide Y (NPY) as well as increa ses appetite (Kojima et al., 1999). It has been shown ACU decreases ghrelin, hence appe tite decreases as shown by our unpublished preliminary results. Other key neurocir cuits that control glucose metabolism are best reviewed by Rother et al. (2008). It is ex pected ACU will affect liver gluconeogenesis via insulin, its mediators and gast rointestinal afferents centrally carrying information regarding energy intake. Acupuncture Affects Satiety One focus of this study is to delineate the neuroho rmonal pathways associated with the hunger response. Pissios and Maratos-Flier (2007) proposed that central serotonin affects glucose homeostasis since inhibit ion of serotonin reuptake decreases appetite. Apparently the arcuate nucleus pro-opiome lanocortin (ARC POMC) neurons respond to serotonin as well as leptin and Glu, whi ch are affected by ACU treatment

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18 (Cabioglu et al., 2006). Low leptin and other adipo kine levels during fasting stimulate food intake and decrease BMR (for full review see A hima and Lazar, 2008). Leptin controls Glu and lipid metabolism via AMP-activated protein kinase and stearoylcoenzyme A desaturase 1 in liver and muscle (Ahima et al., 2000), which may be targeted by ACU treatment. Brain regions involved i n satiety that may be involved in this study are inferior parietal lobes, dorsolateral pre frontal cortex (DLPFC), and ventromedial prefrontal cortex (VMPFC) (James et al ., 2004). Our lab has shown that there is a delayed hypothalamic response to reach s atiety in obese individuals, hence it would be interesting to determine if that is the ca se in overweight individuals. Acupuncture Treats Obesity Obesity research has recognized that there are many factors contributing to this devastating disorder. Much debate has surfaced amon gst scientists to develop models which might be useful in understanding why obesity has become a major health problem and epidemic. Numerous studies have shown t hat manual ACU (Lacey et al., 2003) as well as EAS (Hsu et al., 2005a,b; Cabioglu and Ergene, 2006; Cabioglu et al., 2006; Lee et al., 2006) are effective means for wei ght loss and weight control. One of our initial studies may contribute to help address areas in the brain activated by ACU that may suppress appetite and prevent weight gain by decreasing food intake. Another unpublished pilot study addressed how overweight in dividuals respond to visual stimuli before and after ACU or min SHAM treatments. James et al. (2004) showed activation in the insula, prefrontal cortex (PFC), amygdala (A MY), thalamus, nucleus accumbens (nACC), and ventral basal ganglia in hungry subject s when viewing food versus nonfood related IAPS pictures, which we hope to see as well in future studies. Data from reward pathways showed that photographs of rich, fa ttening food induce significantly

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19 greater activation than non-food object photographs in the left and right striatum (ventral striatum, putamen, caudate), as well as the midbrai n (including the ventral tegmental area), left AMY, and left orbitofrontal (OFC) (Schu r et al., 2009). These pathways integrate aspects of motivation for feeding with hy pothalamic inputs on the state of energy balance (Kelley et al., 2005). Causes of General Obesity Joranby et al. (2005) defines obesity as “an imbala nce between energy input and energy expenditure.” Obesity has been one of the mo st common health disorders affecting both modern and developing countries. Alt hough it is well-known that overeating is the leading cause of obesity, the ove rall etiology is not well explained. Moreover, overeating has been classified as an addi ction that comes from a variety of psychological as well as physiological causes. Anot her factor is the effect of emotions associated with the reward system of the brain (Jor anby et al., 2005). There exist other numerous theories that are beyond the scope of this dissertation as to why this occurs, such as deviant physiological processes involved in eating and homeostasis. It is well-recognized that the hypothalamus (HYP) is the key component for maintaining homeostasis in the body, and is responsive to signals that regulate food intake. If these signals are aberrant in any way, this could lead to delayed feelings of satiety in the individual (Joranby et a l., 2005). The intrinsic and extrinsic signaling pathways will be discussed in more detail Sahu and Kalra (1993) best describe which neuropeptides regulate food intake. These will be discussed in further detail. Aside from the physiological explanations of obesit y, the socioeconomic environment plays a crucial role in eating behavior For instance, social and gender

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20 roles, mass westernization and technological advanc es, and the media have all contributed to the growing population of overweight individuals. Becker et al. (2004) showed that there were three main factors that are responsible for obesity. First, many underdeveloped countries are going through westerni zation and are mimicking western eating habits. Second, the cultural portrayal of ob esity as being undesirable and unsuccessful is changing eating habits, but not nec essarily for the better. Finally, the roles of men and women have caused a shift in eatin g patterns as well. With more women being in the work force and often times seen as breadwinners in the family, this may lead to the entire family eating irregular, non -nutritious, fast-food meals. Although there are various genetic disorders leading to obes ity, Prader-Willi syndrome (PWS) is one of its key genetic causes that will be discusse d below. Prader-Willi Syndrome Background: Causes and Sympto ms Individuals with the rare disorder, PWS, are identi fied as being genetically overweight since childhood. About 70% of cases are caused by a paternal genetic deletion on chromosome 15 (15q11-13), while 25% are from a maternal uniparental disomy of chromosome 15. The remaining 1-5% of PWS cases result from certain imprinting defects, which have a 50% risk potential to recur in future offspring (Glenn et al., 1997; Nichols and Knepper, 2001, Benarroch et al., 2007). There is a loss of specific brain genes such as MKRN3, MAGEL2, NDN, SNURF-SNRPN and sno -RNA that are misrouted or lost resulting in abnormal cortical de velopment in PWS (Pagliardini et al., 2005). These genetic anomalies can be detected by D NA methylation analysis and in situ hybridization of the alleles (Benarroch et al., 20 07). Human subjects with PWS are characterized as having dolichocephaly, almondshaped eyes, small mouth, hands, and feet, decrease d muscle mass and tone

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21 (Cassidy, 1997), infantile hypotonia, early onset o f obesity due to central dysfunction (around 18 to 36 months of age), hypogonadia, short stature (Goldstone, 2004), and show major disturbances in appetite, sleep, breathi ng and metabolism regulation, such as delayed satiety, premature return of hunger afte r eating a meal, seeking and hoarding food and food-related objects, and ingesti ng inanimate items (Miller et al., 2007a) as well as excessive daytime drowsiness, poo r ventilation, hypercapnia, and dental caries (Nixon and Brouillette, 2002). Overal l, many systems are affected by PWS such as the central nervous system (CNS), gastroint estinal, urogenital, cardiovascular, respiratory, and dermatologic resulting in numerous medical conditions and disorders (Benarroch et al., 2007). Anatomically, PWS individuals have speech and langu age impairments as a result of perisylvian abnormalities including ventr iculomegaly, sylvian fissure polymicrogyria, and incomplete sylvian fissure/insu la closure (Miller et al., 2007a,b,c). One particular defect, the failure of growth over t he insula and underdevelopment of the frontal, temporal, and parietal opercula may be cau sed by the lack of paternally expressed genes relating to cortical development. I nsular malfunction is noted in PWS individuals since they have poorly functioning pain perception and autonomic control (Goldstone, 2004). This may be explained by incompl ete insular closure leading to differences in white matter connectivity between th e cortex and the insula (Miller et al., 2007a). Miller et al. (2007a) hypothesized that the aberrant appetite in this condition resulted in irregular reward processing of food sti muli in brain pathways involving the HYP, frontal cortex, insula, and limbic/paralimbic areas. Post-mortem results have shown a decreased number of cells in the paraventri cular nucleus (PVN) (Swabb et al.,

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22 1995). This is crucial evidence for explaining why satiety is difficult to attain, since the PVN is the hunger center of the brain that controls appetite (Kalra and Kalra, 2004b). What is most interesting about this condition is th at PWS hyperphagia is not responsive to pharmaceutical treatment (Holland et al., 1993). Food Addiction Background: Causes and Symptoms Addiction is classically defined as a chronic relap sing problem caused by various fundamental factors that encourage craving for cert ain substances, such as food, in order to obtain a state of heightened pleasure, ene rgy, or excitement (Tartar et al., 1998). An example of this would be carbohydrate cra vers that have learned to consume high carbohydrate foods to improve their mood cause d by a drop in serotonin levels (Wurtman and Wurtman, 1995). A study by Spring et a l. (2008) showed convincing evidence of this phenomenon. Most food addiction is the result of loss of contro l, impulsive and/or compulsive behavior that results from emotional and environmen tal conditions, and a psychological dependence on food. Eating behaviors are similar to those of other addictions since both affect the levels of dopamine (DA) in the meso limbic dopaminergic system (Mogenson, 1982). DA D2 receptors have a high preva lence of Taq I A allele (Noble et al., 1994), meaning that this allele has been linke d with low levels of these receptors in obese individuals (Noble et al., 1991). These patie nts use food to raise their DA levels, even through positive reinforcement (Noble et al., 1997). It has been shown that the activity of DA in the brain can be related to abnor mal eating behavior (Jimerson et al., 1992). One study showed that the predisposition to food ad diction in offspring was caused by feeding rat mothers junk food consisting of fatty, sugary, and salty snacks

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23 during pregnancy and lactation. Rat offspring showe d increased weight gain BMI compared to controls, while their mothers displayed bingeing and overeating of junk food (Bayol et al., 2007). Thus, these findings may be applied to pregnant women’s diets in order for them to have healthy children wi th normal appetites and weight. Avena and colleagues (2004; 2005; 2008) found evide nce for sugar addiction in rats, since it was a good animal model to describe why certain people crave sweets or other delicious foods, and why it is difficult for them to wean themselves from such an eating behavior. Sugar has been found to be an addi ctive substance since it releases opioids and DA, which are characteristic of addicti on neurochemicals. The group classified sugar as an addictive substance because it follows the typical addiction pathway that consists of bingeing, withdrawal, crav ing, and cross-sensitization. The definitions of the components of this system are as follows: bingeing consists of “unusually large bouts of intake” (Colantuoni et al ., 2001); withdrawal is “indicated by signs of anxiety and behavioral depression” (Colant uoni et al., 2002); craving is “measured during sugar abstinence as enhanced by re sponding to sugar” (Avena et al., 2005); and cross-sensitization results “from sugar to drugs of abuse” (Avena et al., 2004). Bingeing is also defined as “escalation of i ntake with a high proportion of intake at one time, usually after a period of voluntary ab stinence or forced deprivation” (Avena et al., 2008). It consists of sensitization and tol erance, which are necessary for the initiation of any form of addiction (Koob and Le Mo al, 2005). Withdrawal has been known to be caused by alterations in the opioid sys tem (Colantuoni et al., 2002). It consists of two parts, in which DA decreases and ac etylcholine (Ach) is released from the nACC. When sugar was analyzed with regards to w ithdrawal symptoms, it was

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24 capable of producing DA, Ach, and opioids similar t o most narcotic substances (Avena et al., 2008). It is marked by anxiety (File et al. 2004) and depression (Avena et al., 2008). Craving usually happens after a prolonged pe riod of abstinence and is better defined by “increased efforts to obtain a substance of abuse or its associated cues as a result of dependence and abstinence” (Avena et al., 2008). Cross-sensitization is predominantly defined as “an increased locomotor re sponse to a different drug or substance” (Avena et al., 2008). All of these defin itions play a major role in helping define and classify food as an addictive substance in comparison to the criteria for drug dependence (Haddock and Dill, 2000). On the contrary, Haddock and Dill (2000) explained that the addictions model of obesity and eating disorders was flawed and could n ot be compared to that of drugs. They quoted various studies (Wilson, 1991; Parham, 1995; Wilson, 1999) to state that food was not a psychoactive substance. However, the studies used to support their arguments were outdated and the studies by Avena et al. (2004; 2005; 2008) showed that sugar itself can be classified as an addictive substance. Shared Anatomical Areas in the Brain The brain areas involved in satiety include the HYP orbitofrontal cortex (OFC), insula, inferotemporal cortex, nACC, ventromedial p refrontal cortex (vmPFC), limbic and paralimbic regions. They are supposed to be involve d in reward, arousal, motivation, memorization, and emotional responses to food and e ating (Tataranni and Delparigi, 2003). The OFC has been found to link food and rewa rding experiences (O’Doherty, 2004), thus if lesions are found in this region, th is may result in hyperphagia in PWS patients (Miller et al., 2006). In most obese subje cts, food is more palatable and enjoyable through the postcentral gyrus of the left and right parietal cortex (Wang et al.,

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25 2002). The nACC and ventral tegmental area (VTA) ar e known for reinforcing behavior such as feeding (Gold, 2003). It is a key area in t he reward system, which will be described later, since it encourages food-seeking b ehavior, learning incentive, motivation, satiety, and stimuli processing (Bassar eo and Di Chiara, 1999). In animal models, damage to the ventromedial hypoth alamus (VMH) leads to hyperphagia and increased appetite, while damage to the lateral hypothalamus (LHA) causes hypophagia and decreased appetite (Liu and G old, 2003). However, the dorsal striatum is most important in the motivation of con suming food. In a study by Volkow et al. (2002), when DA deficient mice were treated wit h DA in the dorsal striatum, feeding behavior was restored. These animals also chose mor e palatable food over that which was not. Mice that were not given DA in the nACC we re capable of initiating feeding behavior; those that were treated with DA in the nA CC chose more palatable foods over non-palatable ones, even though they had no motivat ion to eat enough food to maintain normal function and to stay alive. In another study DA agonists were used to increase the portion size of meals and length of feeding, wh ile long-term administration of DA increased body mass and feeding behavior (Schwartz et al., 2000). When methylphenidate was administered to human subjects as a DA agonist into the striatum, those that got the placebo did not show any increas e in appetite. Thus, the ventral striatum was localized as the site for appetite con trol (Wang et al., 2004). These studies are critical to localize the areas of the brain tha t can be identified and used to treat PWS and FA. Hypothalamic Regulation of Appetite/Afferent Hormon al Signaling The HYP is a key component to feeding behavior, thu s it is important to look into its regulation in more detail. There have been five individual areas identified in the HYP

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26 that regulate feeding behavior and metabolism (Kalr a et al., 1999). Medial areas of the HYP control food intake and energy homeostasis. The se regions obtain important information from referring organs and systems that are involved in nutrient and metabolite consumption and distribution, as well as involvement in hyperphagia and obesity (Berthoud, 2002). Ghrelin and leptin have b een known to target the HYP in regulating feeding behavior. Leptin activates its r eceptors so that NPY, orexin (ORX), endorphin, and alpha melanocyte-stimulating hormone ( -MSH) can decrease appetite stimulation (Kalra and Kalra, 2004b). Leptin has be en shown to have an important role in appetite control. Leptin can suppress ghrelin ex pression at the level of the NPY neurons (Bagnasco et al., 2002a,b). The roles of le ptin and ghrelin feedback on the appetite regulating network (ARN) are crucial for e nergy homeostasis and appetite (Kalra et al., 2003). If there is a drop in leptin levels in the blood, the ARN is stimulated to release orexigenic NPY, agouti related protein ( AgrP), and gamma-aminobutyric acid (GABA) along with an inhibition of anorexigenic -MSH (Kalra and Kalra, 2004b). This can be best summarized by Erlanson-Albertsson (2005 ). There has been interesting evidence explaining how leptin regulation is affected by factors predisposing an individual to obesity. I f an animal consumes too many energy-laden calories and is inactive, this promote s hyperleptinemia and fat accumulation in the body (Kalra et al., 1999). Lept in transport and production in the HYP is limited, and excess leptin in blood circulat ion is unable to control appetite (Bagnasco et al., 2002a,b; Kalra and Kalra, 2002). This may be a significant factor to look into regarding overweight individuals.

