An Investigation of the Common Patterns of Brain Activation in Response to Addiction and Media Events

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An Investigation of the Common Patterns of Brain Activation in Response to Addiction and Media Events
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Klahr,Nelson J
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University of Florida
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Medical Sciences, Neuroscience (IDP)
Committee Chair:
Liu, Yijun
Committee Members:
Petitto, John M
Gold, Mark S
Morris, Jon D
Merlo, Lisa J

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addiction -- adsam -- alcohol -- bold -- craving -- fmri -- smoking -- timing
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Abstract:
Functional MRI has now become the most efficient tool to study functions in the human brain. It is most often used to measure the Blood Oxygen Level Dependent (BOLD) signal that theoretically represents a corresponding proportional level of cognitive activity. Our research group applied this technique in several studies that involved a variety of different forms of addiction and emotional responses to related stimuli and cues. In separate paradigms, we asked participants to watch television commercials, drink a low dose alcoholic beverage, and rate their feelings towards pictures of smoking content while we scanned their brains looking for common patterns and locations of neural activation. The results obtained from each specific study showed that the behavioral data collected evidently reflect the hypothesized imaging data. More importantly, our reported findings also contribute in the efforts towards establishing a medical paradigm observed in patients afflicted with many different forms of addiction by illustrating the roles of the various components involved in modeling this neural network.
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by Nelson J Klahr.
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Thesis (Ph.D.)--University of Florida, 2011.
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Adviser: Liu, Yijun.
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1 AN INVESTIGATION OF THE COMMON PATTERNS OF BRAIN ACTIVATION IN RESPONSE TO ADDICTION AND MEDIA E VENTS By NELSON J. KLAHR A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT O F THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 201 1

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2 201 1 Nelson J. Klahr

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3 I would like to dedicate my dissertation t o my Aunt Alegre Rothstein (1920 2008) and to my friends Philip Schmidt (1972 2010) and Urmil la Antharam (1950 2010) May your souls rest in peace.

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4 ACKNOWLEDGMENTS I would like to express my sincere gratitude to Dr. Yijun Liu and the entire lab group for their assistance and patience in transforming me into a life long learner of fMRI scienc e. My experiences were enriched by working with many colleagues from the past and present including: Dr. Guojun He, Dr. Andy James, Dr. Paul Wright, Dr. Karen von Deneen, Dr. Tang Tianyu, Dr. Zhenyou Zhou, and Dr. Yi Zhang as well as committee members Dr. Mark Gold, Dr. Jon Morris, Dr. Lisa Merlo, and Dr. Jon Pettito. I would also like to thank my close friends and family members for supporting me throughout my graduate studies. In particular, I would like to especially thank my Mom (Judy Klahr), my Dad (Me lvin Klahr), and my Grandmother (Eleanora Semo).

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 COMMON ADDICTION PATHWAYS ................................ ................................ ..... 14 General Definition ................................ ................................ ................................ ... 14 Neuroanatomical Circuitry ................................ ................................ ....................... 1 4 Various Other Forms ................................ ................................ ............................... 16 The Grand Hypotheses ................................ ................................ ........................... 18 2 GENERAL FMRI PRINCIPLES AND APPLICATIONS ................................ ........... 21 History ................................ ................................ ................................ ..................... 21 Understanding and Obtaining the fMRI Signal ................................ ........................ 22 Designing an fMRI Pa radigm ................................ ................................ .................. 24 Analyzing fMRI Data ................................ ................................ ............................... 26 Limitations of fMRI Measurements ................................ ................................ ......... 29 Future Directions ................................ ................................ ................................ .... 31 3 USING THE ADSAM TECHNIQUE AS A MULTIDIMENSIONAL MODEL REPRESENTING EMOTION Using fMRi ................................ .............................. 34 Background ................................ ................................ ................................ ............. 34 Materials and Methods ................................ ................................ ............................ 37 Participants ................................ ................................ ................................ ....... 37 Experimental Protocol ................................ ................................ ...................... 37 Imaging Parameters ................................ ................................ ......................... 40 Data Analysis ................................ ................................ ................................ ... 40 Results ................................ ................................ ................................ .................... 41 Behavioral Data ................................ ................................ ................................ 41 Imaging Data ................................ ................................ ................................ .... 42 Discussion ................................ ................................ ................................ .............. 43

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6 4 THE EFFECTS OF LOW DOSE ALCOHOL ON NEURAL TIMING ........................ 53 Background ................................ ................................ ................................ ............. 53 Materials and Methods ................................ ................................ ............................ 55 Participants ................................ ................................ ................................ ....... 55 Experimental Protocol ................................ ................................ ...................... 55 Imaging Parameters ................................ ................................ ......................... 57 Data Analysis ................................ ................................ ................................ ... 58 Results ................................ ................................ ................................ .................... 59 Behavioral Data ................................ ................................ ................................ 59 Imaging Data ................................ ................................ ................................ .... 60 Discussion ................................ ................................ ................................ .............. 61 5 INVESTIGATING THE NEURAL CORRELATES OF SMOKE CRAVI NG IN THE BRAIN ................................ ................................ ................................ ..................... 77 Background ................................ ................................ ................................ ............. 77 Materials and Methods ................................ ................................ ............................ 79 Partic ipants ................................ ................................ ................................ ....... 79 Experimental Protocol ................................ ................................ ...................... 80 Imaging Parameters ................................ ................................ ......................... 81 Data An alysis ................................ ................................ ................................ ... 81 Results ................................ ................................ ................................ .................... 82 Behavioral Data ................................ ................................ ................................ 82 Imaging Data ................................ ................................ ................................ .... 82 Discussion ................................ ................................ ................................ .............. 83 6 GENERAL SUMMARY ................................ ................................ ........................... 93 LIST OF REFERENCES ................................ ................................ ............................... 97 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 107

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7 LIST OF TABLE S Table page 3 1 Brain activation during commercials with a high level of pleasure ...................... 46 3 2 Brain activation during commercials with a high level of arousal. ....................... 47 4 1 Pre Drink brain activation: Timing > Counting ................................ .................... 66 4 2 Post Drink brain activation: Timing > Counting ................................ ................... 67 4 3 Timing associate d brain activation: Post Drink > Pre Drink ................................ 68 4 4 Counting associated brain activation: Post Drink > Pre Drink ............................ 69

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8 LIST OF FIGURE S Figure page 1 1 The Neuroanatomical circuitry of addiction involving the dopamine and serotonin neurotransmitters. ................................ ................................ ............... 20 2 1 A typical head dedicated scanner. The majority of fMRI expe riments are conducted using a 3 T scanner that can also be used for clinical purposes. ....... 32 2 2 Various accessories used to perform and measure a cognitive task. The Participant responds to a task during an fMRI Experiment. ................................ 33 3 1 The AdSAM manikins in terms of the dimensions: Pleasure, Arousal, and Domi nance. ................................ ................................ ................................ ....... 48 3 2 Results from the AdSAM ratings evaluating the dimension of pleasure. ........... 49 3 3 Results from the AdSAM ratings evaluating the dimension of arousal. .............. 50 3 4 Brain activation during commercials wit h a high level of pleasure. .................... 51 3 5 Brain activation during commercials with a high level of arousal. ....................... 52 4 1 Schemati c of Virtual TL Stimulus. ................................ ................................ ....... 70 4 2 Timeline of Experimental Paradigm. ................................ ................................ ... 71 4 3 Behavioral Data Response Accuracy. ................................ ................................ 72 4 4 Behavioral Data Re action Time ................................ ................................ ......... 73 4 5 Brain activation during Pre Drink; Timing > Counting. ................................ ....... 74 4 6 Brain activation during Post Drink; Counting > Timing ................................ ...... 75 4 7 Brain activation during Timing Task; Post Drink > Pre Drink. ............................. 76 5 1 Examples of the various photographic stimuli used in functional runs. There was a total o f 12 pictures used for each category. ................................ .............. 88 5 2 Scoring in response to craving based on Likert scale. Survey data was acquired outside scanner after last session (n=45). ................................ ........... 89 5 3 The disassociation between craving and liking and the inverse association relation for the emotion of disgust (n=45). ................................ .......................... 90

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9 5 4 Patterns of activation in regions of the brain pertaining to the mesolimbic pathway of addiction. Analysis was based on within condition comparisons across groups (P(corr.)< 0.01). ................................ ................................ ........... 91 5 5 Patterns of activation in regions of the brain pertaining to visual processing and maintain attention. Analysis was based on within group comparisons across conditions(P(corr.) <0.0 1). ................................ ................................ ...... 92

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10 LIST OF ABBREVIATION S ACC Anterior Cingulate Cortex Ach Acetylcholine AdSAM Advertisement Self Assessment Manikins ANOVA Analysis of Variance BA BAC Blo od Alcohol Concentration BOLD Blood Oxygen Level Dependent BVQX Brain Voyager QX DEP Deprived (smoker) DLPFC Dorsolateral Pre Frontal Cortex EPI Echo Planar Imaging FA Flip Angle (MRI parameter) fMRI Functional Magnetic Resonance Imaging FOV Field Of Vie w FWHM Full Width at Half Maximum(Gaussian Analysis) GABA Gamma amino butyric acid GCA Granger Causality A nalysis GFm Medial Frontal Gyrus GLM General Linear Model HRF Hemodynamic Response Function IAPS International Affective Picture System ICA Independen t Components Analysis IFG Inferior Frontal Gyrus IPL Inferior Parietal Lobe

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11 ITG Inferior Temporal Gyrus LCD Liquid Crystal Display LP s Superior Parietal Lobe MFG Middle Frontal Gyrus MTG Middle Temporal Gyrus NAc Nucleus Accumbens NS Non smoker OFC Orbito Frontal Cortex PAD Pleasure, Arousal, Dominance PCC Posterior Cingulate Gyrus PET Positron Emission Tomography PFC Pre Frontal Cortex ROI Regions of Interest RT Reaction Time SAT Satisfied (smoker) SBF Striatal Beat Frequency SFG Superior Frontal Gyrus SMA Supplementary Motor Area STG Superior Temporal Gyrus T Tesla TAL Taliarach (coordinates) TCA Temporal Clustering Analysis TE Echo Time (MRI parameter) TL Traffic Lights (Paradigm model) TR Repetition Time (MRI parameter)

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12 VL Ventrolateral (of thalamus) VTA Ventral Tegmental Area WICA Within condition Interregional Covariance Analysis

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13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor o f Philosophy AN INVESTIGATION OF THE COMMON PATTERNS OF BRAIN ACTIVATION IN RESPONSE TO ADDICTION AND MEDIA EVENTS By Nelson J. Klahr August 2011 Chair: Yijun Liu Major: Medical Science s Neuroscience Functional MRI has now become the most efficient tool to study functions in the human brain. It is most often used to measure the Blood Oxygen Level Dependent (BOLD) signal that theoretically represents a corresponding proportional level of cognitive activity. Our research group applied this technique in several studies that involved a variety of different forms of addiction and emotional responses to related stimuli and cues. In separate paradigms, we asked participants to watch television commercials, drink a low dose alcoholic beverage, and rate their feelings towards pictures of smoking content while we scanned their brains looking for common patterns and locations of neural activation. The results obtained from each specific study showed that the behavioral data collected evidently reflect the hypothe sized imaging data. More importantly, our reported findings also contribute in the efforts towards establishing a medical paradigm observed in patients afflicted with many different forms of a ddiction by illustrating the roles of the various components inv olved in modeling this neural network.

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14 CHAPTER 1 COMMON ADDICTION PAT HWAYS General Definition According to P rofessor Nils Bejerot addiction is defined as an emotional fixation (sentiment) acquired through learning, which intermittently or continually expresses itself in purposeful, stereotyped behavior with the character and force of a natural drive, aiming at a specific pleasure or the avoidance of a specific discomfort (Bejerot, 1980) Addiction has genetic, psychosocial, and environmental component s influencing its development and manifestations. It is characterized by behaviors that include one or more of the following: impaired control over drug use, compulsive use, continued use despite harm, and craving (Bruijnzeel et al., 2004) Physical dependence is a state of being that is manifested by a drug class specific withdrawal syndrome that can be produced by abrupt cessation, rapid dose reduction, decreasing blood level of the drug, and/or administration of an antagonist. Tolerance is the body's physical adaptation to a drug: greater amounts of the drug are required over time to achieve the initial effect as the body "gets used to" and adapts to the intake. From a clinical perspective, addiction can be evaluated in terms of its separate components. One study attempted to categorize the addictive pot ential of various substances by quantifying scores for pleasure experience, psychological dependence, and physical dependence (Nutt et al., 2007) Most relevant to our studies, alcohol and tobacco placed third and fourth respectively behind the most highly addictive drugs of heroin and cocaine. Neuroanatomical Circuitry Addiction i s a primary, chronic disease of brain reward, motivation, memory and related circuitry. Dysfunction in these circuits leads to characteristic biological,

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15 psychological, social and spiritual manifestations. This is reflected in the individual pursuing rewar d and/or relief by substance use and other behaviors. The addiction is characterized by impairment in behavioral control, craving, inability to consistently interpersonal relationships. Like other chronic diseases, addiction involves cycles of relapse and remission. Without treatment or engagement in recovery activities, addiction is progressive and can result in disability or premature death The most commonly accepted theo ry of drug abuse claims that increased dopamine (DA) in limbic brain regions is associated with the reinforcing effects of drugs (Di Chiara and Imperato, 1988) The modern view classifies DA as a neurotransmitter w ith involvement in signaling saliency of events in driving motivational behavior (Volkow et al., 2004) .In general, in contrast to non addicted subjects, addicts have fewer DA receptors available in the striatum may result in decreased output of DA circuits related to rewa rd and hence less sensitivity to natural reinforces. Additionally, decreased metabolic activity in the orbitofrontal cortex during withdrawal and increased metabolic activity in the orbitofrontal cortex during exposure to the abused drug suggests that this region is selectively activated by the drug in the addicted subject and may contribute to an enhanced motivation to take the drug (Volkow et al., 2004) Brain imagining studies have shown abnormalities in the anterior cingulate gyrus, lateral orbitofrontal cortex, and dorsolateral prefrontal cortex that lead to disruptions in the regulation of inhibitory control (Goldstein et al., 2002) MAO B inhibitors or other drugs that increase the amount of DA release in response to DA cell firing might appropriately and successfully compensate for chronic effects of stimulant abuse (George et al., 2003)