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27 Endocrinology of Obesity Defining the endocrinology behind obesity is crucia l to understanding FA and overeating behavior. Most childhood obesity is the result of genetic defects in leptin and its receptor, POMC, pro-hormone convertase-1, melan ocortin-4 receptor (MC4), and ghrelin genes (Farooqi and O’Rahilly, 2000). This i s why PWS is the first and foremost genetic model for obesity that demonstrates leptin resistance as one of the primary causes of obesity. In PWS cases, leptin levels are increased causing an inability to produce an anorexigenic effect (Proto et al., 2007) while numerous studies have indicated other hormonal and metabolic disorders th at may be associated with hyperphagia including impaired growth hormone (GH) secretion and low insulin production (Zipf et al., 1983; Goldstone et al., 20 01a,b; Cummings et al., 2002; Goldstone et al., 2002; Goldstone et al., 2004). Pe ople with FA who are obese may possibly have leptin resistance as well that leads to overeating (Liu and Gold, 2003). Hyperphagia is primarily due to continuous stimulat ion of NPY receptors (Kalra and Kalra, 1996). An imbalance of NPY signaling at a lo cal level (ARC and PVN) results in unregulated eating (Kalra and Kalra, 2004b). The ne urotransmitter GABA has also been known to enhance feeding behavior via its receptors or directly in the ARC, causing decreased melanocortin signaling to the PVN, which in turn results in hyperphagia (Cowley et al., 2001). It is possible that mutation s or disturbances of -MSH and other peptides involved in satiety can lead to hyperphagi a and obesity (Kalra et al.,1999). The key neurotransmitter of addiction, DA, has site -specific action regulating the intake of food; it reinforces the effects of food ( Salamone et al., 1997). DA is necessary to begin the meal process (Meguid et al., 2000). It acts upon the prefornical area, VMH, and ARC to reduce the consumption of food and preve nt hyperphagia, which in turn is

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28 affected by leptin, insulin, and other hormones (Ba skin et al., 1999). It may be inferred that disruptions in DA production and/or structure may predispose certain individuals to addictive behaviors and obesity. There has been some interesting work done by Solina s and Goldberg (2005) regarding how cannabinoid and opioid interactions a ffect motivational effects of food reinforcement, and increase appetite and food consu mption. Delta-9tetrahydrocannabinol (THC) and morphine increased t he reinforcement effects of food. Mu receptors are involved in the effects of THC and c annabinoid-1-receptors, and those are involved in the actions of morphine. Morphine a nd THC have orexigenic effects that promote appetite and food consumption. The reason f or this is that high amounts of endocannabinoids (Gonzalez et al., 1999; Howlett, 2 002) and high levels of opioid peptides are located in the HYP (Mansour et al., 19 87). The main concept is that THC and morphine not only responded to food stimuli, bu t also enhanced palatability of the food (Cooper, 2004). Abnormal hypothalamic function accounts for a varie ty of eating disorders, leading to hyperglycemia, which in turn causes othe r endocrinological problems (Liu and Gold, 2003). This may be explained by one dieta ry example where fructose was consumed. Fructose promotes insulin production but blocks its release (Sato et al., 1996). Insulin is known to inhibit feeding by incre asing leptin which in turn leads to weight gain (Saad et al., 1998). Hence, this would be a good model to explain why FA individuals are overweight and are addicted to high carbohydrate foods containing high fructose corn syrup. A great comparison of peptides involved in appetite control and

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29 how high fat and carbohydrate diets affect them is portrayed by Erlanson-Albertsson (2005). Orexigenic and Anorexigenic Pathways It is necessary to discuss the interactive pathways that regulate appetite and cravings. The ARN has appetite enhancing and reduci ng circuits that are located in the ARC-PVN axis of the HYP. It is affected by signalin g from the LHA and VMN (Kalra and Kalra, 2004b). These particular pathways have their components synthesized in the ARC and are targeted at the parvocellular PVN (pPVN ) and magnocellular PVN (mPVN), which may provide insight on the mechanisms of overweight subjects. The release of these neurochemicals is regulated primar ily by the VMN and LHA (Kalra et al., 1999). Kalra and colleagues (1999) showed that if there was a disruption between these two sites, then the affected individual would overeat and gain weight, as seen in PWS and FA. This suggests that the VMN is responsib le for inhibiting signals to the ARC. Certain areas in the LHA that express ORX or M CH increase NPY release, thereby stimulating appetite. Thus, if there is non -stop stimulation of NPY receptors, then the satiety signal to the HYP is inhibited res ulting in continuous eating (Kalra et al., 1999), which is a typical symptom in overweight ind ividuals. What is interesting is that despite this happening, there is no known receptor down-regulation for NPY (Kalra and Kalra, 1996). It was shown that during the absence or decrease of food intake, NPY levels increased in the ARC in order to stimulate a ppetite (Kalra et al., 1999). The neurotransmitter GABA has also been known to st imulate appetite by its receptor activation or by administering NPY. GABA b y itself can decrease melanocortin signaling to the PVN in order to stimulate appetite (Cowley et al., 2001). Another orexigenic peptide, AgrP, enhances eating by antago nizing MC4, which are responsible

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30 for curbing appetite (Kalra et al., 1999). Ghrelin has been shown to increase appetite by increasing NPY signaling (Kalra et al., 2003). LHA neurons express ORX and MCH, and ARC neurons coexpress NPY; AgrP and GABA are the ke y components of the hypothalamic orexigenic pathway (Kalra and Kalra, 2 004b). Manipulation of this pathway can provide novel insight for the treatment of obesity in general. Anorexigenic pathways are responsible for controlli ng the inhibition of appetite. The melanocortin pathway’s crucial component is the ARC-PVN axis where POMC neurons coexpress -MSH, while cocaineand amphetamine-regulating tra nscript (CART) acts upon PVN to curb appetite (Kalra and Ka lra, 2004b). Inhibition of feeding is regulated by MC4 receptors that act on -MSH. During the hunger state, POMC gene expression is decreased, thus -MSH release decreases as well (Kalra and Kalra, 2004b). Another anorexigenic pathway consists of co rticotropin releasing hormone (CRH) neurons in the PVN, which release CRH due to stress to inhibit NPY-induced food intake. Feeding regulation by anorexigenic neu rochemical signals consists of the links between NPY and POMC, and between NPY and CRH (Morley, 1987). Food Cues: Internal and External Appetite Triggers Food cues and motivation are crucial aspects of foo d intake. Most environmental cues result from Pavlovian conditioning that can ov errule satiety and enhance food ingestion in the network associated with feeding in cluding the AMY, LHA, and mPFC. Other motivational cues paired with eating during t he hunger state can easily override satiety and promote eating in sated rats, which res ults from signals via the forebrain and LHA (Petrovich and Gallagher, 2007). In the developed and developing world, people are c onstantly surrounded by food cues to enhance consumption of not necessarily healthy foods. There are diverse

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31 examples of how food cues affect a specific group o f individuals. This section will focus on how food cues affect those with obesity as compa red to normal humans based on the animal model. Certain brain circuits and networks are responsible for cue-induced eating, appetite induction, as well as specific food cravin gs. Kalra and Kalra (2004a) provide a thorough overview of pathways regulating appetite a nd cravings. The ability of foodrelated cues and a food-associated environment to i nduce eating in healthy humans can shed light on why PWS individuals overeat and becom e overweight. In the animal model, the brain regions consisting of the basolate ral amygdala (BLA), mPFC, and LHA act as a network to regulate eating by learned, mot ivational cues. In neuroimaging studies, the AMY has been shown to be pivotal in it s role in cue-enhanced eating (Arana et al., 2003); it has been suggested that th e AMY is involved in appetite activation and maintenance in humans (Tataranni et al., 2003; Gottfried et al., 2003; Killgore et al., 2003; Kringelbach et al., 2003; Hi nton et al., 2004). The OFC is also involved in food-related cues (Arana et al., 2003). The mPFC is crucial for eating due to environmental cue pressure (O'Doherty, 2004). The A MY shares tight connections with the HYP (Petrovich et al., 2001). Activations of th e AMY and medial orbitofrontal cortical area (mOFC) occur when food-deprived individuals ar e shown food items relative to non-food items, and greater activations are seen wh en food items are viewed (Arana et al., 2003; Hinton et al., 2004). On the other hand, normal eating signaling results from a response to decreased energy, but it can be triggered from environmental or learned cues, which can alter the motivation for food consumption. Petrovich et al. ( 2001) presented findings associated

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32 with major connections formed between the BLA and L HA. These are responsible for processing learned cues in order to forego the sati ety signal and promote eating in sated rats. The BLA shares anatomical connections w ith the HYP in order to control feeding behavior (Elmquist et al., 1999; DeFalco et al., 2001; Petrovich et al., 2001). A portion of the BLA, which originates in the basolat eral nucleus, directly innervates the LHA (Petrovich et al., 2001). It sends vital projec tions to the LHA (Petrovich et al., 2001), which forms part of the feeding circuit asso ciated with the initiation of feeding (Elmquist et al., 1999). The BLA–LHA system is cruc ial for allowing learned cues to override satiety signals and stimulate eating durin g satiation. It was shown that the BLA–LHA system is specifically associated with cont rolling eating via learned signals, because it does not regulate baseline eating or the rate at which rats gain weight when fed ad libitum This was primarily due to an associative process in which food ingestion was directed by a cue paired previously with food b ut not an unpaired one (Petrovich and Gallagher, 2007). When research animals are presented with food cues, they consume more food despite being sated. Visual cues for foods that hav e a higher incentive value produce greater activation in the AMY than foods that were recently eaten to satisfy hunger signals (Gottfried et al., 2003). In general, crave d or highly palatable foods will activate the AMY regardless of sated state (Hinton et al., 2 004). In a recent study by Petrovich et al. (2007), cellu lar activation markers in the vmPFC neurons were activated following exposure to a newly-conditioned cue that stimulated eating in sated rats. When neurotoxic le sions were created in the vmPFC, this caused impaired food consumption as a result o f conditioned motivational cues.

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33 Thus, the vmPFC has a significant role in appetite influenced by motivational cues. Brain lesions did not affect eating in the pretest baseline sessions or the rate at which rats gained weight when fed freely. Rats with selec tive bilateral neurotoxic lesions in the central nucleus showed enhanced feeding when a cond itioned stimulus was applied (Petrovich et al., 2007). Lesions in the BLA, basom edial (BMA), and lateral (LA) nuclei of the AMY as well as the LHA result in decreased f ood consumption and nonresponsiveness to appetite stimulation cues (Petrov ich et al., 2007). This part of the AMY controls food consumption via extrinsic cues (P etrovich et al., 2001). Given its important role in goal-oriented behavior (O’Doherty 2004), the omPFC could play a pivotal role in regulating the impulse to eat in re sponse to highly appetitive cues in PWS and overweight subjects. From a neurohormonal perspective, glutamate is beli eved to be the neurotransmitter responsible for transmitting infor mation between the areas depicted above, although the exact mechanism is still not un derstood (Swanson and Petrovich, 1998). Glutamatergic mechanisms within the LHA have been shown to promote feeding in sated rats (Duva et al., 2001). It is then plaus ible that potential feeding mechanisms involve direct glutamatergic connections from the B LA to LHA, although the exact LHA neurons involved in this process remain unidentifie d. It may be safe to assume that BLA outputs could influence LHA subsystems required for feeding initiation. Groups of LHA neurons express two recently discovered neuropeptid es, MCH and orexin, which are regulated by the hunger–satiety state and are linke d to initiation of feeding (Elmquist et al., 1999). Leptin and NPY have an opposite effect on food ingestion. Leptin inhibits feeding while NPY promotes eating (Schwartz et al., 2000). Learned cues can utilize

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34 BLA to activate NPY or inhibit leptin. The BLA-LHA junction must be intact in order to initiate food cue-related eating (Kelley, 2004). Hu nger caused by food cues is an adaptive mechanism for survival, but at the same ti me, learned cues can serve as a harmful force to promote overindulgence in food des pite satiety. These particular learned cues can overcome specific satiety signals in order to promote continued eating (De Castro, 1997). This may be the situation in PWS and overweight patients. Metabolic factors and non-homeostatic signals contr ol motivational eating. Despite no clear definition of food cravings, speci fically in animal models (Weingarten and Elston, 1990), cravings for food in humans can be elicited by food cues and are often associated with hedonic overeating (Jansen, 1 998; Fedoroff et al., 2003; Sobik et al., 2005; Tiggemann and Kemps, 2005). Regions of t he PFC may also participate in brain networks involved in cue-induced drug craving s. Other regions overlapping the vmPFC are also activated by chocolateand nicotine -associated contextual cues in rats (Schroeder et al., 2001). Additional studies are ne eded to determine if this rat model parallels the role of the mOFC in human appetite an d cravings. The lateral OFC is not needed for food consumption, but the ventral areas within the rat vmPFC could represent a functional counterpart for the mOFC in humans (Ongr and Price, 2000). DA also plays a critical role in food consumption s timulated by unpredictable cues (Roitman et al., 2001). DA efflux within the vmPFC resulting from signal-induced satiety was correlated with decreased consumption of high c aloric, sweet and fatty foods; this may be the case in overweight individuals. Human OF C activation decreased in response to an olfactory cue of food eaten to satie ty but not to an odor of uneaten food

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35 (O’Doherty et al., 2000). This may be a key point a s to why food addicted overweight individuals continue to overeat despite satiety. Neophobia, a species-specific adaptive response to novel food, suggests a role for the intact vmPFC in regulation of unlearned ada ptive feeding responses, as seen in the Petrovich group’s (2007) study which showed the effects of lesions in this region. Rats with neurotoxic lesions of the prelimbic area (PL) were given a choice between familiar lab chow and a novel, preferred food in an unfamiliar, open field environment. Rats with lesions had an increased tendency to cons ume the novel food more and tended to eat the familiar food less as compared to controls. The amount consumed of the two foods was similar for rats with lesions tha n those without lesions. Food neophobia in rats was increased (Petrovich et al., 2007) and decreased with mPFC lesions. Lesions were located in the PL and infrali mbic area (ILA) (Petrovich et al., 2007), along with damage to the adjacent ventral PL and mOFC regions. This may suggest different roles of the sub-regions within t he vmPFC in food neophobia. The vmPFC seems to be crucial in controlling eating imp ulses based on environmental cues. A dysfunctional vmPFC could mechanistically depict feeding behavior in PWS or overweight humans relevant to overeating, appetite, cues and cravings. Explicit food-associated cues during the hunger sta te will enhance food consumption despite subsequent satiety. Other studi es link the nACC and dopaminergic brain systems to motivation and food reward (for re ference reviews see Cardinal et al., 2002; Berridge and Robinson, 2003; Phillips et al., 2003; Kelley, 2004; Wise, 2004). Currently, there are no valid studies showing wheth er DA is necessary for cuepotentiated eating. Thus, individualized sub-circui ts may be integrated into a vast,

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36 combined system depending on the underlying process es controlling motivation to consume food or to seek out highly desired foods vi a external cue activation. More research is needed to elucidate the precise mechani sms of food consumption through different aspects of learning (Petrovich et al., 20 07), despite a lack of a definition of food craving in animal models (Weingarten and Elston, 19 90). Even human cravings have been defined with uncertainty especially in overwei ght individuals. Craving for food can be induced by exposure to food cues (Tiggemann and Kemps, 2005), and cue-elicited craving is associated with binge eating (for review s see Jansen, 1998; Sobik et al., 2005). Cue-induced eating could be considered bingi ng, since Petrovich et al. (2007) showed that sated rats consume more food pellets in a short period of time. A recent human study (Fedoroff, 2003) showed that in diet-re stricted eaters, food cues elicited specific cravings for the cued food, as opposed to a general desire to partake in noncraved food. As the craving for the desired food in creased, the restricted dieters consumed more of the cued food (Fedoroff, 2003). Brain systems and mechanisms that dictate food rewa rd learning were correlated with drug addiction (Cardinal and Everitt, 2004; Vo lkow and Wise, 2005). Contextual cues, used to stimulate eating, are also very power ful cues in drug addiction craving and relapse (Crombag and Shaham, 2002). Koob and Le Moal (2005) argued that drug addiction is the ‘dark side’ of the reward neurocir cuitry in the form of impulsive to compulsive behavior much like that displayed in PWS Wang et al. (2004) conceptually reviewed the similarity between obesity and drug ad diction using neuroimaging techniques.