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16 When examining the biologica l basis of drug addiction, one must first understand the pathways in which drugs act and how drugs can alter those pathways. The reward circuit also referred to as the mesolimbic system is characterized by the interaction of several areas of the brain. The vent ral tegmental area (VTA) consists of dopaminergic neurons which respond to glutamate (Figure 1 1) These cells respond when stimuli indicative of a reward are present. The VTA supports learning and sensitization development and releases dopamine (DA) into the for ebrain. These neurons also project and release DA into the nucleus accu m bens, through the mesolimbic pathway Virtually all drugs causing drug addiction increase the dopamine release in the me solimbic pathway, in addition to their specific effects. The nucleus accumbens ( NAc ) consists mainly of medium spiny projection neurons (MSNs), which are GABA neurons. The NAc is associated with acquiring and eliciting conditioned behaviors and involved in the increased sensitivity to drugs as addiction progresses. The prefro ntal cortex more specifically the anterior cingulate and orbitofrontal cor tices, is important for the integration of information which contributes to whether a behavior will be elicited. It appears to be the area in which motivation originates and the salience of stimuli is determined. The basolateral amygdale projects into the NAc and is thought to be important for motivation as well. More evidence is pointing towards the role of the hippocampus in drug addiction because of its importance in learning and memory. Much of this evidence stems from investigations manipulating cells in the hippocampus alters dopamine levels in NAc and firing rates of VTA dopaminergic cells. Various Other Forms Addiction is most often classified based on documented vices such as alcohol, tobacco, and various forms of drug substance abuse. However, processing addiction

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17 can also be classified based on overly indulgent and craving behavior in the act of overea ting, gambling, sexual compulsivity, exercise, excessive spending and other detrimental and life consuming engagements. Many experts now concur that addiction results from a combination of genetic and biological factors that interact with environmental fa ctors (Holden, 2001) C urren t research efforts are trying to demonstrate that behavioral addictions such as food and Internet overuse may have similar pathways and patterns of activation in the brain. By analogy to drug addiction, it has been speculated that pathological gambling mig ht also be related to a deficiency of the mesolimbic dopaminergic reward system. A decreased activation of the ventral striatum, which is a hallmark of drug addiction (Volkow et al., 2002) and decreased VMPFC activation, which is r elated to impaired impulse control (Rogers et al., 1999) favor the view that pathological gambling is a non substance related addiction. Many recent studies have shared data attempting to display the vulnerabiliti es of non substance abuse addiction that mimic physical dependence (Frascella et al.) An addiction to food affects as many as 4 million U.S. adults and is strongly linked to depression. About 15% of mildly obese people are compulsive eaters. Binge eating, thought to be the most common eating disorder in America, is considered bulimia when a person purges to lose weight (Avena, 2009) Two million American adults are considered pathological gamblers who continue to wager regardless of the consequences (Potenza and Winters, 2003) Their moods generally follow the arc of their winning and losing. An. At least 1 in 20 Americans is considered a compulsive shopper and the addiction af fects both genders almost equally. Cultural factors, like

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18 advertising emphasis on the happiness that products elicit, are thought to be the fuel of addictive purchasing (Knutson et al., 2007) Sex addicts have been observed to display many behavioral characteristics of addiction. They obsess about whatever their favorite practice is, never get enough, feel out of control, and experience serious disruption of their lives because of it (Holden, 2001) Similar to cocaine addicts, many sex addict s have admitted overriding feelings of (Holden, 2001) Studies with normal subjects have indicated that brain activity associated with sexual arousal looks like that accompanying drug consumption (Carnes and Schneider, 2000; Merlo et al., 2008) The mesolimbic dopamine system plays a critical role in the motivated and rewarding aspects of sexual behavior. The dopaminergic projection neurons (DA) in the ventral t egmental area (VTA) are likely under tonic inhibition by local GABAergic interneurons. Stimulation of the Gi/o coupled mu opioid receptor (MOR) results in the inhibition of these GABAergic neurons, which in turn leads to disinhibition of dopaminergic proje ction neurons and the subsequent release of DA into the nucleus accumbens (NAc) (Balfour et al., 2004) Neuroanatomical evidence has demonstrated that the mesolimbic system is activated by both sexual behavior and exposure to sex related environmental cues. In humans, drug addicts often report intense bouts of craving following exposure to drug associated environmental cues, while in rodents, exposure to drug associated cues leads to conditioned activation of the mesolimbic dopamine system (Duvauchelle et al., 2000) The Grand Hypotheses Overall, my long term goals and objectives can be summarized by stipulating three major aims:

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19 i) To measure the changes in brain activation in respons e to the way we receive and process media information and communicate emotions. ii) To show correlations between behavioral data acquired from relevant cognitive tasks and fMRI data through paradigms that involved both Top Down (driven by knowledge or intentio n) and Bottom Up (driven by stimulus content) processing. iii) To confirm the roles of various components from established neural circuits involving proposed models for the processing of emotion, neural timing, and addictive craving. These ideas can be integrat ed together and more simply stated by claiming that t here are common patterns of brain activation which can be identified in response to addictive and emotional stimuli using fMRI. The ultimate goal of such research is to help Psychiatrists to better under stand the cognitive functions of patients at various stages of diagnosis and treatment and thus further advance the technological capabilities in the field of addiction medicine.

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20 Figure 1 1 The Neuroanatomical circuitry of addiction involving the do pamine and serotonin neurotransmitters.

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21 CHAPTER 2 GENERAL FMRI PRINCIPLES AND A PPLICATIONS History Although functional magnetic resonance imaging, or fMRI, is a relatively new technique, the concept of localizing functions in the brain can be traced back to the phrenologists of the 18 th century. They falsely believed that bumps in the skull correlated with men t al capabilities Current data demonstrate that one mental activity may engage a network of brain regions and that a single brain region may perform multiple tasks or processes In addition, it is now understood that there are not simple one to one relationship s between components in the brain and cognitive functions. The field of functional neuroimaging has been advanced by clinical cases of patients with brain lesions. Perhaps t he most famous example is Paul Broca who suffered from a terrible speech impediment. When his brain was dissected after his death lesions in the left prefrontal lobe were reported to be very severe. Today, this portion of th is implicated in our ability to develop language related skills and speak coherently. Of course experimenta l studies that involve purposeful production of a brain lesion for the purpose of studying its effect on behavior are limited to animal subjects. These studies also fail to demonstrate interactions within a neural network of brain regions. As a result, other methods are needed to adequately study brain behavior relationships. E lectroencephalography, or EEG is one mod ern technique that is used in functional neuroimaging Electrical signals are obtained from electrodes placed directly on the scalp in close proximity to the action potential fired from neurons The main advantage of EEG is the excellent temporal resoluti on and the main disadvantage is the