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37 The main role of the external feeding environment i n food intake depicted by an animal model might be relevant to PWS and overweigh t human eating. In fact, the environment in which food is consumed has been chan ging over the past 30 years in the United States. Increasingly disproportional foo d portions are served and eaten in distinct environments such as restaurants and fast food places (Nielsen et al., 2002). Advertising on television further elicits food cues encouraging even normal weight children and adults to seek out food despite lack o f hunger signals. On the other hand, external food cues can be depicted in a much simple r fashion than the internal cues described above. In PWS patients, obsession and pre occupation with food, lack of satiation, and incessant food seeking are typical b ehaviors as compared to normal obese humans (Holm et al., 1993; Dimitropoulos et a l., 2000; Lindgren et al., 2000; Ogura et al., 2008). PWS adults show preference for sweet or high carbohydrate foods over any other type of food. This is sometimes the case in normal obese individuals (Ogura et al., 2008; Singh et al., 2008). PWS patie nts will often eat the most desirable foods first, such as sweet, high caloric foods, and the least preferred foods last. Oftentimes, this is a ritualistic procedure in whic h the PWS-afflicted individual will gather the food and line it up in order of preference and ingest it sequentially (Singh et al., 2008). Since PWS cases are often highly affected by visual cues, even more so than normal overweight adults, environmental cues are of greater pertinence. For instance, passing by a bakery or restaurant, or even seeing s weet or highly palatable foods on television, will cause an enormous increase in crav ing and appetite despite satiety in PWS individuals as compared to normal overweight on es. Oftentimes, PWS patients will have tantrums and aberrant behavior after seeing or smelling delicious, inviting food

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38 (Singh et al., 2008), which is highly uncommon in n on-PWS people. It would almost appear that in PWS, food cues (visual) have a very high emotional attachment and significance that leads to bingeing episodes (Simmo ns et al., 2005). The key to these disorders is that anything that al ters the satiety and hunger signaling may involve binging on food. In FA, the a ct of eating itself brings pleasure but in PWS, eating is pleasureless (Liu and Gold, 2003) Overall, much progress has been done in order to fully understand the anatomical, f unctional, and neurohormonal mechanisms of PWS and FA which would help explain t he ultimate causes of obesity in the general population. Food–induced hyperphagia us es the hypothalamic neural network and afferent signaling to control appetite, but it still does not define if this mechanism is due to low energy stores. New therapeu tic options, especially ACU, can then be utilized to curb food cravings and help con trol weight in human individuals. Reward System Hypothesis In drug-related addictions, it has been shown that the ventral striatum and midbrain were associated with immediate rewards and the HIPP responded to reward consequences. The globus pallidus, thalamus, and su bgenual cingulate were associated with immediate rewards, while the caudat e, insula, and vPFC responded to reward consequences (Elliott et al., 2000). The mes olimbic reward system is a common pathway that substances of abuse, such as food and drugs, follow in order to reinforce craving behavior (Tartar et al., 1998). Reward proc essing is linked to addiction and is processed only if it can promote the addicted indiv idual in pursuit of the addiction. This then explains why addicts are more prone to seeking rewards, such as food, rather than facing the consequences of the reward behavior (Jor anby et al., 2005). The fundamental idea of the reward system lies in the f act that there must be an emotional

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39 state connected with the addiction. The stronger th e emotional link, the stronger the addiction. There exist a couple of primary circuits for the reward system. The first one involves a reciprocal connection between the prefro ntal areas of the brain and the AMY. The second is the limbic system that links the AMY with the HYP and septal nuclei. The Papez limbic system also joins the HYP with the HIP P and thalamus (Joranby et al., 2005). The reward system hypothesis states that app etizing food and addictive behaviors compete for reward regions (such as the n ACC) in the brain. The act of overeating and obesity can lead to decreasing food reward and addiction (Kleiner et al., 2004). Anticipating and ingesting appetizing food c auses an increase in DA levels in the nACC (Hernandez and Hoebel, 1988). Obesity is a “re ward deficiency syndrome” (Blum et al., 1996a,b) since DA D2 receptors are mediator s of reinforcement and compulsiveness, and obese subjects were found to ha ve lower levels of these receptors in the striatum (Wang et al., 2001). The common pat hway for addiction involves the mesolimbic frontocortical dopamine (MFD) system, wh ich is a reward pathway that controls eating behavior. Addictive behaviors relea se DA in the reward pathway causing almost immediate positive reinforcement (Hodgkins e t al., 2003). Increased activation in the somatic parietal areas in FA individuals sugges ts that enhanced activity in these regions involves sensory processing of food, which may make food even more rewarding (Wang et al., 2003). Morris (2001) showed that the state of hunger can be influential on the memory of food-related stimuli i n fasting individuals. In the study by Joranby et al. (2005), they found that the activity of the brain was regulated depending on the stimulus it received. The right anterior OFC had a variable response to all stimuli despite hunger, while the right posterior OFC had d ifferent responses only with food-

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40 related stimuli during hunger. The posterior area w as associated with general rewards, while the anterior part was associated with abstrac t and goal-oriented rewards. As mentioned earlier, studies done in sugar-depende nt rats would help define the reward hypothesis (Avena et al., 2005; Rada et al., 2005; Avena et al., 2008; Bayol et al., 2007). It is well-known that certain drugs of abuse release more DA in the nACC; Rada et al. (2005) found that it was the same in ra ts eating highly palatable food, especially sugar. They found that sugar-dependent r ats had a delayed Ach response for satiety, imbibed more sugar, and released more DA t han controls. It was questionable if sugar had a similar mechanism to opioids on satiati on and the lowering of Ach in the nACC. It was found that sugar indeed promoted satie ty causing the release of Ach (Rada et al., 2005). Just one event of showing pala table food is enough to increase DA, which has been shown to be correlated to taste reco gnition (Di Chiara, 2002). Rats that were known to consume a large quantity of sugar had a delayed release of Ach, resulting in overeating of sugar (Rada et al., 2005 ). This may explain why FA individuals may be addicted to certain palatable foods that cau se a delayed, prolonged increase in Ach levels. Neuroimaging Studies Neuroimaging studies, such as done by our lab, have shown that aberrant eating behaviors and obesity have altered the brain chemis try (Liu and Gold, 2003) as well as the anatomy (Miller et al., 2007a,b,c) of affected PWS and FA individuals. In an fMRI study by Shapira et al. (2005), PWS patients had de layed blood oxygen level dependent (BOLD) responses in the HYP during rest and after G lu ingestion in the frontal cortex after viewing food pictures. This was probably due to defects in the HYP resulting in abnormal reward processing that led to calorie over loading. It is well-known in fMRI that

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41 the frontal cortex is involved in linking food and other rewarding objects with hedonism (O’Doherty, 2004). It was found that obese individu als ate more food than controls when food cues were present during a food-directed reinf orcement task (Johnson, 1974). Wang et al. (2004) found that when subjects viewed delicious food, the anterior insula and right OFC, brain regions that are involved in t he DA system, were activated. Another interesting study looked at dissociated res ponses in the AMY and OFC using bottom-up and top-down approaches (Wright et al., 2008). The bottom-up phenomena is stimulus-driven and the top-down one i s task-driven, meaning that emotional evaluation pertaining to obesity is contr olled by different neural systems as depicted by fMRI. Wright and colleagues (2008) show ed that the AMY is associated with bottom-up processing, and the OFC and vmPFC is linked with the top-down approach. Regarding obesity, this can explain how e motions about food are associated with the OFC, and how satiety and the reward hypoth esis are correlated with the vmPFC and ACC. This information is useful in what a reas of the brain need to be targeted for future treatment and intervention. Hyperphagia is thought to be a contributing factor to increased caloric intake and hence obesity. Interestingly, there have been many unverified animal and human models that have sought to mechanistically identify the sole causes of aberrant appetite control leading to weight gain. PWS is a biological model for hyperphagia and the reward system utilized to explain human obesity usi ng fMRI. Neuroimaging would be the most logical tool in precisely locating the bra in regions responsible for controlling appetite and for being the reward centers of food a ddiction (Tataranni and DelParigi, 2003). In past studies, using food-related pictures or other visual means to elicit brain

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42 responses has been a standard method of determining valid mechanisms that delineate the path to obesity (Jansen, 1998; Ernst and Epstei n, 2002; Killgore et al., 2003; Hawk et al., 2004; Kemps et al., 2004; Simmons et al., 2 005; Sobik et al., 2005; Beaver et al., 2006; Cornier et al., 2007; Rolls and McCabe, 2007; Stoeckel et al., 2008). Hence, the fMRI-supported hypothesis that PWS is a naturally o ccurring human model for food addiction or loss of control of eating or absence o f satiety would be crucial for further ACU studies. In the end, what remains is how logica l and effective past, present, and future research can aid and treat abnormal eating b ehavior and brain responses to internal and external food cues in individuals affl icted with obesity. Treatments for PWS and FA With respect to the obesity issue, there are two co mmon types of non-medicinal methods to decreasing body weight and/or improving the health condition of the individual. The first one is the undieting approach which discourages the use of food restriction or dieting due to its ineffectiveness a nd possible health risks (Foster, 2001). The second type is isolated dieting in which one co nsumes less of a particular type of food or food group such as seen in the Adkins diet where carbohydrates are almost completely eliminated from the diet. A restriction diet can also be combined with supplements or specific weight-reducing herbs or ev en acupuncture (Foster, 2001). Besides altering the endocrinological makeup of ove rweight individuals via drug therapies, alternative and complementary approaches could play a major role in the intervention and possible prevention of obesity. FA is easier to prevent than PWS. Initially, a comprehensive medical and psychologica l evaluation should be done on each patient to determine the root of the problem. With FA, decreasing access to highly palatable and addicting foods is necessary. On the other hand, restriction to all foods

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43 and small inanimate objects for PWS patients is a n ecessity. Management includes 24 hour or constant supervision, planned physical acti vity, a strict diet ( 1200 cal/day) divided into structured, portioned meals at set tim es, and a static, predictable way of life (Benarroch et al., 2007). Encouraging both afflicte d groups to exercise or do other enjoyable activities will discourage them from thei r usual eating behaviors, as well as maintaining a highly controlled eating environment and food regimen with strict, consistent and reinforced rules. Treatment of PWS a nd FA is a group effort that requires major lifestyle changes and dedication. One novel treatment for obesity, FA, and PWS is acu puncture. The TCM explanation given for obesity or increased appetite for the affected individuals was described as having excess heat in the gastrointest inal system, a deficiency of the energy known as Qi in the spleen and stomach, or si mply a generalized deficiency in Qi. That was one reason why we chose our acupoints.One example of a TCM formula for treating obesity consists of the acupoints Neiguan (SP 6), Fenglong (ST40), Liangmen (ST 21), Guanyuan (R 4), Zusanli (ST 36), Tianshu ( ST 25), and Quchi (LI 11) (Li, 1999). We used ST36 and SP 9, which were easily acc essible in the scanner. Another form of acupuncture, auricular acupuncture, has had some good results in promoting weight loss (Stux and Pomeranz, 1987). Some common auricular points for us to consider using in future studies are the hunger, st omach, and shenmen points that are indicated to promote satiety and cause sedation/ana lgesia respectively (Huang et al., 1996). These points are often stimulated using pres s needles, staples, or beads (Dung, 1986). Acupuncture may be utilized particularly in FA subjects to improve their mood, alleviate stress, and lessen depression in order to control appetite (Akil et al., 1984).

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44 This can be achieved by the release of various neur ochemicals during acupuncture treatment (Hans and Terenius, 1982) that could poss ibly affect our physiological results. In one example, Sun and Xu (1993) utilized traditio nal ear and body acupuncture methods over a 3 month period. Interestingly, the c ontrols only received the herb Oenothera erythrosepala (evening primrose oil), while the treatment group showed greater weight loss ( P <0.01). Auricular EAS has also been a fascinating m ethod for weight loss, such as in the study by Shafshak (1995 ). He found that when auricular electroacupuncture was given to overweight women 5 times a week for 3 weeks along with a low-calorie diet, the weight loss was signif icant ( P <0.05). In another randomized study by Steiner et al. (1983), there were 4 experi mental groups consisting of real acupuncture, sham acupuncture, eating behavior alte ration only, and controls. The results concluded that weight loss was best using r eal auricular and body acupuncture and/or behavior modification as compared to sham ac upuncture and controls ( P <0.05). Other recent studies have all shown great success i n using alternative modalities for weight loss (Hsu et al., 2005a,b; Cabioglu et al., 2006; Lee et al., 2006). We may implement such methods in future studies. Experimental treatments in animals may have some pr actical application in treatment and prevention of obesity. It has been sh own in mice that targeting the DA system with DA agonists promote decreased appetite and food consumption (Scislowski et at., 1999). There is a possibility such drugs ca n be marketed for use in human medicine and our studies. Another suggested experimental treatment is the aid of central leptin gene therapy (Bagnasco et al., 2002a). In a study by Kal ra and Kalra (2002), an injection of

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45 recombinant adeno-associated virus vector encoding leptin into the HYP of prepubertal and adult rats resulted in weight gain and suppress ed diet-induced obesity. The explanation was that it promoted loss of fatty depo sits caused by a decrease in NPY and increase in MCH and thermogenesis. This is a no vel approach that may not be suitable for humans at this point. Disrupting NPYer gic signaling at multiple loci without affecting normal hypothalamic function would be ide al, but more research needs to be done in this area (Kalra and Kalra, 2004a). Another experimental method is based on the theory that Ach inhibits feeding via M1 receptors if a muscarinic agonist, arecholine, i s injected into the nACC. This can be reversed by using an M1 antagonist pirenzapine (Rad a and Hoebel., 2000). It would be interesting to determine if arecholine would be a s afe and effective method to prevent hyperphagia in overweight patients. However, there is no safe and effective pharmaceutical agent to specifically treat hyperpha gia except possibly fenfluramine, which has been known to have cardiopulmonary side e ffects (Selikowitz et al., 1990). Some studies showed that taste aversion was a very useful therapy in which Ach levels were increased while decreasing DA levels (Mark et al., 1995), using D-fenfluramine with phentermine to control appetite using a simila r mechanism (Rada and Hoebel, 2000). Others have found that baclofen, a GABA-B ag onist, is useful in overeating fatty foods (Buda-Levin et al., 2005). Current methods of treatment for PWS include supple mentation with GH before 18 months of age in order to decrease body fat and increase lean muscle mass (Carrel et al., 2004). Other treatments utilize naloxone (a n opioid antagonist) to block the opioid system and rimonabant (a CB1 receptor antagonist) t o block the cannabinoid system;

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46 these systems have been shown to reinforce feeding behavior, and when used together, they act synergistically to treat obesity (Berry and Mechoulam, 2002). There are other possible cannabinoid agonists and antagon ists or acupoints with those properties that may potentially be used to treat FA and other eating disorders (Solinas and Goldberg, 2005). Both PWS and FA have similar morbidities associated with them such as developing obesity, type 2 diabetes, orthopedic abn ormalities, sleep apnea, gallbladder stones, hepatic lipidosis, insulin resistance, dysm etabolic syndrome, renal disease, cardiovascular disease, certain cancers, depression anxiety disorders, sleep disturbances, and a slew of others (Miller et al., 2006). Therefore, PWS is a good genetic model to study these various obesity-relate d diseases and help us with our ACU studies in the overweight population.

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47 CHAPTER 2 AIMS, MATERIALS AND METHODS Aims To compare the acute effects on Glu levels and CBT via fMRI in overweight Chinese males using ACU versus min SHAM protocols. To determine areas of functional connectivity in the brain following ACU and min SHAM acupuncture. To determine how ACU and min SHAM acupuncture affect t he hunger state in fooddeprived human subjects. Rationale We hypothesize that Glu levels will decrease and CB T will increase during ACU treatment and decrease post-treatment due to increa sed insulin production as well as other hormonal interplay via vagal stimulation due to central stimulation of the thermoregulatory center in the HYP affecting BMR. W e expect to see activation due to acupuncture effects in the HYP (VMH/LHA), insula, m PFC and ventral striatal regions, nACC, caudate nucleus (CN), putamen, and globus pal lidus), brain stem, PVN, ARC, cerebral cortex, subcortical structures (AMY, HIPP, cerebellum, and thalamus), inferior parietal lobes, DLPFC/VMPFC, insula, and ventral ba sal ganglia. The VMH, LHA and ventral striatal regions are known to regulate glyc ometabolism (Morton et al. 2006), while the HYP and brainstem are CNS centers that af fect gastric function (Wu et al., 1999). The cerebral cortex, PVN, ARC, and subcortic al brain structures are involved in cognitive function (Fuster, 2002). The inferior par ietal lobes, DLPFC, VMPFC, insula and ventral basal ganglia are involved in satiety ( see review by Ahima and Antwi, 2008). We expect to see hunger decrease after ACU acupunct ure only (rather than min SHAM)

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48 due to neurophysiological effects of acupuncture wh ich are used to suppress hypothalamic activity. Subjects The study was performed on 19 right-handed voluntee r Chinese males aged 2145 years (10 for ACU treatment and 9 for min SHAM) who had no history of major neurologic and psychiatric disease. All subjects we re acupuncture nave and gave written informed consent as approved by West China University of Medical Science. All subjects were in accordance with the Declaration of Helsinki. All patients were free to withdraw at any time from the study without obligat ion. Table 2-1 shows inclusion and exclusion criteria used to recruit subjects. Table 2-1. Inclusion and exclusion criteria used to select subjects for this experiment. Inclusion Criteria Exclusion Criteria Right-handed adult (age 21-45 years) Chinese males Left-handed non-Chinese males or females (age <21 and >45 years) BMI >18 and <30 Normal weight/BMI Non-smoker Smokers Non-dieter (regular diet and exercise program in past 3 months) Not on prescription or non-prescription medication especially anti-depressants and appetite suppressants On a weight loss program in past 3 months Taking antidepressants or appetite suppressing drugs (i.e. loperamide) Never experienced acupuncture (acupuncture nave) Had major acupuncture treatment in the past, especially recently Healthy (no neurological and endocrine problems) Not claustrophobic Neurological or endocrine disorders Claustrophobic 12 hour fast prior to experiment Ate within 12 hour s of the experiment

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49 Experimental Design Subjects were recruited and pre-screened based on a standard questionnaire. Subjects were randomly assigned by a computer progr am to groups A and B (the acupuncturist is the only non-blinded individual in the research group). Group A received standard ACU treatment. Group B was treate d with min SHAM. Session I Experiment I (which included group A) and II (which included group B) consisted of the following protocols. Physiological Measurements Height (cm) and weight (kg) were measured for each subject in order to calculate the body mass index (BMI). A brief chest and heart auscultation was performed for each patient. Prior to scan, initial CBT was measured su blingually with an Omron electronic thermometer (MC-142L). Initial Glu was taken from t he left index finger and was measured via OneTouch Ultra™ Blood Glucose Monitoring System (Lifescan; Johnson & Johnson Company). Instrument used glucose oxide ( >0.8 IU) and buffer (0.05 mg). Range was 20-600 mg/dL or 1.1-33.3 mmol/L. Accuracy was a slope of 0.986, yintercept = -5.5 mg/dL, and CC = 0.984. Precision w as 1.6-3.2% for blood and 2.4-4.4% for the control. Blood pressure was measured via Om ron electronic blood pressure monitor (HEM-645). Sensitivity was 4 mmHg (5% acc uracy) with a range of 0-299 mmHg. The hunger survey was then conducted asking t he patient to evaluate his hunger on a standard scale from 0 (no hunger) to 10 (starvation). After the 21 min scan, CBT, Glu (from right index f inger), and a hunger survey were conducted. The patient was asked to evaluate t he Deqi sensations he felt during the treatment. A standard scale (0 being no sensati on and 10 being most intense sensation felt) was used to evaluate Deqi sensation s listed. When the anatomic scan

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50 and post-scan were done, the final CBT, Glu (left m iddle finger), and a hunger survey were conducted. Subjects were asked if they think t hey received real or sham acupuncture. Treatment Methods After a 5 min prescan, the certified acupuncturist set up for either ACU or min SHAM procedure, depending on random patient assignm ent. Scan began when needles were in at time 0 min. For ACU, 4 acupoints were us ed bilaterally, ST 36 and SP 9. ST 36 is 3 cun below ST 35 (Dubi), which is in the dep ression lateral to the patellar ligament on the lower border of the patella when th e knee is flexed, and 1 cun lateral to the anterior crest of the tibia. When the knee is f lexed, SP 9 is located along the posterior border of the upper tibia. For min SHAM, the SHAM acupoints were located 2 cun lateral and dorsal to ST 36 and 2 cun medial to SP 9 on the same plane bilaterally. Acupuncturist used paramagnetic (0.18 mm x 40mm) ne edles for both ACU and min SHAM. For ACU, after a 1 min pause, the acupuncturist ins erted needles vertically to a depth of 2-3 cm and rotated needles in a “tonifying and reducing” technique clockwise and counterclockwise at a rate of 60 times per minu te or 2 Hz in an alternating bilateral diagonal manner at 30 sec intervals for a total of 2 min. The subject was allowed to raise his right index finger if the Deqi sensations were painful. The lower legs were covered to mask the treatment choice. Scan continue d for 21 min. For min SHAM, after a 1 min pause, the acupuncturis t inserted needles to a depth of 2-3 cm and immediately removed them, but p retended to rotate the needles as described for the ACU procedure. The lower legs wer e covered to mask the treatment choice. After treatments, a 7 min anatomic scan and a 9 min post-scan were conducted.

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51 fMRI Parameters The functional MRI experiment was performed using a 3.0 Tesla Signa (GE) MR with a standard head coil. The images covered the e ntire brain and were parallel to the AC-PC line. Functional images were acquired with a single-shot gradient–recalled echo planar imaging (EPI) sequence (TR/TE: 2000ms/30ms, field of view (FOV): 240mm240mm, matrix size: 6464, flip angle: 90, i n-plane resolution: 3.75mm3.75mm, slice thickness: 5mm thick with no g aps, 43 sagittal slices). A set of T1-weighted high-resolution structural images was c ollected (TR/TE: 5.7ms/2.2 ms, FOV: 256mm256 mm, matrix size: 256256, flip angle : 7, in-plane resolution: 1mm x 1mm, slice thickness: 1mm with no gaps). Preprocessing of Data and Analysis The first 5 time points were discarded to avoid the instability of the initial MRI signal. The fMRI runs were intensity-scaled to yiel d a whole brain mode value of 1,000. Data sets were preprocessed using SPM5 (www.fil.ion .ucl.ac.uk/spm). Images were realigned to the first image. If translation and ro tation was > 1 mm in any direction or > 1 degree, the subject was excluded. The images were t hen normalized to a Montreal Neurological Institute (MNI) template and re-sample d to 3mm x 3mm x 3mm. Resting data used a band-pass filter of 0.01-0.1 Hz. Finall y, the images were smoothed using 6mm x 6mm x 6mm. For regions of interest (ROIs), the HYP and AMY wer e chosen initially. The first 0.5 min of data were omitted and 8.5 min of the ACU data were extracted. The HYP (7 grey voxels) and AMY masks (2 grey voxels) were sel ected using WFU (Wake Forest University) Atlas software. The data was smoothed with a 12-mm full width at ha lf maximum (FWHM) Gaussian kernel for the discrete cos ine transform (DCT) analysis.

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52 The DCT analysis was followed by steps depicted in Liu et al. (2009). The discrete cosine bias set contained 60 regressors spanning th e frequency of 0–0.1Hz. Statistical parametrical maps were constructed by computing F-c ontrasts, which compared the effect of signal fluctuations in the range of 0.01– 0.1Hz. Statistical parametrical maps were created under the threshold P <0.005 (corrected for multiple comparisons) at the first level. The final overlapping mask was created by multiplying the binary values of the individual mask in each group. Finally, the con junction analysis of the two group masks was applied to detect inter-group similaritie s of spatial patterns, which was adopted as the ROI for the functional connectivity analysis. The ROI was applied for further functional connectivity analysis. First, th e data were processed with a bandpass filter of 0.01–0.1 Hz. The data sets were then spat ially smoothed with 6 mm FWHM Gaussian kernel. Second, linear regressions were us ed to remove several spurious variances along with their temporal derivatives: he ad motion parameters, signals from a region centered in the white matter, and a region c entered in the cerebrospinal fluid. Third, correlation maps were created by computing t he correlation coefficients between the BOLD time course from the seed region, and the BOLD time course from all of the other brain voxels. Finally, correlation coefficien ts were converted to an approximately normal distribution using Fisher’s z-transformation At the second-level analysis, a two sample t -test was applied to evaluate the baseline scan of the two acupoint groups before ACU or min SHAM. Finally, the test for diffe rences of brain networks between the two groups was evaluated using a two sample t -test. All contrasts had a threshold at P< 0.005 (uncorrected) and a cluster size >3 voxels. Seed voxel correlation analysis (SCA) was then applied for connectivity results. SC A is based on extracting the BOLD

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53 time course from a predefined seed region and calcu lating cross-correlation coefficients with all other voxels in the brain (Liu et al., 200 9).

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54 CHAPTER 3 RESULTS Parametric Test Statistics There was an outlier present for subject 067 (CBT 1 = 35.34) which was substituted with the average of the remaining value s (36.31), and that value was used in the following calculations. Using Repeated Measures ANOVA, there was no significant within group difference ( p >0.05) found in ACU and min SHAM acupuncture groups for CBT and GLU. There were no significant group differ ences ( p >0.05) seen in the intergroup analysis using the same test. These resu lts point out that despite visual trends seen in CBT and Glu in the two different gro ups, no significance was found (see Figures 3-1 and 3-2). There were individual differences that are of inter est. In CBT, all ACU patients had a higher CBT during acupuncture which was lower at the completion of the treatment except for patients 028 (CBT values: 36.2 5, 36.21, 36.43) who had a BMI of 19.76, and 045 (CBT values: 36.59, 36.54, 36.46) wh ose BMI was 26.89. On the other hand, individual min SHAM patients differed in CBT outcomes. In general, there was slight increase or no change from before, during or after treatment. Patient 020 showed a major decrease in CBT (CBT values: 37.12, 36.67, 36.71). Patients 029 (CBT values: 36.61, 36.62, 36.56), 046 (CBT values: 36.87, 36.89 36.91), and 055 (CBT values: 36.93, 36.94, 36.77) had a slight increase followed by a decrease post-treatment. Patients 066 (CBT values: 36.42, 37.34, 37.23) and 087 (CBT values: 36.82, 36.96, 36.98) had major continuous increases in CBT after treatment (Figure 3-1). In most ACU individuals, Glu decreased during treat ment and post-treatment except in patient 018 (Glu values: 4.3, 4.3, 4.2) w ho remained relatively the same. In

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55 patients 045 (Glu values: 5.5, 5.8, 6.7) and 089 (G lu values: 4.2, 4.3, 4.9) there was a major increase during and after treatment. Patient 054 (Glu values: 5.3, 4.7, 5.6) had a decrease during treatment followed by an increase i n Glu. Patient 067 (Glu values: 4.8, 5.2, 4.8) showed an increase only during treatment. On the other hand, min SHAM patients showed a decrease in Glu during treatment except patient 020 (Glu values: 4.6, 4.7, 4.7) who had a slight increase during treatmen t. Patients 055 (Glu values: 4.9, 4.6, 6) and 068 (Glu values: 5.5, 4.8, 6.2) showed major Glu increases at the end of treatment (Figure 3-2). Nonparametric Test Statistics The original categorical Deqi data was best analyze d using a Mann-Whitney rank sum test based on the analysis done by Park et al. (2005). The results indicated that out of the 12 different sensations, only soreness, numb ness, and fullness were significant ( p <0.05, 1 tailed). The remaining sensations were ins ignificant ( p >0.05, 1 tailed) when compared between ACU and min SHAM acupuncture Deqi. A two sample KolmogorovSmirnov rank sum test was conducted to determine if there were intergroup differences between the ACU and min SHAM group Deqi sensations. Only soreness was found to be significantly different ( p <0.05, 2 tailed) between the two groups (see Figure 3-3). For individual Deqi data in the ACU group, there were n o reportings of warmth, tingling, itching, aching, pressure or heaviness sensations. For soreness, the highest score was 8.5 and the lowest was 0. For numbness, the highest score was 7 and the lowest was 0. For fullness, the highest was 8 and the lowest was 0. Only one patient felt coolness (score of 2). For sharp pain, the highest score was 7 and the lowest was 0. Only one patient felt dull pain (score 10). For other sensat ions reported, one patient felt pain for 1 min after needle insertion. On the other hand, in t he SHAM group, there were no

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56 reported sensations of warmth, dull pain, tingling, itching, aching, and pressure. For soreness, the highest score was 3 and the lowest wa s 0. For numbness, the highest score was 3 and the lowest was 0. For fullness, the highest score was 4 and the lowest was 0. Only one patient reported coolness (score of 4). For sharp pain, the highest score was 4 and the lowest was 0. For heaviness, th e highest score was 2 and the lowest was 0. For other sensations felt, patients 0 26, 047, 053, and 055 were sore for 1 sec after needle insertion and felt that the needle insertion was painful (Figure 3-3). For the ordinal repeated measures hunger data, we u sed the Kruskal-Wallis rank sum test to determine that there was no significant within-group difference between hunger 1, 2, and 3, and no interaction between time and group ( p >0.05). However, the intergroup comparison showed a significant differen ce between ACU and min SHAM hungers ( p <0.05) (see Figure 3-4). For individual hunger data in the ACU group, most patients had a slight increase or no change during treatment except for patients 017 (HUNGER values: 4, 6, 7), 027 (HUNGER values: 3.5, 9, 9), 028 (HUNGER values: 5.5, 7, 7.5), and 067 (HUNGER values: 3, 6, 7) who showe d dramatic increases in hunger values during and after treatment. Patient 018 (HUN GER values: 7, 5, 6) had a steady decrease during and after treatment. On the other h and, in the SHAM hunger group, most patients showed a steady increase or no change in hunger values except patient 020 (HUNGER values: 5, 3, 1) who had a steady decre ase in hunger values (Figure 34).

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57 Physiological Data Figure 3-1. Measurement of corrected average core b ody temperature (CBT) in degrees Celsius shown before (Pre-Acu), during (Acu) and af ter (Post-Acu) acupuncture (ACU) or minimal sham (min SHAM) treatm ents in overweight adult Chinese males (n = 10 for ACU and n = 9 for m in SHAM). There was no significant difference between groups as shown by t he student t -test ( p >0.05) despite a visual increase in CBT during both treatm ents. Standard errors bars are shown for each time point.

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58 Figure 3-2. Measurement of uncorrected average bloo d glucose (Glu) shown before (Pre-Acu), during (Acu) and after (Post-Acu) real a cupuncture (ACU) or minimal sham (min SHAM) treatment in overweight adu lt Chinese males (n=10 for ACU and n = 9 for min SHAM). There was no significant difference between or within the two groups as shown by a stud ent t -test ( p >0.05) despite a visual decrease in blood Glu during treat ments. Standard errors bars are shown for each time point.

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59 Figure 3-3. Averaged major Deqi sensations (sorenes s, numbness, fullness, coolness, warmth, sharp pain, dull pain, and heaviness) compa rison between real acupuncture (ACU) and minimal sham (min SHAM) treat ments in overweight adult Chinese males (n=10 in ACU and n = 9 min SHAM ). Significant intragroup differences were only found between soreness, numbness, and fullness ( p 0.05; 1 tailed; Mann-Whitney Rank sum test). Sorene ss was significant in an inter-group comparison ( p 0.05; 1 tailed; KolmogorovSmirnov Rank Sum test). Standard errors bars are sh own for each sensation.

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60 Figure 3-4. Averaged hunger sensation comparison be tween real acupuncture (ACU) and minimal sham (min SHAM) treatments in overweigh t adult Chinese males (n=10 in ACU and n = 9 in min SHAM). Significant in ter-group differences were found before (Hunger 1), during (Hunger 2), an d after (Hunger 3) treatment ( p 0.05; 1 tailed; Kruskal-Wallis Rank Sum test). Ther e was no interaction between time and ACU or min SHAM groups Standard errors bars are shown for each time point.