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22 poor spatial resolution. EEG data can be acquired within milliseconds but can only accurately distinguish centimeters of brain tissue. As a result, P ositron E mission T omography or PET is arguably the chief rival of fMR I in current applications Here, cerebral blood flow is measured by tracing the metabolism of radioactive glucose PET may present results more clearly than fMRI in terms of identifying neuroanatomical spacing; however, the lagging delay between neuronal f iring and the radioactive isotopes colliding prevents accurate measur ement of the neurophysiologic response to various cognitive tasks which take s place within seconds. In addition this invasive procedure is considered to have a far greater potential of harm than fMRI The first fMRI study was reported in 1992, which showed the BOLD signal in the occipital lobe to fluctuate in response to a visual stimulus (Kwong et al., 1992) A similar study at that time showed that the increased signal from the visual stimulus was delayed for about 2 3s (Blamire et al., 1992) This finding led to a key foundation principle known as the BOLD hemodynamic response (HDR). The HDR is dependent on changes in blood o xygenation as more oxygen presumably predicts changes in neural activation. Many scientists in the field of functional neuroimaging soon realized that the best way to corroborate their findings was to compare the findings that examined similar brain behav ior emotion relationships from a variety of different methods, especially with the new ly developed fMRI technology. Understanding and Obtaining the fMRI Signal FMRI attempts to measure brain activ ation by detecting changes in blood oxygenation in response to cognitive tasks Th ese data are calculated by contrasting the MR signal during a period when the subject engages in a task condition compared

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23 to a rest ing period in which the subject views a default screen The brain r egions that show a significant diff erence in the obtained signal may be postulated as part of the neural network responsible for the cognitive functions involved in performing the given task. The applications of M RI physics are founde d on the concept of acquiring information about the elec tromagnetic energy that is released and absorbed from the excit ation of hydrogen nuclei through their return ing state of equilibrium. Its magnitude is determined by the following equation: S(t) = S 0 e t/T2* Where S = signal, t = time, S 0 = signal at t=0, an d T2* = decay constant. The variable T2* is a factor of the local inhomogeneities or imbalanced regions within the magnetic field. Such effects may arise from the interaction between air, water, and/or the presence of paramagnetic materials. Variations in T 2* a r e time dependent and caused by the local concentration of deoxygenated hemoglobin which has an effect on the local magnetic field. An increase in the concentration of oxygenated hemoglobin thus increases the MR signal Ogawa tested this hypothesis us ing rats by controlling the levels of blood oxygenation in their brains (Ogawa et al. 1990) By altering inhaled carbon dioxide, blood glucose level, and level of anesthesia, Ogawa and colleagues demonstrated that BOLD contrast depended upon both cerebral blood flow (supply) and cerebral metabolism (demand). T his phenomenon was then r eferred to as blood oxygen ation level dependent or BOLD Several complex mathematical algorithms including Fourier transformations are used to reconstruct the data obtained from th is BOLD signal in K space and into a

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24 graphical representation of its three dimensional location. The main magnet ( between 1.5T 4T ), the gradient coils, and the RF coils all contribute to the process of incorporating their frequency and phase encoding ( Figure 2 1) The geometry of these images can be separated into slices within the axial, coronal, or sagit t al planes. The MRI data can then be further analyzed in terms of three dimensional cubic voxels also referr ed to as volume elements. These individualize d boxes of data currently have a spatial resolution of approximately 4mm based on the limitations of T2 weighted MR images and a temporal resolution of about 2 3 seconds as the timing of the BOLD response is delayed in respect to the firing of neural synaptic activation. Designing an fMRI Paradigm An fMRI study may address a s ingle question, several unrelated questions, or a combination of questions related to neural activation in the brain. Examples include: How is information processing implemented in the brain; H ow are these processes organized in neural tissue? When are par ticular processes and structures invoked and under what circumstances or patholog ical conditions? How can one use patterns of activation to infer that specific processes or structures were invoked? In answering these questions, the fMRI scientist must orga nize the study so that the tasks and trials performed by the participant disentangle the desired brain based cognitive operations with the goal of isolat ing the processing roles of specific brain areas The participants can be exposed to a variety of stim uli engage in related multidimensional tasks and be grouped according to reported physiological conditions. While inside the scanner the participants can view pictures or videos on a screen listen to music or auditory linguistic information with headph ones, and respond to multiple choice questions and surveys with a button response glove. The majority of fMRI facilities are equipped with

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25 instrumentation that integrates these devices (Figure 2 2) Prior to scanning, participants can also be given specifi c dietary instructions or in cases of clinical studies directions related to regulating pharmacological treatment The selection of participants is perhaps the most underrated aspect of conducting an fMRI study because the time and effort invested is usu ally overlooked and taken for granted Ideally, participants are cooperative and reflective of the sub population that they are representing. The fMRI researcher must be able to select participants that best fit the criteria meeting the conditions set for th and refrain from selecting participants with a potential bias or preconception. But most importantly, the fMRI researcher must be very clear in explaining the safety precautions involved in the experiment and with the expectations and guidelines of the task. The two most commonly employed fMRI paradigms are the block design and the event related design. In the block design, a series of trials of the same condition are presented consecutively for an extended period of time and separated into distinct bl ocks by rest periods. It is also referred to as a box car design because of the box car shape that it resembles. In an event related design, the series of trials are separated by periods of rest into discrete, short duration events whose order of presentat ion is randomized. The block design has the advantage of producing a stronger signal and avoids scanner drift. Its main disadvantage is that the participants are inclined to anticipate upcoming similar stimuli as they are repeated. The main advantage of th e event related design is that it avoids this expectancy ; but it also has the disadvantage of needing many repetitions of each trial to acquire a reliable signal and the BOLD responses to these trials may overlap. Both of these designs may also incorpora te

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26 jittering, which randomizes the rest period intervals between consecutive trials. The type of task the participants perform, the hypothesis and aims of the study, and the various logistics and conditions involved will all influence the design of the exp erimental paradigm. An alyzing fMRI Data Another critical issue in the design of the fMRI cognitive task paradigm is the statistical analysis of the fMRI response to the task. However, the imaging data must first undergo several steps of preprocessing befor e any statistical maps can be generated. The functional images acquired from each subject are first co registered with the 3D (T1 weighted) anatomical images. The resulting 3D functional data are then normalized into Talairach space (Talairach and Tournoux, 1988) Scripts coded in MATLAB will usually then perform progra ms that enable functions such as motion correction, slice scan time correction, and linear trend removal to efficiently compute across entire data sets. Depending upon the scanning parameters used, usually bandwidth filtering that cuts off high pass and lo w pass frequencies are used for temporal smoothing and a Gaussian filter of 5.7 mm full width half maximum is used for spatial smoothing (Friston and Ashburner, 2004) Normally, the first two functional volumes are discarded because of their T1 saturation. Voxel wise statistical activation maps are overlaid onto the 3D anatomical images and the statistical value s are r epresented by a range of colors. This lengthy and complicated but worthwhile process is e xplain ed in the following paragraphs below. The BOLD HDR will usually rise to a smooth curve for approximately 2 3 seconds upon presentation of a stimul us and decline for 3 5 sends afterward. Applying the technique of temporal smoothing reduces the lag between n eural activity and