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61 Neuroimaging Data Using Talairach coordinates and SPM5, the AMY and HYP were chosen as ROIs for the functional connectivity results (see method s section and Figures 3-5 and 3-6). SCA results showed overlapping areas from functiona l connectivity analysis listed in Tables 3-1 to 3-4. For the AMY connectivity analysi s during ACU, the major overlapping regions are listed in Table 3-1. The key regions we re the uncus, putamen, caudate, para-HIPP, HIPP, lateral/medial globus pallidus, in sula, cingulate, substantia nigra and BA 2, 3, 4, 5, 6, 9, 21, 22, 24, 25, 28, 29, 30, 31 34, 35, 36, 37, 38, 40, 44, 45, and 47. For the AMY connectivity analysis during min SHAM, the major overlapping regions are listed in Table 3-2. The most important areas were the caudate, cingulate, insula, putamen, HIPP, para-HIPP, ACC, and BA 6, 7, 10, 11, 21, 22, 24, 25, 30, 31, 32, 34, 35, 36, 40, 41, 42, 43, and 47. For the HYP connect ivity analysis during ACU, the major overlapping regions are listed in Table 3-3. The ma in areas were the HIPP, para-HIPP, AMY, putamen, thalamus, ACC, and BA 2, 6, 8, 9, 10, 24, 28, 32, 34, 36, 38, 40, 44, 45, 46, and 47. For the HYP connectivity analysis durin g min SHAM, the major overlapping regions are listed in Table 3-4. The key areas were the HIPP, substantia nigra, insula, caudate, putamen, thalamus, and BA 17, 21, 28, 32, 35, 38, 39, 40, and 47. Comparisons of overlapping regions between the ROI s during ACU and min SHAM produced interesting results (see Table 3-5). Common overlapping regions for both ACU and min SHAM using the AMY as the ROI were the putamen, caudate, paraHIPP, HIPP, insula, cingulate, and BA 6, 21, 22, 24 25, 30, 31, 34, 36, 40, and 47. The common overlapping regions for both ACU and min SHA M using the HYP as the ROI were the HIPP, putamen, thalamus, and BA 28, 32, 38 40, and 47.

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62 Figure 3-5. Blood oxygen level dependent (BOLD) sig nificant brain regions from an amygdala-related functional connectivity analysis c omparing real acupuncture (ACU) versus minimal sham (SHAM)-treated individual s. Epoch of treatment lasted for 9 min. Results from the conjunction anal ysis were based on the discrete cosine transform (DCT) group results of th e two acupoints. The overlapping areas are the putamen, insula, parahipp ocampus, hippocampus, hypothalamus, and cerebellum. T-value scales locate d on bottom of picture ( p <0.05).

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63 Figure 3-6. Blood oxygen level dependent (BOLD) sig nificant brain regions from a hypothalamus-related functional connectivity analys is comparing real acupuncture (ACU) versus minimal sham (SHAM)-treate d individuals. Epoch of treatment lasted 9 min. Results from the conjunc tion analysis were based on the discrete cosine transform (DCT) group result s of the two acupoints. The overlapping areas are the putamen, insula, para hippocampus, hippocampus, amygdala, anterior cingulate cortex (A CC), thalamus, and pons. T-value scales located on bottom of picture ( p <0.05).

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64 Table 3-1. Overlapping areas during acupuncture tre atment from functional connectivity analysis using the amygdala as the region of interest. Talairach X Talairach Y Talairach Z Direction Regio n Area t -Value -27 -10 -20 Left Cerebrum Parahippocampal Gyrus Hi ppocampus 6.35 27 -4 -20 Right Cerebrum Uncus Amygdala 8.13 -24 -4 -20 Left Cerebrum Uncus Amygdala 6.84 -48 -4 -20 Left Cerebrum Middle Temporal Gyrus Bro dmann area 21 4.51 -24 -27 -16 Left Cerebrum Parahippocampal Gyrus Br odmann area 35 5.25 33 -15 -17 Right Cerebrum Parahippocampal Gyrus Hi ppocampus 7.39 -30 2 -18 Left Cerebrum Parahippocampal Gyrus Brod mann area 34 5.74 27 8 -18 Right Cerebrum Inferior Frontal Gyrus Bro dmann area 34 5.026 30 -33 -14 Right Cerebrum Parahippocampal Gyrus Br odmann area 47 4.12 27 -33 -14 Right Cerebrum Parahippocampal Gyrus Br odmann area 36 3.50 -21 -30 -14 Left Cerebrum Parahippocampal Gyrus Br odmann area 36 6.90 -24 -15 -14 Left Cerebrum Parahippocampal Gyrus Br odmann area 28 3.51 -30 2 -15 Left Cerebrum Parahippocampal Gyrus Brod mann area 34 5.90 30 5 -15 Right Cerebrum Parahippocampal Gyrus Brod mann area 38 5.24 27 8 -16 Right Cerebrum Inferior Frontal Gyrus Bro dmann area 47 4.53 -30 8 -16 Left Cerebrum Inferior Frontal Gyrus Bro dmann area 47 5.55 27 -36 -11 Right Cerebrum Parahippocampal Gyrus Br odmann area 37 4.43 -9 -18 -9 Left Brainstem Substania Nigra 3.88 36 11 -11 Right Cerebrum Inferior Frontal Gyrus Su bstania Nigra 4.23 -21 -32 -6 Left Cerebrum Parahippocampal Gyrus Bro dmann area 30 4.04 -9 -21 -7 Left Brainstem Brodmann area 28 3.57 12 -18 -7 Right Brainstem Substania Nigra 4.31

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65 Table 3-1. continued Talairach X Talairach Y Talairach Z Direction Regio n Area t -Value 18 11 -8 Right Cerebrum Lentiform Nucleus Putamen 6 .20 -3 11 -8 Left Cerebrum Anterior Cingulate Putamen 4 .33 -18 11 -8 Left Cerebrum Lentiform Nucleus Brodmann area 25 3.73 21 -12 -4 Right Cerebrum Lentiform Nucleus Lateral Globus Pallidus 4.79 -24 -12 -4 Left Cerebrum Lentiform Nucleus Medial G lobus Pallidus 4.43 -27 -9 -5 Left Cerebrum Lentiform Nucleus Lateral G lobus Pallidus 4.80 -24 -3 -5 Left Cerebrum Lentiform Nucleus Brodmann area 22 5.02 -18 12 2 Left Cerebrum Lentiform Nucleus Caudate He ad 4.20 -9 -43 8 Left Cerebrum Posterior Cingulate Brodmann area 29 3.80 42 -2 8 Right Cerebrum Insula Brodmann area 44 4.53 59 4 14 Right Cerebrum Precentral Gyrus Brodmann ar ea 6 4.63 -59 7 16 Left Cerebrum Inferior Frontal Gyrus Brodm ann area 6 4.59 -42 -34 21 Left Cerebrum Insula Brodmann area 44 5. 65 -45 -2 22 Left Cerebrum Inferior Frontal Gyrus Brod mann area 45 4.92 -45 4 27 Left Cerebrum Inferior Frontal Gyrus Brodm ann area 9 5.66 -45 -7 34 Left Cerebrum Precentral Gyrus Brodmann a rea 40 3.96 9 16 39 Right Cerebrum Cingulate Gyrus Brodmann area 3 3.56 -12 -7 39 Left Cerebrum Cingulate Gyrus Brodmann a rea 24 6.08 -9 -9 45 Left Cerebrum Cingulate Gyrus Brodmann ar ea 31 7.47 9 -4 44 Right Cerebrum Cingulate Gyrus Brodmann ar ea 31 5.58 -3 -21 48 Left Cerebrum Medial Frontal Gyrus Brodm ann area 5 3.69 -24 -26 57 Left Cerebrum Precentral Gyrus Brodmann area 3 3.79 -27 -32 60 Left Cerebrum Postcentral Gyrus Brodman n area 2 7.78 -21 -26 59 Left Cerebrum Postcentral Gyrus Brodman n area 3 6.02 -33 -23 59 Left Cerebrum Precentral Gyrus Brodmann area 4 4.33 9 -12 59 Right Cerebrum Medial Frontal Gyrus Brodm ann area 4 3.79

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66 Table 3-2. Overlapping areas during minimal sham ac upuncture treatment from functional connectivity an alysis using the amygdala as the region of interest. Talairach X Talairach Y Talairach Z Direction Regio n Area t -Value -24 -27 -16 Left Cerebrum Parahippocampal Gyrus Br odmann area 35 3.71 -30 -15 -14 Left Cerebrum Parahippocampal Gyrus Hi ppocampus 6.31 30 -12 -15 Right Cerebrum Parahippocampal Gyrus Hi ppocampus 3.78 21 2 -15 Right Cerebrum Parahippocampal Gyrus Brod mann area 34 6.23 -24 -30 -11 Left Cerebrum Parahippocampal Gyrus Br odmann area 36 4.52 -24 2 -13 Left Cerebrum Parahippocampal Gyrus Brod mann area 34 7.82 36 17 -13 Right Cerebrum Inferior Frontal Gyrus Br odmann area 47 4.54 18 -32 -8 Right Cerebrum Parahippocampal Gyrus Bro dmann area 35 4.00 -3 49 -13 Left Cerebrum Medial Frontal Gyrus Brodm ann area 11 3.65 6 52 -13 Right Cerebrum Medial Frontal Gyrus Brodm ann area 11 4.24 15 -35 -6 Right Cerebrum Parahippocampal Gyrus Bro dmann area 30 4.93 -18 -32 -6 Left Cerebrum Parahippocampal Gyrus Bro dmann area 30 4.41 6 11 -8 Right Cerebrum Anterior Cingulate Brodmann area 25 4.27 -24 8 -5 Left Cerebrum Lentiform Nucleus Putamen 5 .99 21 11 -6 Right Cerebrum Lentiform Nucleus Putamen 4.92 -9 11 -6 Left Cerebrum Caudate Caudate Head 4.04 12 14 -6 Right Cerebrum Caudate Caudate Head 4.73 6 46 -7 Right Cerebrum Medial Frontal Gyrus Brodma nn area 10 3.50 -59 -6 -2 Left Cerebrum Superior Temporal Gyrus Br odmann area 21 3.75 56 11 -3 Right Cerebrum Superior Temporal Gyrus Br odmann area 22 4.64 12 46 -5 Right Cerebrum Anterior Cingulate Brodman n area 32 3.70 -59 0 8 Left Cerebrum Precentral Gyrus Brodmann ar ea 6 3.69 -56 -17 12 Left Cerebrum Transverse Temporal Gyrus Brodmann area 41 3.86 -62 -17 12 Left Cerebrum Transverse Temporal Gyrus Brodmann area 42 3.91

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67 Table 3-2. continued Talairach X Talairach Y Talairach Z Direction Regio n Area t -Value 3 -12 45 Right Cerebrum Paracentral Lobule Brodman n area 31 5.53 -3 -12 45 Left Cerebrum Paracentral Lobule Brodman n area 31 3.75 -3 8 44 Left Cerebrum Medial Frontal Gyrus Brodman n area 32 4.05 3 -9 56 Right Cerebrum Medial Frontal Gyrus Brodma nn area 6 4.81 -62 -52 16 Left Cerebrum Superior Temporal Gyrus B rodmann area 22 4.25 -48 -17 17 Left Cerebrum Postcentral Gyrus Brodman n area 43 3.60 -48 -22 20 Left Cerebrum Insula 4.39 -6 22 24 Left Cerebrum Anterior Cingulate Brodmann area 24 3.58 -6 19 29 Left Cerebrum Cingulate Gyrus Brodmann ar ea 32 4.16 -62 -30 37 Left Cerebrum Inferior Parietal Lobule Brodmann area 40 3.89 3 -12 42 Right Cerebrum Cingulate Gyrus Brodmann a rea 24 4.41 -3 -12 42 Left Cerebrum Cingulate Gyrus Brodmann a rea 24 3.75 3 11 41 Right Cerebrum Cingulate Gyrus Brodmann ar ea 32 6.35 15 -49 66 Right Cerebrum Postcentral Gyrus Brodman n area 7 3.62

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68 Table 3-3. Overlapping areas during acupuncture tre atment from functional connectivity analysis using the hypothalamus as the region of interest. Talairach X Talairach Y Talairach Z Direction Region Area t-Value -24 -30 -14 Left Cerebrum Parahippocampal Gyrus Br odmann area 36 3.68 -24 -7 -15 Left Cerebrum Parahippocampal Gyrus Amy gdala 3.91 30 -1 -15 Right Cerebrum Parahippocampal Gyrus Amy gdala 3.65 33 5 -15 Right Cerebrum Superior Temporal Gyrus Brodmann area 38 3.89 27 5 -15 Right Cerebrum Parahippocampal Gyrus Brod mann area 34 3.75 -27 5 -15 Left Cerebrum Parahippocampal Gyrus Brod mann area 34 3.72 30 14 -16 Right Cerebrum Inferior Frontal Gyrus Br odmann area 47 3.79 -24 -30 -11 Left Cerebrum Parahippocampal Gyrus Br odmann area 36 3.56 18 -15 -12 Right Cerebrum Parahippocampal Gyrus Br odmann area 28 4.01 30 -27 -9 Right Cerebrum Sub-Gyral Hippocampus 4.8 4 -30 -27 -9 Left Cerebrum Sub-Gyral Hippocampus 3.9 0 -24 -21 -9 Left Cerebrum Parahippocampal Gyrus Bro dmann area 28 3.99 45 37 -9 Right Cerebrum Middle Frontal Gyrus Brodm ann area 47 4.33 42 37 -9 Right Cerebrum Middle Frontal Gyrus Brodm ann area 47 3.61 27 -3 -5 Right Cerebrum Lentiform Nucleus Putamen 3.63 -24 6 -5 Left Cerebrum Lentiform Nucleus Putamen 6 .46 15 -23 1 Right Cerebrum Thalamus Ventral Posterior Medial Nucleus 3.51 50 17 -1 Right Cerebrum Inferior Frontal Gyrus Bro dmann area 47 3.69 45 47 -2 Right Cerebrum Inferior Frontal Gyrus Bro dmann area 10 3.76 6 47 -2 Right Cerebrum Anterior Cingulate Brodmann area 32 3.65 6 49 -2 Right Cerebrum Anterior Cingulate Brodmann area 10 3.65 12 -9 3 Right Cerebrum Thalamus Ventral Lateral Nu cleus 5.33 -3 32 1 Left Cerebrum Anterior Cingulate Brodmann area 24 4.37 21 -23 7 Right Cerebrum Thalamus Ventral Posterior Lateral Nucleus 5.09 6 47 3 Right Cerebrum Anterior Cingulate Brodmann area 32 4.82 6 50 3 Right Cerebrum Medial Frontal Gyrus Brodman n area 10 4.48 59 15 8 Right Cerebrum Precentral Gyrus Brodmann a rea 44 4.81 -56 18 7 Left Cerebrum Inferior Frontal Gyrus Brod mann area 45 4.06

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69 Table 3-3. continued Talairach X Talairach Y Talairach Z Direction Region Area t-Value 9 33 15 Right Cerebrum Anterior Cingulate Brodmann area 24 3.68 45 39 15 Right Cerebrum Middle Frontal Gyrus Brodm ann area 46 3.83 -6 39 15 Left Cerebrum Anterior Cingulate Brodmann area 32 4.02 3 33 18 Right Cerebrum Anterior Cingulate Brodmann area 32 3.67 48 42 17 Right Cerebrum Middle Frontal Gyrus Brodm ann area 46 5.70 -3 22 24 Left Cerebrum Anterior Cingulate Brodmann area 24 3.54 36 22 32 Right Cerebrum Middle Frontal Gyrus Brodm ann area 9 4.91 -3 34 34 Left Cerebrum Medial Frontal Gyrus Brodma nn area 6 3.92 -24 22 38 Left Cerebrum Middle Frontal Gyrus Brodm ann area 8 3.53 6 28 37 Right Cerebrum Medial Frontal Gyrus Brodma nn area 6 4.16 56 -27 43 Right Cerebrum Postcentral Gyrus Brodman n area 2 4.22 3 22 40 Right Cerebrum Cingulate Gyrus Brodmann ar ea 32 3.51 6 28 40 Right Cerebrum Medial Frontal Gyrus Brodma nn area 8 5.86 -56 -36 46 Left Cerebrum Inferior Parietal Lobule Brodmann area 40 3.85 -3 37 42 Left Cerebrum Medial Frontal Gyrus Brodma nn area 8 8.19