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27 subsequent hemodynamic changes. Two or more unique BOLD responses may be identified by subtracting the measured signal from a single variable assuming that the BOLD responses add roughly linearly (Friston et al., 1996) The principle of linear insertion (Dale and Buckner, 1997) allo ws the BOLD HDR to be predicted with mathematical models. The General Linear Model (GLM) is a class of statistical tests that assume that the experimental data are composed of the linear combination of different model factors, along with uncorrelated noise The solution to the general linear model for a given voxel is given in the following equation: x M x y M y (t) + e(t) task = beta weight for task x or y, M = modeled hemodynamic response for task x or y, and e = error or baseline. The beta weight is a parameter that represents the magnitude of the BOLD HDR response function by considering its relative contribution to the observed data compared to the estimated baseline condition (Boynton et al., 1996) The GLM reference function computes a weigh ted combination of the various predictors (beta weights) Contrasts between the variable predictors (beta weights) are then used to calculate the relative contribution of each condition to the variance in t he percentage changes in BOLD signa l of the fMRI data series at each individual voxel and for each task condition Based on the experimental paradigm, the fMRI researcher will construct a design matrix, denoted as G, specif ying how the parameters of the GLM will change with respect to time. There are many different methods for analyzing the statistical data. The two most commonly applied are the fixed effects approach and the random effects approach. A fixed effect s analysis assumes that the experimental effect is constant (fixed) across the

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28 subject population. Thus, t he experimental manipulation should have the same effect on all subjects with differences between subjects caused by random noise The data are combined across subjects and then at each voxel the statistical significance of the response to each task condition (or contrast between predictors) is calculated using the technique of least squares fitting. The main disadvantage of this approach is that it is open to contamination because only a single beta weight is used for the whole group. T he results can be greatly skewed from only 1 2 subjects within a small sample who may display extremely strong BOLD responses incorrectly implying that the entire group also displays this outcome. An alternative approach is the random effects anal ysis Here, it is assume d that the effects of the experimental manipulation will vary across subjects with a different effect on each individual subject. Thus, a t each voxel for each task condition, all of the beta weights are calculated individually for e ach subject and not for the entire group as a whole. First, s tatistical maps are created for each subject, and then the output of those statistical tests is subjecte d to a second level of analysis by performing a t test on the sample of beta weights The o nly drawback to this approach is that i t requires a larger sample size in order to obtain a respectable statistical power. In normal circumstances the number of degrees of freedom is equal to number of participant s multiplied by the number of data volumes in each fMRI run in a fixed effects analysis; whereas the degrees of freedom is equal to the number of participants in a random effects analysis. Applying the GLM approach to analyzing imaging data is most common and is used by the vast majority of commer cialized software programs. However, more advanced techniques have been developed that allow for more specific and detailed

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29 investigations. Sometimes a region of interest (ROI) analysis will be performed within selected clusters of significantly activated voxels based on a predetermined section of the brain related to the apriori hypothesis for the fMRI study Measuring the standard errors of each time point is critical in determining whether the activation could have resulted from noise, head motion, or other bias irrelevant to the aim of the specific cognitive task being evaluated. In the latter stages of data analysis, the statistical threshold may be adjusted to achieve the maximum output of results and further eliminate areas where the BOLD response was less significant. A threshold of p < 0.05 with Bonferroni correction is most frequently used in the fMRI literature. Applying such criteria to a normal sized brain that contains over 20,000 voxels will yield approximately 1,000 false positive results. A n overview p ost hoc analysis should always be implemented to assess the validity of the various clusters of activation and to confirm the statistical significance of the findings Our lab group has also made significant contributions to this fie ld in terms of developing techniques to analyze functional connectivity. Among these approaches include Temporal Clustering Analysis (TCA) (Liu et al., 1999) Within condition Interregional Covariance Analysis (WICA) (He et al., 2003) and Granger Causality Analysis (Zhou et al.) Limitations of fMRI Measur e ments Another important goal of designing a high quality fMRI paradigm is to achieve a high functional contrast to noise ratio. But errors in accurately measuring the BOLD signal can arise from many different sources. Thermal noise (also referred to as in trinsic noise) involves fluctuations in the intensity of the signal over space or time caused by thermal motion of electrons within the sample or scanner hardware. Physiological noise involves fluctuations in the intensity over space and time caused by phy siological

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30 activities such as respiration, cardiac activity, and metabolism. Motion artifact occurs due to the movement of the head during the scanning period. Scanner drift refers to slow changes in voxel intensity over time due to slow changes in tempera ture in the Although some of these errors are unavoidable, a diligent fMRI researcher will carefully select the control conditions and thoroughly analyze the data in order to remove as much of the non task related neural activity as possible. While fMRI may be perceived as an invaluable instrument in the quest of human brain mapping, its limitations and confounds continue allow the detractors of the field to criticize the extent of its current and future applications. Many scientis ts have questioned whether the changes in the concentration of oxygenated hemoglobin and hence percent age changes in BOLD signals arising from a given cognitive task can accurately and appropriately define the neural correlates in the brain associated with the subsequent changes in neural activation arising from the given experimental condition. Interpreting this concept requires investigating t he coupling relationship between oxygenated blood flow ing to wards a site where neurons are propagat ing action pote ntials. Considering that the ATP used as a cognitive resource must eventually be replenished through aerobic respiration, a demand for glucose and oxygen brings about a change in the concentration of oxygenated hemoglobin, as discussed earlier. The postula ted theory relies on models such as the astrocyte neuron lactate shuttle (Shulman et al., 2001) that supports the claim by justifying the energy demands in clearing glutamate from the synaptic cleft Although the basic principles of neurophysiology validate the interpretation of the BOLD HDR functio n with little doubt

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31 the exact location, timing, and true significance of what the findings from an fMRI study actually represents continue to be debated and discussed in the fMRI literature Future Directions In summary, many of the limitations involved w ith analyzing fMRI data can be minimized with a carefully designed paradigm and with a detailed and professional interpretation and presentation of the data Several fMRI experts believe that the current state of the field is on the brink of an explosive e xpansion, and that the next decade will bring about numerous applications to society (Logothetis, 2008; Miller, 2008) Already, several countries have been using fMRI evidence in legal proceedings and it has been ga ining much popularity as tool for lie detection. Some mental health professionals have be gun using fMRI data obtained from their patients to evaluate the progress of therapy by comparing the before and after treatment states relative to the salient issue o f their diagnosed problem. Some educators are now calling for using fMRI to categorize the various pathways of learning and memory and thus assisting in the development of differentiated instruction. If fMRI can be used to effectively describe the levels o f sexual arousal, romantic love, and attitudes towards commercial brands then obviously the future unchartered waters should be profound and deep and full of unex pected surprised discoveries.

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32 Figure 2 1 A typical head dedicated scanner. T he majority of fMRI experiments are conducted using a 3 Tesla MRI scanner that can also be used for clinical purposes.

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33 Figure 2 2 V arious accessories used to perform and measure a cognitive task. The Participant responds t o a task during an fMRI Experiment.