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70 Table 3-4. Overlapping areas during minimal sham ac upuncture treatment from functional connectivity an alysis using the hypothalamus as the region of interest. Talairach X Talairach Y Talairach Z Direction Regio n Area t -Value -36 5 -18 Left Cerebrum Superior Temporal Gyrus Br odmann area 38 3.97 -24 11 -16 Left Cerebrum Inferior Frontal Gyrus Br odmann area 47 4.70 -18 -18 -12 Left Cerebrum Parahippocampal Gyrus Br odmann area 35 7.59 -33 -18 -12 Left Cerebrum Parahippocampal Gyrus Hi ppocampus 3.81 -18 -15 -12 Left Cerebrum Parahippocampal Gyrus Br odmann area 28 5.58 12 -21 -9 Right Brainstem Substania Nigra 6.13 -9 -18 -9 Left Brainstem Substania Nigra 4.68 -56 -15 -9 Left Cerebrum Middle Temporal Gyrus Bro dmann area 21 3.49 -42 -6 -7 Left Cerebrum Sub-Gyral Brodmann area 21 3.95 42 -12 -4 Right Cerebrum Insula 3.66 6 -93 2 Right Cerebrum Lingual Gyrus Brodmann area 17 4.47 9 9 -3 Right Cerebrum Caudate Caudate Head 3.94 -18 9 -3 Left Cerebrum Lentiform Nucleus Putamen 4 .49 -6 15 -1 Left Cerebrum Caudate Caudate Head 4.54 -12 -8 6 Left Cerebrum Thalamus Ventral Lateral Nu cleus 5.92 -12 -6 6 Left Cerebrum Thalamus Ventral Anterior N ucleus 3.57 -45 -75 12 Left Cerebrum Middle Temporal Gyrus Bro dmann area 39 4.56 -12 -20 12 Left Cerebrum Thalamus Medial Dorsal Nu cleus 4.50 -15 -20 12 Left Cerebrum Thalamus Lateral Posterio r Nucleus 3.89 -12 -17 15 Left Cerebrum Thalamus Lateral Dorsal N ucleus 5.27 -6 16 30 Left Cerebrum Cingulate Gyrus Brodmann ar ea 32 3.85 53 -53 41 Right Cerebrum Inferior Parietal Lobule Brodmann area 40 4.30

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71 Table 3-5. Common overlapping areas during acupunct ure and minimal sham acupuncture treatments from fu nctional connectivity analysis using the amygdala and hypoth alamus as the regions of interest. Amygdala Region of Interest Hypothalamus Region of Interest Caudate Thalamus Putamen Putamen Parahippocampus Hippocampus Hippocampus Brodmann areas 28, 32, 38, 40, 47 Insula Cingulate Brodmann areas 6, 21, 22, 24, 25, 30, 31, 34, 35, 3 6, 40, 47

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72 CHAPTER 4 DISCUSSION AND CONCLUSION Discussion As mentioned in the literature review, acupoint spe cificity has been studied and questioned. We specifically chose ST 36 and SP 9 ba sed on their functions in TCM and acupuncturist recommendation and approval. However, it was noted by another experienced acupuncturist that for our specific hyp otheses and goals, other acupoints could have been chosen instead. For glucose metabol ism and thermoregulation, acupoints LI 4, LIV 3, GV 3, GV 4, LI 10, and ST 36 bilaterally were recommended. For HYP activation and hunger suppression, acupoints GB 34, LIV 3, LI 4, SP 6, and SP 9 bilaterally were suggested. Most importantly, ST 36 and SP 9 are crucial points in obesity and weight loss acupuncture studies as summ arized by Cho et al. (2009). Choosing appropriate acupuncture controls is diffic ult in this field of study, hence the choice of our control method was unique with no teworthy results. Kleinhenz and peers (1999) used the Streitberger needle method, w hich mimics needle penetration in real acupuncture but does not fully penetrate the s kin layers. We combined this method with the standard sham placebo acupuncture protocol Surprisingly, all subjects believed they received real acupuncture, thus this proves we used an effective control method. Our neurophysiological results were rather unexpect ed. Most of our results were insignificant although visually, it could be percei ved that Glu decreased during ACU and min SHAM, and CBT increased during ACU and min SHAM Some significance was found in certain Deqi sensations and hunger ( p < 0.05; Kruskal-Wallis Rank Sum test and Mann-Whitney test respectively). Based on previ ous studies, fasting plasma levels

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73 in obese subjects showed that levels of ghrelin, ad iponectin, CCK, and NPY decreased, while leptin levels increased (Cabioglu and Ergene, 2006; Pissios and Maratos-Flier, 2007). Unlike the findings in obese individuals, we are led to believe that the specificity of the acupoints chosen caused a release in ghrelin during ACU to stimulate appetite, hence increasing hunger in our overweight populatio n. This is different compared to other studies that show ACU suppresses appetite by increasing serotonin levels (Wenhe and Yucun, 1981) and promotes satiety in the HYP (Shiraishi et al., 1995). The VMH, LHA and ventral striatal regions are known to regulate glycometabolism (Morton et al. 2006), while the HYP and brainstem are CNS c enters that affect gastric function (Wu et al., 1999). ST 36 is supposed to promote sat iety, regulate intestinal motility, cause sedation (Cabioglu and Ergene, 2006), and inc rease excitability of the satiety center in the VMH (Zhao et al., 2000). Acupuncture at this point and SP 9 caused a significant increase in hunger in our subjects, whi ch would be useful to treat anorexic patients or individuals receiving chemotherapy. Zus anli has also been reported to increase motility in individuals with hypoactive in testines and vice-versa (Li et al., 1992). Stimulation of ST 36 also increases the amplitude a nd frequency of gastric peristalsis in normal individuals, shortening the gastric emptying time and delaying the contractions (Li et al., 1992). Therefore, it would have been us eful to auscultate the subjects’ intestines and stomach before, during, and after AC U or min SHAM procedures to verify these findings. Studies have shown that mere electrical stimulation of the VMH caused increased Glu uptake via the sympathetic nervous sy stem in skeletal muscle without increasing plasma insulin concentration (Minokoshi et al., 1994; Lang et al., 1995). In

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74 our study, ACU stimulation could be correlated to t he electrical stimuli by causing Glu to decrease during treatment due to ACU having a centr al effect on the overall physiology. The ARC and VMH may even be activated centrally to uptake Glu (Morton et al., 2006) via the effects of ACU. If we used EAS or prolonged manual ACU treatment in our experiment, we would probably have significant phys iological results. It is also necessary to look at deactivations and activations in specific brain areas during rest, stimulation, and post-stimulation in order to verif y this assumption in future studies. ACU treatment in our study had no statistically signifi cant effect on Glu, hence it may be a time effect only; as hunger increases, Glu decrease s then increases. A possible interaction may be occurring, or this difference is probably contributed to hormonal interplay and acupuncture stimulation of the CNS. The AMY and HYP were chosen as ROIs for the simple reason that the various regions of the HYP are involved in appetite control and thermoregulation. The AMY is often linked with the HYP (Joranby et al., 2005). A s shown in Figures 3-5 and 3-6, there was a significant difference in the spatial pattern s of the distinct brain regions between the two treatment groups. Functional connectivity d escribes the temporal synchrony or correlation of the BOLD fMRI signal from two or mor e anatomically separated brain regions (Friston et al., 1993). Therefore, the spat ial and temporal patterns of brain responses would be modulated by the sustained effec ts of ACU versus min SHAM. We derived the functional connectivity networks from t he temporal pattern during the states during and after stimulation associated with the RO Is and the overlapping regions. This is along the lines of our hypotheses that the mPFC, cerebral cortex, cerebellum, DLPFC, and VMPFC would be activated due to the acut e ACU effects affecting

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75 primarily satiety and some cognitive functions. Wit h respect to the connectivity analyses, primary somatosensory, motor function, vi sual stimulation, language, limbic system (pain), and cognitive function centers were involved in both ACU and min SHAM. This was expected since the sensation of the needle and the surrounding environment stimulated all subjects’ brain areas su ch as noted in BA 2-47. One important observation was that with activations in the AMY, ACC, and BA 9, 20, and 36, the subjects were thinking about food, Deqi sensati ons and/or hunger. Therefore, there was a direct correlation between behavioral data an d the functional connectivity results. Conclusion Based on connectivity results with the AMY and HYP, it can be assumed that the mode of action for ACU and min SHAM is mediated by the limbic system specifically the neurotransmitter DA. DA is known to increase heart rate and blood pressure (Benes, 2001), hence it would affect CBT in our subjects. D A also has a role in pain processing (Flores et al., 2004), which would explain Deqi or sensations felt during ACU stimulation. This conclusion is based on ACU activa tion of the insula (responsible for homeostasis), ACC, HYP, putamen, globus pallidus, s ubstantia nigra, and HIPP. The ACC is of great importance in this study since it i s involved in blood pressure regulation and heart rate, but it also shares direct connectio n with the AMY, HYP, nACC, and insula (Bush et al., 2000). As for the physiologica l data, it can be inferred that the reason for the variability amongs treatment groups was due to the fact that ACU is tailored to the unique physiology of each individua l despite having a homogeneous experimental population.

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76 Study Limitations Limitations in most acupuncture studies regard obta ining individuals that meet the inclusion criteria and adequate sample size. A large group size is needed to do a power analysis in multiple sessions to capture the activation and deactivation patterns evoked by acupuncture stimulation at particular acu points. Confounding factors include Deqi mixed with pain, artifactual activation, appro priate controls, patient anxiety and anticipation of pain or discomfort from acupuncture treatment. Hui et al. (2007) described this in their study regarding the influen ce of patient sensations on fMRI BOLD signal changes. It was difficult to obtain an ideal overweight population in our study as well. In the Sichuan Province (China), the BMI of o verweight individuals was much lower than in other areas or countries. It could be due to the diet (hot, spicy food) and lifestyle (genetic hypertension). As discussed in a nother one of our studies, individual differences in response to acupuncture should be ta ken into account as seen by the variable results in our study population. More subj ects are needed to verify our pilot study results, as well as implementing a different experimental design.

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77 CHAPTER 5 LIST OF REFERENCES Ahima RS, Antwi DA. Brain regulation of appetite an d satiety. Endocrinol and Metabol Clinics 2008;4:37. Ahima RS, Lazar MA. Adipokines and the peripheral a nd neural control of energy balance. Mol Endocrinol 2008;22(5):1023-31. Ahima RS, Saper CB, Flier JS, Elmquist JK. Leptin r egulation of neuroendocrine systems. Front Neuroendocrinol 2000;21:263-307. Akil H, Watson SJ, Young E, Lewis ME, Khachaturian H, Walker JM. Endogenous opioids: biology and function. Annu Rev Neurosci 19 84;7:223-5. Anonymous, NIH. Consensus Conference. Acupuncture. JAMA 1998;280:1518-24. Arana FS, Parkinson JA, Hinton E. Dissociable contr ibutions of the human amygdala and orbitofrontal cortex to incentive motivation an d goal selection. J Neurosci 2003;23(29):9632-8. Avena NM, Carrillo CA, Needham L, Leibowitz SF, Hoe bel BG. Sugar-dependent rats show enhanced intake of unsweetened ethanol. Alcoho l 2004;34:203-9. Avena NM, Long KA, Hoebel BG. Sugar-dependent rats show enhanced responding for sugar after abstinence: evidence of a sugar depriva tion effect. Physiol Behav 2005;84(3):359-62. Avena NM, Rada P, Hoebel BG. Evidence for sugar add iction: behavioral and neurochemical effects of intermittent, excessive su gar intake. Neurosci Biobehav Rev 2008;32(1):20-39. Bagnasco M, Dube MG, Kalra PS, Kalra SP. Evidence f or the existence of distinct central appetite and energy expenditure pathways an d stimulation of ghrelin as revealed by hypothalamic site-specific leptin gene therapy. Endocrinol 2002a;143:4409-21. Bagnasco M, Kalra PS, Kalra SP. Ghrelin and leptin pulse discharge in fed and fasted rats. Endocrinol 2002b;143:726-9. Bai L, Yan H, Li L, Qin W, Chen P, Liu P, Gong Q, L iu Y, Tian J. Neural specificity of acupuncture stimulation at pericardium 6: evidence from an fMRI study. J Magn Reson Imag 2009a;In Press. Bai L, Qin W, Tian J, Liu P, Li L, Chen P, Dai J, C raggs JG, von Deneen KM, Liu Y. Time-varied characteristics of acupuncture effects in fMRI studies. Human Brain Mapping 2009b;30(11):3445-60.

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78 Baskin DG, Figlewicz Latteman D. Insulin and leptin : dual adiposity signals to the brain for the regulation of food intake and body weight. Brain Res 1999;848:114-23. Bassareo V, Di Chiara G. Modulation of feeding-indu ced activation of mesolimbic dopamine transmission by appetitive stimuli and its relation to motivational state. Europ J Neurosci 1999;11:4389-97. Bayol SA, Farrington SJ, Stickland NC. A maternal ‘ junk food’ diet in pregnancy and lactation promotes an exacerbated taste for ‘junk f ood’ and a greater propensity for obesity in rat offspring. Brit J Nutr 2007;98(4 ):843-51. Beaver JD, Lawrence AD, van Ditzhuijzen J. Individu al differences in reward drive predict neural responses to images of food. J Neuro sci 2006;26(19):5160-6. Becker AE, Keel P, Anderson-Fye EP, Thomas JJ. Gene s and/or jeans?: genetic and socio-cultural contributions to risk for eating dis orders. J Addict Dis 2004;23(3):81-103. Benarroch F, Hirsch HJ, Genstil L, Landau YE, Gross -Tsur V. Prader-Willi syndrome: medical prevention and behavioral challenges. Child Adolesc Psychiatric Clin N Am 2007;16(3):695-708. Benes, FM. Carlsson and the discovery of dopamine. Trends Pharm Sci 2001;22(1):467. Berridge KC, Robinson TE. Parsing reward. Trends Ne urosci 2003;26(9):507-13. Berry EM, Mechoulam R. Tetrahydrocannabinol and end ocannabinoids in feeding and appetite. Pharmacol Ther 2002;95:185-90. Berthoud H-R. Multiple neural systems controlling f ood intake and body weight. Neurosci Biobehavior Rev 2002;26:393-428. Blum K, Cull JG, Braverman ER, Comings DE. Reward d eficiency syndrome. Am Scientist 1996a;84:132-45. Blum K, Sheridan PJ, Wood RC. The D2 dopamine recep tor gene as a determinant of reward deficiency syndrome. J R Soc Med 1996b;89(7) :396-400. Buda-Levin A, Wojnicki FH, Corwin RL. Baclofen redu ces fat intake under binge-type conditions. Physio Behav 2005;86(1-2):176-84. Bush G, Luu P, Posner MI. Cognitive and emotional i nfluences in anterior cingulate cortex. Trends Cogn Sci 2000;4(6):215–22.

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79 Cabioglu MT, Ergene N, Tan U. The treatment of obes ity by acupuncture. Intern J Neurosci 2006;116:165-75. Cabioglu MT, Ergene N. Changes in serum leptin and beta endorphin levels with weight loss by electroacupuncture and diet restriction in obesity treatment. Am J Chin Med 2006;34(1):1-11. Calle EE, Kaaks R. Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer 2004;4:579-91. Cardinal RN, Everitt BJ. Neural and psychological m echanisms underlying appetitive learning: links to drug addiction. Curr Opin Neurob iol 2004;14(2):156-62. Cardinal RN, Parkinson JA, Hall J. Emotion and moti vation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci B iobehav Rev 2002;26(3):32152. Carrel AL, Moerchen V, Myers SE. Growth hormone imp roves mobility and body composition in infants and toddlers with Prader-Wil li syndrome. J Pediatr 2004;145:744-9. Cassidy SB. Prader-Willi syndrome. J Med Genet 1997 ;34:917-23. Cho ZH, Chung SC, Jones JP, Park JB, Park HJ, Lee H J, Wong EK, Min BI. New findings of the correlation between acupoints and c orresponding brain cortices using functional MRI. National Acad Sci 1997;2670-3 Cho ZH, Chung SC, Lee HJ, Wong EK, Min BI. Retracti on. New findings of the correlation between acupoints and corresponding bra in cortices using functional MRI. Proc Natl Acad Sci USA 2006;103:105-27. Cho ZH, Lee JS, Thabane L, Lee J. Acupuncture for o besity: a systematic review and meta-analysis. Intl J Obesity 2009;33:183-96. Cho ZH, Oleson TD, Alimi D, Niemtzow RC. Acupunctur e: the search for biologic evidence with functional magnetic resonance imaging and positron emission tomography techniques. J Altern Complement Med 2002 ;8(4):399-401. Colantuoni C, Rada P, McCarthy J, Patten C, Avena N M, Chadeayne A, Hoebel BG. Evidence that intermittent, excessive sugar intake causes endogenous opioid dependence. Obesity Research 2002;10:478-88. Colantuoni C, Schwenker J, McCarthy J, Rada P, Lade nheim B, Cadet JL, Schwartz GJ, Moran TH, Hoebel BG. Excessive sugar intake alt ers binding to dopamine and mu-opioid receptors in the brain. Neuroreport 2 001;12:3549-52.

PAGE 80

80 Cornier MA, Von Kaenel SS, Bessesen DH. Effects of overfeeding on the neuronal response to visual food cues. Am J Clin Nutr 2007;8 6(4):965-71. Cowley MA, Smart JL, Rubinstein M, Cerdan MG, Diano S, Horvath TL, Cone RD, Low MJ. Leptin activates anorexigenic POMC neurons thro ugh a neural network in the arcuate nucleus. Nature 2001;411:480-4. Crombag HS, Shaham Y. Renewal of drug seeking by co ntextual cues after prolonged extinction in rats. Behav Neurosci 2002;116(1):16973. Cummings DE, Clement K, Purnell JQ. Elevated ghreli n levels in Prader-Willi syndrome. Nat Med 2002;8:643-4. De Castro JM. How can energy balance be achieved by free-living human subjects? Proc Nutr Soc 1997;56(1A):1-14. DeFalco J, Tomishima M, Liu H. Virus-assisted mappi ng of neural inputs to a feeding center in the hypothalamus. Science 2001;291(5513): 2608-13. Di Chiara G. Nucleus accumbens shell and core dopam ine: differential role in behavior and addiction. Behav Brain Res 2002;137:75-114. Dimitropoulos A, Feurer ID, Roof E. Appetitive beha vior, compulsivity, and neurochemistry in Prader-Willi syndrome. Ment Retar d Dev Disabil Res Rev 2000;6(2):125-30. Dung HC. Role of the vagus nerve in weight reductio n through auricular acupuncture. Am J Acupuncture 1986;14:249-54. Duva MA, Tomkins EM, Moranda LM, Kaplan R, Sukhaseu m A, Jimenez A, Stanley BG. Reverse microdialysis of N-methyl-D-aspartic acid i nto the lateral hypothalamus of rats: effects on feeding and other behaviors. Br ain Res 2001;921(1-2):122-32. Elliott R, Friston KJ, Dolan RJ. Dissociable neural responses in human reward systems. J Neurosci 2000;20:6159-65. Elmquist JK, Elias CF, Saper CB. From lesions to le ptin: hypothalamic control of food intake and body weight. Neuron 1999;22(2):221-32. Erlanson-Albertsson C. How palatable food disrupts appetite regulation. Basic Clin Pharm Toxicol 2005;97:61-73. Ernst MM, Epstein LH. Habituation of responding for food in humans. Appetite 2002;38(3):224-34.

PAGE 81

81 Farooqi IS, O’Rahilly S. Recent advances in the gen etics of severe childhood obesity. Arch Dis Child 2000;83:31-4. Fedoroff I, Polivy J, Herman CP. The specificity of restrained versus unrestrained eaters' responses to food cues: general desire to e at, or craving for the cued food? Appetite 2003;41(1):7-13. File SE, Lippa AS, Beer B, Lippa MT. Unit 8.4 Anima l tests of anxiety. In: Crawley JN et al. (eds), Current Protocols in Neuroscience, John Wiley & Sons, Inc.: Indianapolis 2004. Flores JA, El Banoua F, Galn-Rodrguez B, Fernande z-Espejo E. Opiate antinociception is attenuated following lesion of large dopamine neurons of the periaqueductal grey: critical role for D1 (not D2) dopamine receptors.Pain 2004;110(1-2):205-14. Foster GD. Non-dieting approaches. In: Brownell KD, Fairburn CG (eds). Eating disorders and obesity: a comprehensive handbook. Gu lford: NY 2001. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Fun ctional connectivity: the principalcomponent analysis of large (PET) data sets. J Cere b Blood Metab 1993;13:514. Fuster JM. Frontal lobe and cognitive development. J Neurocytol 2002;31:373-85. Galani C, Schneider H. Prevention and treatment of obesity with lifestyle interventions: review and meta-analysis. IntJ Public Health 2007;5 2(6):348-59. Gareus IK, Lacour M, Schulte AC, Hennig J. Is there a BOLD response of the visual cortex on stimulation of the vision-related acupoin t GB 37? J Mag Res Imag 2002;15:227-32. Gill T, King L, Caterson I. Obesity prevention: nec essary and possible. A structured approach for effective planning. Proc Nutr Soc 2005 ;64:255-61. Glenn CG, Driscoll DJ, Thomas PY. Genomic imprintin g potential function and mechanisms revealed by the Prader-Willi and Angelma n syndromes. Mol Hum Reprod 1997;3:321-32. Gold, MS. Introduction. Psych Annals 2003;33(2):5-2 1. Goldstone AP, Brynes AE, Thomas EL. Resting metabol ic rate, plasma leptin concentrations, leptin receptor expression, and adi pose tissue measured by whole-body magnetic resonance imaging in women with Prader-Willi syndrome. Am J Clin Nutr 2002;75:468-75.

PAGE 82

82 Goldstone AP, Patterson M, Kalingag N. Fasting and post-prandial hyperghrelinemia in Prader-Willi syndrome is partially explained by hyp oinsulinemia, and is not due to peptide YY 3-36 deficiency or seen in hypothalamic obesity due to craniopharyngioma. J Clin Endocrinol Metab 2001a;90 :2681-90. Goldstone AP, Thomas EL, Brynes AE. Visceral adipos e tissue and metabolic complications of obesity are reduced in Prader-Will i syndrome female adults: evidence for novel influences on body fat distribut ion. J Clin Endocrinol Metab 2001b;86:4330-8. Goldstone AP. Prader-Willi syndrome: advances in ge netics, pathophysiology and treatment. Trends Endocrinol Metab 2004;15(1):12-20 Gonzalez S, Manzanares J, Berrendero F, Wenger T, C orchero J, Bisogno T. Identification of endocannabinoids and cannabinoid CB(1) receptor mRNA in the pituitary gland. Neuroendocrinol1999;70:137-45. Gottfried JA, O'Doherty J, Dolan RJ. Encoding predi ctive reward value in human amygdala and orbitofrontal cortex. Science 2003;301 (5636):1104-7. Haddock CK, Dill PL. The effects of food on mood an d behavior: implications for the addictions model of obesity and eating disorders. F ood as a Drug, The Haworth Press, Inc. 2000;17-47. Hans J-S, Terenius L. Neurochemical basis of acupun cture analgesia. Ann Rev Pharmacol Toxicol 1982;22:193-220. Hawk LW, Baschnagel JS, Ashare RL. Craving and star tle modification during in vivo exposure to food cues. Appetite 2004;43(3):285-94. Hernandez L, Hoebel BG. Feeding and hypothalamic st imulation increase dopamine turnover in the accumbens. Physiol Behav 1988;44(45):599-606. Hinton EC, Parkinson JA, Holland AJ. Neural contrib utions to the motivational control of appetite in humans. Eur J Neurosci 2004;20(5):141118. Holland AJ, Treasure J, Caskeran P. Measurement of excessive appetite and metabolic changes in Prader-Willi syndrome. Int J Obes 1993;1 7:527-32. Holm VA, Cassidy SB, Butler MG. Prader-Willi syndro me: consensus diagnostic criteria. Pediatrics 1993;91(2):398-402. Howlett AC. The cannabinoid receptors. Prostaglandi ns Other Lipid Mediat 2002;6869:619-31.

PAGE 83

83 Hsu C-H, Hwang K-C, Chao C-L, Chang H-H, Chou P. El ectroacupuncture in overweight women: a randomized, controlled pilot st udy. Journal of Women’s Health 2005a;14(5):434-40. Hsu C-H, Hwang K-C, Chao C-L, Lin J-G, Kao S-T, Cho u P. Effects of electroacupuncture in reducing weight and waist cir cumference in overweight women: a randomized crossover trial. Intl J of Obes ity 2005b;29:1379-84. Huang MH, Yang RC, Hu SH. Preliminary results of tr iple therapy for obesity. Int J Obes Relat Metab Disord 1996;20:830-36. Hui KK, Nixon EE, Vangel MG, Liu J, Marina O, Napad ow V, Hodge SM, Rosen BR, Makris N, Kennedy DN. Characterization of the "deqi response in acupuncture. BMC Complement Altern Med 2007;7:33. James GA, Gold MS, Liu Y. Interaction of satiety an d reward response to food stimulation. J Addict Dis 2004;23(3):23-37. Jansen A. A learning model of binge eating: cue rea ctivity and cue exposure. Behav Res Ther 1998;36(3):257-72. Jimerson DC, Lesem MD, Kaye WH. Low serotonin and d opamine metabolite concentrations in cerebrospinal fluid from bulimic patients with frequent binge episodes. Arch Gen Psychiatry 1992;49:132-8. Johnson WG. Effect of cue prominence and subject we ight on human food-directed performance. J Personality Soc Psych 1974;29:843-8. Joranby L, Pineda KF, Gold MS. Addiction to food an d brain reward systems. Sex Addict Compulsivity 2005,12:201-17. Kalra SP, Dube MG, Pu S. Interacting appetite-regul ating pathways in the hypothalamic regulation of body weight. Endocr Rev 1999;20(1):68 -100. Kalra SP, Kalra PS. NPY and cohorts in regulating a ppetite, obesity and metabolic syndrome: beneficial effects of gene therapy. Neuro peptides 2004a;38:201-11. Kalra SP, Kalra PS. Nutritional infertility: the ro le of the interconnected hypothalamic neuropeptide Y-galanin-opioid network. Front Neuroe ndocrinol 1996;17:371-401. Kalra SP, Kalra PS. Obesity and metabolic syndrome: long-term benefits of central leptin gene therapy. Prous, JR Ed. Drugs of Today. Barcelona, Spain: Prous Science 2002;38:745-57. Kalra SP, Kalra PS. Overlapping and interactive pat hways regulating appetite and craving. J Addict Dis 2004b;23(3):5-21.

PAGE 84

84 Kalra SP. Bagnasco M, Otukonyong EE. Rhythmic, reci procal ghrelin and leptin signaling: new insight in the development of obesit y. Regul Pept 2003;111:1-11. Kelley AE, Baldo BA, Pratt WE, Will MJ. Corticostri atal-hypothalamic circuitry and food motivation: integration of energy, action and rewar d. Physiol Behav 2005;86:773–95. Kelley AE. Memory and addiction: shared neural circ uitry and molecular mechanisms. Neuron 2004;44(1):161-79. Kemps E, Tiggemann M, Woods D. Reduction of food cr avings through concurrent visuospatial processing. Int J Eat Disord 2004;36: 31-40. Killgore WD, Young AD, Femia LA. Cortical and limbi c activation during viewing of highversus low-calorie foods. Neuroimage 2003;19(4):138 1-94. Kleiner KD, Gold MS, Frost-Pineda K, Lenz-Brunsman B, Perri MG, Jacobs WS. Body mass index and alcohol use. J Addict Dis 2004;23(3) :105-18. Kleinhenz J, Streitberger K, Windeler J. Randomised clinical trial comparing the effects of acupuncture and a newly designed placebo needle in rotator cuff tendonitis. Pain 1999;83:235-41. Knight B, Mudge C, Openshaw S, White A, Hart A. Eff ect of acupuncture on nausea of pregnancy: a randomized, controlled trial. Acog J 2 001;97:184-8. Kojima M, Hosoda H, Date Y, Nakazato M, Matsuo H, K angawa K. Ghrelin is a growthhormone-releasing acylated peptide from stomach. Na ture 1999;402:656-60. Kong J, Kaptchuk TJ, Webb JM, Kong JT, Sasaki Y, Po lich GR, Vangel MG, Kwong K, Rosen B, Gollub RL. Functional neuroanatomical inve stigation of vision-related acupuncture point specificity-a multisession fMRI s tudy. Hum Brain Mapp 2009;30(1):38-46. Koob GF, Le Moal M. Plasticity of reward neurocircu itry and the 'dark side' of drug addiction. Nat Neurosci 2005;8(11):1442-4. Kringelbach ML, O'Doherty J, Rolls ET. Activation o f the human orbitofrontal cortex to a liquid food stimulus is correlated with its subject ive pleasantness. Cereb Cortex 2003;13(10):1064-71. Kumanyika SK, Obarzanek E. Pathways to obesity prev ention: report of a national institutes of health workshop. Obes Res 2003;11:126 3-74. Lacey JM, Tershakovec AM, Foster GD. Acupuncture fo r the treatment of obesity: a review of the evidence. Int J Obes 2003;27:419-27.

PAGE 85

85 Lang CH, Ajmal M, Baillie AGS. Neural control of gl ucose uptake by skeletal muscle after central administration of NMDA. Am J Physiol 1995;268: R492-7. Lee MS, Kim JH, Lim H-J, Shin B-C. Effects of abdom inal electroacupuncture on parameters related to obesity in overweight women: a pilot study. Complement Therap in Clin Pract 2006;12:97-100. Lewith GT, White PJ, Pariente J. Investigating acup uncture using brain imaging techniques: the current state of play. Evid Based C omplement Altern Med 2005;2(3):315-9. Li G, Cheung RTF, Ma QY, Yang ES. Visual cortical a ctivations on fMRI upon stimulation of the vision-implicated acupoints. Neu roReport 2003;14:669. Li J. Clinical experience in acupuncture treatment of obesity. J Tradit Chin Med 1999;19:48-51. Li Y, Tougas G, Chiverton SG, Hunt RH. The effect o f acupuncture on gastrointestinal function and disorders. Am J Gastroenterol 1992;87: 1372-81. Lindgren AC, Barkeling B, Hagg A. Eating behavior i n Prader-Willi syndrome, normal weight, and overweight control groups. J Pediatr 20 00;137(1):50-5. Litscher G, Rachbauer D, Ropele S, Wang L, Schikora D, Fazekas F, Ebner F. Acupuncture using laser needles modulates brain fun ction: first evidence from functional transcranial Doppler sonography and func tional magnetic resonance imaging. Lasers in Medical Science 2004;19(1):6-11. Liu Y, Gold MS. Human functional magnetic resonance imaging of eating and satiety in eating disorders and obesity. Psych Annals 2003;33( 2):127-32. Liu P, Qin W, Zhang Y, Tian J, Bai L, Zhou G, Liu J Chen P, Dai J, von Deneen KM, Liu Y. Combining spatial and temporal information to ex plore function-guide action of acupuncture using fMRI. J Magn Reson Imaging 2009;3 0(1):41-6. MacPherson H and Asghar A. Acupuncture needle sensa tions associated with De Qi: a classification based on experts’ ratings. J Altern Complement Med 2006;12(7):663-7. Mansour A, Khachaturian H, Lewis ME, Akil H, Watson SJ. Autoradiographic differentiation of mu, delta, and kappa opioid rece ptors in the rat forebrain and midbrain. J Neurosci 1987;7:2445-64. Mark GP, Weinberg JB, Rada PV, Hoebel BG. Extracell ular acetylcholine is increased in the nucleus accumbens following the presentation of an aversively conditioned taste stimulus. Brain Research 1995;688:184-88.

PAGE 86

86 Meguid MM, Fetissov SO, Varma M. Hypothalamic dopam ine and serotonin in the regulation of food intake. Nutrition 2000;16:843-57 Miller J, Kranzler J, Liu Y, Schmalfuss I, Theriaqu e DW, Shuster JJ, Hatfield A, Mueller OT, Goldstone AP, Sahoo T, Beaudet AL, Driscoll DJ. Neurocognitive findings in Prader-Willi syndrome and early onset morbid obesit y. J Pediatr 2006;149:192-8. Miller JL, Couch JA, Leonard CM, Schwenk K, Towler SD, Shuster J, Goldstone AP, He G, Driscoll DJ, Liu Y. Sylvian fissure morphology i n Prader-Willi syndrome and early-onset morbid obesity. Genetics in Medicine 20 07a;9(8):536-43. Miller JL, Couch J, Schnaulfuss I, He G. Morphologi cal brain abnormalities detected by three dimensional magnetic resonance imaging in Pra der-Willi syndrome. Am J Med Genet A 2007b;143:476-83. Miller JL, James GA, Goldstone AP, Couch JA, He G, Driscoll DJ, Liu Y. Enhanced activation of reward mediating prefrontal regions i n response to food stimuli in Prader-Willi syndrome. J Neural Neurosurg Psychiatr y 2007c;78:615-19. Minokoshi Y, Okano Y, Shimazu T. Regulatory mechani sm of the ventromedial hypothalamus in enhancing glucose uptake in skeleta l muscles. Brain Res 1994;649:343-7. Mogenson GJ. Studies of the nucleus accumbens and i ts mesolimbic dopaminergic affects in relation to ingestive behaviors and rewa rd. In The Neural Basis of Feeding and Reward. Eds: Hoebel GB, Novin D. Brunsw ick, ME, Haer Institute 1982;275-506. Morley JE. Neuropeptide regulation of appetite and weight. Endocr Rev 1987;8:256-87. Morris JS. Involvement of human amgydala and orbito frontal cortex in hunger-enhanced memory for food stimuli. J Neurosci 2001;21:5304-10 Morton GJ, Cummings DE, Baskin DG, Barsh GS, Schwar tz MW. Central nervous system control of food intake and body weight. Natu re 2006;443:289–95. Ng DK, Chow P, Ming S, Hong S, Lau S, Tse D, Kwong WK, Wong MF, Wong WT, Fu YM, Kwok KL, Li H, Ho JC. A double-blind, randomize d, placebo-controlled trial of acupuncture for the treatment of childhood persi stent allergic rhinitis. Pediatrics 2004;114(5):1242-7. Nichols RD, Knepper JL. Genome organization, functi on and imprinting in Prader-Willi and Angelman syndromes. Annu Rev Genomics Hum Genet 2001;2:153-75. Nielsen SJ, Siega-Riz AM, Popkin BM. Trends in food locations and sources among adolescents and young adults. Prev Med 2002;35(2):1 07-13.