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34 CHAPTER 3 USING THE ADSAM TECH NIQUE AS A MULTIDIME NSIONAL MODEL REPRESENTING EMOTIO N USING FMRI Background Researchers have used a variety of self report techniques in analyzing the emotional responses to commercials. Some have used a discrete self report approach that focused on specific emotions such as happiness and anger (Izard, 1977; Plutchik, 1984) Other researchers have used a more robust three dimensional self report ap proach (Osgood et al., 1957; Russell and Mehrabian, 1977; Sundar and Kalyanaraman, 2004) including physiological measures to assess the emotional response. A consolidation between self report and physiological meas urements would provide convergent validity for both methods, but a link between the two measures of emotion in response to marketing communications has not been fully explored yet In this study, we proposed that the three dimensional self report approach to measuring emotion would be an effective methodological tool that corresponds to key physiological functions in the brain. Such findings could provide a promising new perspective for investigating issues that have been unexplored or vaguely defined in pr evious neuroimaging studies. The search for physiological links to emotion reflects an approach that seeks fundamental or discrete emotions (Mandler, 1984) Subcortical emotional responses have been recorded through classical conditioning of fear reactions to audio or visual stimuli (LeDoux et al., 1989) The responses either interrupt the cognitive focus of current attent ion or influence the context for ongoing cognitive processes (Simon, 1982) Pleasant and unpleasant emotional responses were found related to increases in the neural activity in the medial prefrontal cortex (mPFC), thalamus, and hypothalamus,

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35 while unpleasant emotions were associated with the neural activity i n the occipitotemporal cortex (OTC), parahippocampal gyrus, and amygdale (Lane et al., 1997) Additionally, facial expressions of disgust or anger were found to increase the neural activity in the left inferior frontal gyrus (Sprengelmeyer et al., 1998) and anterior insular cortex (Phillips et al., 1997) A meta analysi s of emotion activation studies in PET and fMRI (Phan et al., 2002) concluded that no single brain region is activated by all emotions, and no single brain region is activated by one particular emotion. The discrete approach assumes categorical judgment of emotional stimuli. This requires connections between both hemispheres (Bowers et al., 1991) and between the anterior cingulated cortex (ACC) and the bilater al prefrontal cortices (Devinsky et al., 1995) Hence, certain brain regions may become activated from the demand to categorize or label discret e emotions rather than becoming activated from the natural emotional responses to given stimuli. Furthermore, most neuroimaging studies treat emotions as two discrete categories pleasant and unpleasant while ignoring the nuances along the pleasure dime nsion and the additional explanatory power of the arousal and dominance dimensions. For example, the intensity of fear has been associated with brain activities in the left inferior frontal gyrus (Morris et al., 1998) while anger and disgust have been associated with different degrees of intensity or arousal (Ii daka et al., 2001) The alternative three dimensional approach to emotion attempts to simplify the representation of responses by identifying a set of common dimensions that can be used to distinguish specific emotions from one another. This approach inc ludes both verbal and non verbal measures (Osgood et al., 1957; Russell and Mehrabian, 1977;

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36 Lang, 1980) and has been largely ignored in previous research. One example of this approach is the P leasure displeasure, A rousal calm and Dominance submissiveness (PAD) model (Russell and Mehrabian, 1977) The three bi polar dimensions are independent of each other, and the variance of emotional responses can be identified with their positions along these three dimensions. The dimensional appr oach helps differentiate emotions postulated by the discrete approach (Shaver et al., 1987) by providing a numeric level of each dimension to describe the specific emotions. Each discrete emotion can be identified by specific combinations of the dimensions. The meaning of these specific adje ctives may differ by individual, culture, or other influences; nevertheless, the method for identifying the response is universal. The current study was designed to assess the validity of the dimensional approach for measuring emotional responses by comp aring them to the brain responses obtained through neuroimaging. The neuroimaging data w ere derived from blood oxygenation level dependent (BOLD) signals detected with fMRI. The n euroimaging analysis targeted the amygdal a prefrontal cortex (PFC), and temp oral cortex for several reasons. First, the PFC has been found to have neural projections to the amygdal a (McDonald et al., 1996) and the te mporal cortex has been found to send stimulus information to the PFC (Hasselmo et al., 1989) Second, the temporal cortex has been found to allow the brain to merge perceptual and semantic information, past memories and short term manipulation of the stimuli (Mitchell et al., 2003) Third, both the semantic information and the dynamic nature of video clips have been found to increase neural a ctivity in the PFC (Adolphs, 2002; Decety and Chaminade, 2003)

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37 The self report data were derived from responses to the Advertisement Self Assessment Manikins (AdSAM ) scale (Morris et al., 2005) This scale provides a nonverbal, cross cultural, visual measure of emotional response that measures the dimensions of pleasure, arousal, and dominance. Therefor e we suggest it is a better tool than a verbal technique that requires respondents to cognitively translate their reactions into words before reporting their feelings We postulate that this methodology, grounded in psychological literature since the 1950s should be the basis of emotional detection in the brain. Materials and Methods Participants Twelve healthy, right handed young adult participants (6 males/6 females, age range 22 28, mean age 24.8) signed written, informed consent for the protocol appr oved by the Institutional Review Board at the University of Florida. At the time of the scan, none of the participants reported taking any psychiatric medication or having any history of neurological disorders. All of the participants stated having either normal vision or corrected to normal vision, and three of the participants used specialized, non magnetic corrective lenses inside the scanner. All participants were financially compensated $50.00 USD. Experimental Protocol The participants viewed five co mmercials inside of the scanner in a block design paradigm created with E Prime (Psychology Software Tools, Pittsburgh, PA). The commercials were presented by back resolution of 1024 pixels by 768 pixels through the Integrated Functional Imaging System (IFIS, MRI Devices, Inc., Waukesha, WI). An MRI compatible auditory system

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38 (Resonance Technology, Inc.) with stereo earphones and a microphone protected participants from scanner noise while permitting verbal communica tion with the operator and transmitted the sound of the commercials. The first two commercials lasted 30 seconds each and the other three commercials lasted 1 minute each. Resting blocks consisting of viewing a red cross on a black screen for duration of 30 seconds were interspersed between each commercial block. The functional scan consisted of six runs with each run (except for the initial resting state run) being separated into three blocks: 1) a resting period, 2) a task of viewing a commercial, and 3 ) the AdSAM task. The initial run started with a 30 second the participants performed the AdSAM task, which consisted of three trials of rating pleasure, arousal, and dominance. The participants were asked to convey their feelings in terms of pleasure (happy vs. sad), arousal (stimulated vs. bored), and dominance (in control vs. cared for) by selecting the most appropriate Self Assessment Manikin out of five possible choices immediately after viewing e a ch commercial. The responses and reaction times were recorded with a right handed button response glove (IFIS, MRI Devices, Inc., Waukesha, WI). The participants were instructed to indicate how they felt after watching each commercial by rating the AdSAM scales without spending a lot of time thinking about the questions. They participated in an AdSAM training on the same day before beginning the scanning and were explained how to best interpret the manikin figures For the training, the participants practiced the task by answering

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39 questions regarding their general feelings and their feeling towards viewing a commercial for V8 vege table juice outside of the scanner. The five commercials utilized for the study were all originally broadcasted more than ten years ago to avoid the likelihood of the participants having previously seen the m on television. The first commercial was a public service ann ouncement called Be a Hero. It shows happy looking children answering a question Who is my hero? Some name famous people but one boy names his teachers. The commercial urges those interested to get involved in teaching and make a difference. The secon d commercial was from the spring water company Evian. This commercial portrays beautiful mountains, blue sky and the sun sparkling on snow in the French Alps. The third commercial was from the soft drink Coke. It shows a young boy offering his Coke bottle outside the locker room after a football game to Mean Joe Greene of the Pittsburgh Steelers. At first Mean Joe Green politely declines but then changes his mind, accepts the Coke bottle, and passes his jersey to the young boy as a return gift. The fourth c ommercial was for the sports drink Gatorade. It showcases special effects of a 23 year old Michael Jordan, in a Chicago Bulls uniform, playing the modern day Michael Jordan in a one on one grudge match. A 1987 version of Jordan's head was digitally "place d" on the torso of an actual performance double playing against the real 39 year old Jordan. The two engage in the one on one basketball, which shows the older but wiser Jordan mentoring his younger, more energetic self. The fifth and final commercial wa s and an Anti fur public service announcement. The commercial starts with scenes from a fashion show with several models showcasing fur coats. The spectators are clapping and admiring the fur coats. As one model turns around there is blood dripping

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40 from her coat and it is splattered all over the spectators who are now terrified and disgusted. As the model leaves, there is a bloody trail behind her. It closes with and an announcement: It takes up to 40 dumb animals to make a fur coat but only one to wear it. Imaging Parameters A 3T head dedicated MRI scanner (Siemens Allegra; Munich, Germany) was used. Functional images were acquired with a gradient echo EPI pulse sequence sensitive to the BOLD signal using the following parameters: TR=3.0s, TE=30ms, FA=9 0 Matrix size=64x64, FOV=240mm, 36 axial slices with a slice thickness of 3.8mm without gaps. The first two functional volumes were discarded because of their T1 saturation. For structural coregistration with the functional images, T1 weighted 3D anatomi cal images were acquired with a MPRAGE sequence in the following parameters: TR=1.5s, TE=4.38ms, FA=8 Matrix size =256x256; FOV=240mm, 160 slices, slice thickness =1.1mm. Data Analysis BrainVoyager v. 4.9.6 (Brain Innovations, Maastricht, Holland) was u sed to analyze all of the imaging data. The functional images from each participant were first co registered with the 3D anatomic images and then normalized into Talairach space. The resulting 3D functional data then underwent motion correction, linear tre nd removal, spatial smoothing (5.7 mm FWHM Gaussian filter). A general linear model (GLM) produced voxel wise statistical activation maps. The predictors were estimated hemodynamic responses to the tasks. Contrasts between the predictors were used to evalu ate the relative contribution of each condition to the variance in the BOLD signal. A

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41 statistical threshold was set to p < 0.052 corrected with a minimum cluster size of 150 voxels when comparing the BOLD signal between the tasks. ROIs were analyzed for se lected clusters of significantly activated voxels. Within each ROI, the BOLD responses for each condition were visualized using time locked averaging of the percentage signal change relative to the baseline resting condition. We used the method of a group split in order to integrate the behavioral data into our approach to the imaging data. We set up contrasts with low pleasure ads on one side and high pleasure on the other and then similarly with low arousal ads vs. high arousal ads. We split the stimuli b ased on the AdSAM behavioral scores and compared activation brain activity between stimuli on each side of the split. Results Behavioral Data As shown in Figure 3 1, each of the five television commercials was rated on a five point AdSAM scale of Pleasure and Arousal and Dominance and was averaged from all twelve subjects (see Methods). The lowest mean rating from Pleasure was scored from the Anti Fur commercial, 2.7 1.1, (mean sd) and it was significantly lower than the mean ratings from all of the ot her four commercials ( Figure 3 2 ). In the ratings for Arousal, the mean scores from the Anti Fur commercial (3.9 0.9) and Gatorade commercial (3.7 0.9) were significantly higher than the scores for the Teacher commercial (2.9 0.6) and Coke commercial (2.9 0.8) ( Figure 3 3 ). In the ratings for Dominance, the mean scores for all the five commercials did not significantly differ from one another; therefore, they are not shown. (p value < 0.05)

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42 Imaging Data We compared changes in the BOLD signal for the combined blocks viewing the Teacher, Evian, Coke, and Gatorade commercials relative to the viewing of the Anti Fur commercial using strict threshold criteria of p(corr.) < 0.052 and a minimum cluster size of 150 voxels. We observed BOLD signal increases i n the bilateral inferior frontal gyri [IFG/Brodmann Area (BA) 47] and bilateral middle temporal gyri (MTG/BA 21) and BOLD signal decreases in the right superior parietal lobe (BA 7, see Table 3 1 for details). There are three steps in our selection of reg ions of interest ( ROIs) in the Pleasure dimension First, we acquired beta weights with the above mentioned general linear model (GLM) on the imaging data to search for significantly activated regions, which are listed in Table 3 1. Beta weights are estima tes of the fMRI hemodynamic responses to the modeled condition. Second, we contrasted the beta weights to the behavioral data collected through AdSAM to examine the correspondence between the two types of measures. Third, we used the time locked average r esponse plots of significantly activated regions to identify ROIs where the patterns of activations also highly corresponded to the pattern of the AdSAM behavioral data for the pleasure dimension. By following those three steps, we selected to present the bila teral IFG and bilateral MTG in Figure 3 4 for the pleasure dimension. We also compared changes in BOLD signal for the combined blocks of the Anti Fur and Gatorade commercials relative to the viewing of the Teacher and Coke commercials using the same s trict threshold criteria of p(corr.) < 0.052 and a minimum cluster size of 150 voxels. We observed several areas with BOLD signal increases in the left hemisphere regions including the middle frontal gyrus (MFG/BA9), superior

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43 frontal gyrus (SFG/BA10), midd le occipital gyrus (BA19), inferior temporal gyrus (ITG/BA37) and thalamus, and in the right hemisphere regions including the cerebellum, middle frontal gyrus (MFG/BA10), and optic radii ( Table 3 2). We again used the above mentioned three steps to select dimension and correlated brain activation in the right MFG and right superior temporal gyrus (STG). As shown in Figure 3 5 the time locked average responses in these ROIs corresponded well to the AdSAM behavioral data of arousal. Discussion In this study, we evaluated emotional responses based upon our hypotheses that the processes involved with AdSAM and PAD ratings can be observed functioning in the brain. The empirical evidence supported the identification of regions of the bra in that correspond to both the pleasure displeasure and arousal calm dimensions of the PAD model of emotions (Russell and Mehrabian, 1977) Furthermore, the self report data generated through the AdSAM measure correlated well with the fMRI data. The AdSAM pleasure scores of four stimuli (Teacher, Evian, Coke and Gatorade) were significantly higher than that of the Anti Fur commer cial ( Figure 3 2 ). The disturbing content of the Anti Fur Commercial that included a scene showering blood most likely contributed to this intense un pleasant emotion. When the imaging data of the first four commercials were contrasted to those of the Anti Fur commercial, significant differences were identified in brain regions that are known to be associated with emotional valence. These regions includ ed the bilate ral IFG and the bilateral MTG ( Figure 3 4 ) which have been found to be associated with emotional responses (George et al., 1996; Sprengelmeyer et al., 1998) Activation in the amygdal a is often intertw ined with activation of these two areas of the PFC (McDonald et al., 1996) ; however, in this