PAGE 87

87 Nixon GM, Brouillette RT. Sleep and breathing in Pr ader-Willi syndrome. Pediatr Pulmonol 2002;32:209-17. Noble EP, Blum K, Ritchie T. Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arc h Gen Psychiatry 1991;48:64854. Noble EP, Fitch RJ, Ritchie T. The D2 dopamine rece ptor gene:obesity, smoking and mood. In Obesity Assessment: Tools, Methods, Interp retations. Eds: St. Jeor ST, Koop CE. New York:NY, Chapman and Hall 1997;522-33. Noble EP, Noble RE, Ritchie T. D2 dopamine receptor gene: obesity, smoking and mood. Int J Eat Disord 1994;15:205-17. O’Doherty JP. Reward representations and reward-rel ated learning in the human brain: insights from neuroimaging. Curr Opin Neurobiol 200 4;14:769-76. Ogura K, Shinohara M, Ohno K. Frontal behavioral sy ndromes in Prader-Willi syndrome. Brain Dev 2008;30(7):469-476. Ongur D, Price JL. The organization of networks wit hin the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex 20 00;10(3):206-19. Pagliardini S, Ren J, Wevrick R, Greer JJ. Developm ental abnormalities of neuronal structure and function in prenatal mice lacking the Prader-Willi syndrome gene necdin. Am J Pathol 2005;167:175-91. Parham ES. Compulsive eating: applying a medical ad diction model. In TB Van Itallie and AP Simopoulos, Obesity: new directions in asses sment and management. Philadelphia, PA:The Charles Press 1995. Park J, Park H, Lee H, Lim S, Ahn K, Lee H. Deqi se nsation between the acupunctureexperienced and the nave: a Korean study II. Am J Chi Med 2005;33(2):329-37. Parrish TB, Schaeffer A, Catanese M, Rogel MJ. Func tional magnetic resonance imaging of real and sham acupuncture. Engineering i n Medicine and Biology Magazine, IEEE 2005;24:35-40. Petrovich GD, Canteras NS, Swanson LW. Combinatoria l amygdalar inputs to hippocampal domains and hypothalamic behavior syste ms. Brain Res Brain Res Rev 2001;38(1-2):247-89. Petrovich GD, Gallagher M. Control of food consumpt ion by learned cues: a forebrainhypothalamic network. Physiol Behav 2007;91(4):397403.

PAGE 88

88 Petrovich GD, Ross CA, Holland PC, Gallagher M. Med ial prefrontal cortex is necessary for an appetitive contextual conditioned stimulus t o promote eating in sated rats. J Neurosci 2007;27(24):6436-41. Phillips AG, Ahn S, Howland JG. Amygdalar control o f the mesocorticolimbic dopamine system: parallel pathways to motivated behavior. Ne urosci Biobehav Rev 2003;27(6):543-54. Pissios P and Maratos-Flier E. More than satiety: c entral serotonin signaling and glucose homeostasis. Cell Metabolism 2007;6:345-7. Proto C, Romualdi D, Cento RM, Romano C, Campagna G Lanzone A. Free and total leptin serum levels and soluble leptin receptors le vels in two models of genetic obesity: the Prader-Willi and the Down syndromes. M etabol Clin Experimental 2007;56:1076-80. Qin W, Tian J, Bai L, Pan X, Yang L, Chen P, Dai J, Ai L, Zhao B, Gong Q, Wang W, von Deneen KM, Liu Y. FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network. Molecular Pain 2 008;4(55):1-17. Rada P, Avena NM, Hoebel BG. Daily bingeing on suga r repeatedly releases dopamine in the accumbens shell. Neurosci 2005;134:737-44. Rada PV, Hoebel BG. Supraadditive effect of d-fenfl uramine plus phentermine on extracellular acetylcholine in the nucleus accumben s: possible mechanism for inhibition of excessive feeding and drug abuse. Pha rmacol Biochem Behav 2000;65:369-73. Roitman MF, van Dijk G, Thiele TE. Dopamine mediati on of the feeding response to violations of spatial and temporal expectancies. Be hav Brain Res 2001;122(2):193-9. Rolls ET, McCabe C. Enhanced affective brain repres entations of chocolate in cravers vs. non-cravers. Eur J Neurosci 2007;26(4):1067-76. Rother E, Kohner AC, Bruning JC. Neurocircuits inte grating hormone and nutrient signaling in control of glucose metabolism. Am J Ph ysiol Endocrinol Metab 2008;294:E810-16. Rubin CT, Capilla E, Luu YK, Busa B, Crawford H, No lan DJ, Mittal V, Rosen CJ, Pessin JE, Judex S. Adipogenesis is inhibited by br ief, daily exposure to highfrequency, extremely low-magnitude mechanical signa ls. Proc Natl Acad Sci USA 2007;104(45):17879-84.

PAGE 89

89 Saad MF, Khan A, Sharma A, Michael R, Riad-Gabriel MG, Boyadjian R, Jinagouda SD, Steil GM, Kamdar V. Physiological insulinemia a cutely modulates plasma leptin. Diabetes 1998;47:544-9. Sahu A, Kalra SP. Neuropeptide regulation of feedin g behavior. Neuropeptide Y. TEM 1993;4(7):217-24. Salamone JD, Cousins MS, Snyder BJ. Behavioral func tions of nucleus accumbens dopamine: empirical and conceptual problems with th e anhedonia hypothesis. Neurosci Biobehav Rev 1997;21:341-59. Sato Y, Ito T, Udaka N, Kanisawa M, Noguchi Y, Cush man SW, Satoh S. Immunohistochemical localization of facilitated-dif fusion glucose transporters in rat pancreatic islets. Tissue Cell 1996;28:637-43. Schroeder BE, Binzak JM, Kelley AE. A common profil e of prefrontal cortical activation following exposure to nicotineor chocolate associ ated contextual cues. Neuroscience 2001;105:535–45. Schur EA, Kleinhans NM, Goldberg J, Buchwald D, Sch wartz MW, Maravilla K. Activation in brain energy regulation and reward ce nters by food cues varies with choice of visual stimulus. Int J Obes. 2009;33(6):6 53-61. Schwartz MW, Woods SC, Porte D Jr. Central nervous system control of food intake. Nature 2000;404:661-71. Scislowski PW, Tozzo E, Zhang Y. Biochemical mechan isms responsible for the attenuation of diabetic and obsess conditions in ob /ob mice treated with dopaminergic agonists. Int J Overweight Relat Metab Disord 1999;23:425-31. Seidell JC, Nooyens AJ, Visscher TL. Cost-effective measures to prevent obesity: epidemiological basis and appropriate target groups Proc Nutr Soc 2005;64:1-5. Selikowitz M, Sunman J, Pendergast A. Fenfluramine in Prader-Willi syndrome: a double-blind, placebo-controlled trial. Arch Dis Ch ild 1990;65:112-4. Shafshak TS. Electroacupuncture and exercise in bod y weight reduction and their application in rehabilitating patients with knee os teoarthritis. Am J Chin Med 1995;23:15-25. Shapira NA, Lessing MC, He HG. Satiety dysfunction in Prader-Willi syndrome demonstrated by fMRI. J Neurol Neurosurg Psychiatry 2005;76:260-2. Shiraishi T, Onoe M, Kojima T, Sameshima Y, Kageyam a T. Effects of auricular stimulation on feeding-related hypothalamic neurona l activity in normal and obese rats. Brain Res Bull 1995;36:141–8.

PAGE 90

90 Siedentopf CM, Golaszewski SM, Mottaghy FM, Ruff CC Felber S, Schlager A. Functional magnetic resonance imaging detects activ ation of the visual association cortex during laser acupuncture of the foot in humans. Neurosci Letters 2002;327:53-6. Simmons WK, Martin A, Barsalou LW. Pictures of appe tizing foods activate gustatory cortices for taste and reward. Cerebral Cortex 2005 ;15:1602-8. Singh NN, Lancioni GE, Singh AN. A mindfulness-base d health wellness program for an adolescent with Prader-Willi syndrome. Behav Modif 2008;32(2):167-81. Sobik L, Hutchison K, Craighead L. Cue-elicited cra ving for food: a fresh approach to the study of binge eating. Appetite 2005;44(3):25361. Solinas M, Goldberg SR. Motivational effects of can nabinoids and opioids on food reinforcement depend on simultaneous activation of cannabinoid and opioid systems. Neuropsychopharmacology 2005;30:2035-45. Spring B, Schneider K, Smith M, Kendzor D, Appelhan s B, Hedeker D, Pagoto S. Abuse potential of carbohydrates for overweight carbohydr ate cravers. Psychopharmacol 2008;197(4):637-47. Steiner RP, Kupper N, Davis AW. Obesity and appetit e control: comparison of acupuncture therapies and behavior modification. Pr oceedings: International Forum on Family Medicine Education. Society of Teac hers of Family Medicine, Kansas City, MO 1983;313-26. Stoeckel LE, Weller RE, Cook EW, 3rd. Widespread re ward-system activation in overweight women in response to pictures of high-ca lorie foods. Neuroimage 2008;41(2):636-47. Stux G, Pomeranz B. Acupuncture: textbook and atlas Springer-Verlag: Berlin 1987. Sun Q, Xu Y. Simple obesity and obesity hyperlipemi a treated with otoacupoint pellet pressure and body acupuncture. J Tradit Chin Med 19 93;13:22-6. Sun Y, Asnicar M, Smith RG. Central and peripheral roles of ghrelin on glucose metabolism. Neuroendocrinol 2007;86:215-28. Swaab DF, Purba JS, Hofman MA. Alterations in the h ypothalamic paraventricular nucleus and its oxytocin neurons (putative satiety cells) in Prader-Willi syndrome: a study of five cases. J Clin Endocrinol Metab 1995 ;80:573-9. Swanson LW, Petrovich GD. What is the amygdala? Tre nds Neurosci 1998;21:323–31.

PAGE 91

91 Tartar RE, Ammerman RT, Ott PJ. Handbook of substan ce abuse. Neurobehavioral Pharmacology. NY: Premium Press 1998. Tataranni PA, Delparigi A. Functional neuroimaging: a new generation of human brain studies in obesity research. Obes Rev 2003;4:229-38 Tiggemann M, Kemps E. The phenomenology of food cra vings: the role of mental imagery. Appetite 2005;45(3):305-13. Tsui P, Leung MC. Comparison of the effectiveness b etween manual acupuncture and electro-acupuncture on patients with tennis elbow. Acupunct Electrother Res 2002;27:107-17. Volkow ND, Wang G-J, Fowler JS. “Nonhedonic” food m otivation in humans involves dopamine in the dorsal striatum and methylphenidate amplifies this effect. Synapse 2002;44:175-80. Volkow ND, Wise RA. How can drug addiction help us understand obesity? Nat Neurosci 2005;8(5):555-60. Wang G, Volkow ND, Thanos PK. Similarity between ob esity and drug addiction as assessed by neurofunctional imaging: a concept revi ew. J Addict Dis 2004;23:3953. Wang GJ, Volkow ND, Fowler JS. Enhanced resting act ivity of the somatosensory cortex in overweight subjects. Neuroreport 2002;13: 1151-5. Wang GJ, Volkow ND, Logan J. Brain dopamine and obe sity. Lancet 2001;357: 354-7. Wang GJ, Volkow ND, Thanos PK, Fowler JS. Similarit y between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. Psych Annals 2003;33(2):39-53. Wang GJ, Volkow ND, Thanos PK. Similarity between o besity and drug addiction as assessed by neurofunctional imaging: A concept revi ew. J Addict Dis 2004;23(3):39-53. Weingarten HP, Elston D. The phenomenology of food cravings. Appetite 1990;15(3):231-46. Wenhe Z, Yucun S. Change in levels of monoamine neu rotransmitters and their main metabolites of rat brain after electric acupuncture treatment. Int J Neurosci 1981;15:147–9. Wilson GT. Eating disorders and addiction. Drugs & Society 1999;15:87-101.

PAGE 92

92 Wilson GT. The addiction model of eating disorders: a critical analysis. Adv Behav Res Therapy 1991;13:27-72. Wise RA. Dopamine, learning and motivation. Nat Rev Neurosci 2004;5(6):483-94. Wright P, Albarracin D, Brown RD, Li H, He G, Liu Y Dissociated responses in the amygdala and orbitofrontal cortex to bottom-up and top-down components of emotional evaluation. NeuroImage 2008;39:894-902. Wu MT, Hsieh JC, Xiong J, Yang CF, Pan HB, Chen YC, Tsai G, Rosen BR, Kwong KK. Central nervous pathway for acupuncture stimulation : localization of processing with functional MR imaging of the brain-preliminary experience. Neuroradiology 1999;212(1):133-41. Wurtman RJ, Wurtman JJ. Brain serotonin, carbohydra te craving, obesity and depression. Obes Res 1995;3(4):477S-80S. Yoo SS, Teh EK, Blinder RA, Jolesz FA. Modulation o f cerebellar activities by acupuncture stimulation: evidence from fMRI study. NeuroImage 2004;22(2):93240. Zipf WB, O’Dorisio TM, Cataland S. Pancreatic polyp eptide responses to protein meal challenges in overweight but otherwise normal child ren and overweight children with Prader-Willi syndrome. J Clin Endocrinol Metab 1983;57:1074-80. Zhao M, Liu Z, Su J. The time-effect relationship o f central action in acupuncture treatment for weight reduction. J Tradit Chin Med 2 000;20:26–9.

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93 BIOGRAPHICAL SKETCH Karen Milada von Deneen was born in Ostrava, Czech Republic. She came from a large, well-rounded family who decided to emigrate to the United States in 1984. English was her 4th language at that time out of the 9 that she has st udied so far. Ever since the age of 5, Karen wanted to be a veterinari an and pursue academia. Her family moved all over North America and so have her academ ic endeavors. She graduated from Barstow High School in California as the salut atorian and chose to attend Morehead State University in Kentucky where she obt ained an associate’s degree in veterinary technology, a bachelor’s degree in gener al education, graduated from the highly competitive Honors Program, and played Natio nal Collegiate Athletic Association (NCAA) women’s tennis. In 1998, Karen won the covet ed NCAA Woman of the Year and Ohio Valley Conference Scholar-Athlete of the Y ear. She then obtained her Master of Science in animal sciences with an integrated mi nor in equine reproduction from Oregon State University. She was accepted to the Co llege of Veterinary Medicine and Biological Sciences (CVMBS) at Colorado State Unive rsity to become a veterinarian. It was there that she was exposed to veterinary acupun cture and alternative medicine. In 2004, Karen was recruited by the Department of Path obiology at the University of Florida to begin her doctorate training in immunolo gy and infectious diseases. In 2007, she transferred to the Department of Psychiatry to study neuroimaging and physiology. Her mentor enabled Karen to go to China from 2007 t o 2008 to do alternative medicine research and promote the scientific method at diffe rent universities. Some of her other research orientation has been in food addiction, Pr ader-Willi Syndrome, and Parkinson’s disease. Karen currently has a faculty position at XiDian University in Xi’an, China.