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44 study, no significant activities were detected in the amygdal a Previous research indicates that the activation of the amygdal a can be substantially reduced by an explicit request for emotional recognition (Nomura et al., 2003; Wright and Liu, 2006b) It is pos sible that the activity in the amygdal a was habituated in this study because the subjects were explicitly requested to report their emotional responses, leading to an inability of the stimuli to elicit strong levels of fear or anxiety related to amygdal a a ctivation (LeDoux, 1995) The AdSAM arousal scores of the Teacher and Coke commercials were significantly lower than those of the Gato rade and Anti Fur commercials ( Figure 3 3 ). When these same pairs were contrasted in the imaging data, significant differences were identified in th e right STG and the right MFG ( Figure 3 5 ). The STG has been found to be a motion processing region (Schultz et al., 2005) Perceiving the graphical display of motion has been found to increase arousal (Simons et al., 1999) ; hence, the identification of motion may actually be arousal. For dominance, there were no findings of significant differences in th ese data. This should not be surprising since dominance often accou nts for a much smaller amount of variance of emotional responses than do pleasure or arousal (Mehrabian and Ru ssell, 1974) and is often not a factor in vicarious experiences such as watching a television commercial. Dominance was included in this study because it is a content feature of emotional stimuli (Bradley and Lang, 2000) We acknowledged the theoretical importance of dominance, but primarily focused on pleasure and arousal. Arguably, the PAD model of emotion as shown in these findings appears to be a superior method than the discrete approach for identifyin g the dynamics of emotional

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45 pleasure displeasure, arousal calm and dominance submissiveness dimensions and found specific brain regions associated with pleasure and arousal, re spectively. By focusing on the patterns of responses as reported in the PAD model, we were able to locate specific functional regions where pleasure and arousal were distinctly and significantly different from each other as well as areas where they were d ifferent among the various stimuli. Our findings were not intended to have an impact on the specific commercial ads used. As mentioned previously, these ads were selected mainly because they were once popular in the past but were probably never seen by th e participants. We focused on targeting the emotions involved in processing television ads and hope that our results will contribute to further promoting the multi dimensional approach in analyzing commercial ads. This study used a relatively small but ade quate sample for fMRI studies and further research is needed to locate the responses in the dominant dimension and to calibrate the levels of activity in the brain that distinguish among responses to stimuli. These preliminary results suggest that human em otions are multi dimensional and that self report techniques for emotional response along the pleasure and arousal dimensions correspond to a specific task but different functional regions of the brain. Moreover, our research may help examine emotional res ponses to television advertisements and predict consequent attitudes and behaviors towards marketing communications.

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46 Table 3 1. Brain activation during commercials with a high level of pleasure Region BA Side X Y Z Size t value Inferior Frontal Gyrus 47 R 46 37 8 447 5.64 Inferior Frontal Gyrus 47 L 43 35 4 289 5.69 Middle Temporal Gyrus 21 R 59 19 12 3139 5.81 Middle Temporal Gyrus 21 L 55 19 5 302 5.21 Superior Parietal Lobe 7 R 13 77 44 168 5.13 Comparison between 4 commercials (T, C, G, E) and commercial F. P(corr.) < 0.052. Size = number of 1mm 3 voxels. X, Y, and Z refer to Talairach coordinates.

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47 Table 3 2 Brain activation during commercials with a high level of arousa l. Region BA Side X Y Z Size t value Cerebellum R 19 41 39 1629 5.30 Inferior Temporal Gyrus 37 L 48 43 11 1896 5.43 Middle Frontal Gyrus 9 L 36 28 30 477 5.27 Middle Frontal Gyrus* 10 R 26 32 5 1113 5.40 Middle Occipital Gyrus 19 L 46 65 5 159 5.07 Pulvinar of the Thalamus L 24 26 4 349 5.04 Superior Frontal Gyrus 8 M 7 29 47 2774 5.70 Superior Frontal Gyrus 10 L 16 44 10 214 5.17 Superior Temporal Gyrus* 22 R 64 37 14 808 5.69 P(corr.) < 0.052. Only clusters > 150 voxels are sho Size = number of 1mm 3 voxels. X, Y, and Z refer to Talairach coordinates.

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48 Figure 3 1 The AdSAM manikins in terms of the dimensions: Pleasure, Arousal, and Dominance. Behavioral ratings were scored as the participants sele cted a manikin from each row that best represented the emotions and feelings from each commercial.

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49 Figure 3 2 Results from the AdSAM ratings evaluating the dimension of pleasure. E ach of the five commercials is represented by a different color (Teacher= blue, Evian=yellow, Coke=red, Gatorade=green, Anti Fur=brown). Th e Figure illustrates that the Anti Fur commercial is significantly lower than the other four commercials on mean Pleasure scores

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50 Figure 3 3 Results from the AdSAM ratings evaluati ng the dimension of arousal. E ach of the five commercials is represented by a different color (Teacher=blue, Evian=yellow, Coke=red, Gatorade=green, Anti Fur=brown). The Figure illustrates that that the Gatorade and Anti Fur commercials combined are signif icantly higher than the Teacher and Coke commercials combined on mean Arousal scores

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51 Figure 3 4 Brain activation during commercials with a high level of pleasure. Contrasts of the Anti Fur commercial (4X balanced) against the other four commercials Figure 2a and in Figure 2c include bilateral inferior frontal gyri: right at Tal (46,37, 8) t=5.64 and left at Tal ( 43,35, 4) t=5.69 and the bilateral middle temporal gyri: right at Tal (59,19,1 2) t=5.81 and left at Tal ( 55, 19, 5) t=5.21. The graphs in Figs. 2b & 2d depict an increased BOLD signal in all four commercials relative to the Anti Fur commercial block for the selected voxels. Each of the five commercials is represented by a different color (Teacher=blue, Evian=yellow, Coke=red, Gatorade=green, Anti Fur=brown).

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52 Figure 3 5 Brain activation during commercials with a high level of arousal. Contrasts of the Gatorade and Anti Fur commercials against the Teacher and Coke Tal (64, 37,14) t=5.69 and the right middle frontal gyrus: at Tal (26,32,5) t=5.40. The graphs in Figure 3a and in Figure 3b depict an increased BOLD signal in the blocks c ontaining the Gatorade and Anti Fur commercials relative to the blocks containing the Teacher and Coke commercials for the selected voxels.

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53 CHAPTER 4 THE EFFECTS OF LOW D OSE ALCOHOL ON NEURA L TIMING Background All together the brain processes temporal inf ormation over a range of at least 10 orders of magnitude that can be categorized into four different time scales: microseconds, milliseconds, seconds, and circadian rhythms (Buonomano and Karmarkar, 2002) The vast majority of mammals must rely on such temporal processing for functions related to sound localization, auditory recognition and vocalization, motion detection, motor coordination, and conscious time estimation among many other tasks related to species survival and prosperity. For the past several decades, neuroscientists have attempted to identify the neural substrates that control are regulated by the hypothalamus (King and Takahashi, 2000) Evidence implicating the roles of the posterior parietal cortex and hippocampus is supported by a study (Leon and Shadlen, 2003) that trained macaque rhesus monkeys to indicate with an eye movement whether the duration of a test light (<1 s) was longer or shorter than a remembered standard. A recent fMRI study comparing explicit vs. implicit timing (Coull and Nobre, 2008) showed the basal ganglia, prefrontal, premotor, and cerebellar areas t o be context dependent and concluded that the inferior parietal and premotor areas have roles associated with expectation related behavior. A comprehensive proposal presented by Warren Meck (Meck et al., 2008) claims that cortico striatal circuits optimized by the dopaminergic modulation of oscillatory activity and lateral connectivity are chiefly responsible for discriminating interval timing. Case d isease (Paulsen et al.,

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54 2004) d isease (Malapani et al., 1998) provide clinical verification for the essential roles of the basal ganglia and neurotransmitters in the proper functioning of time perception. A previous study conducted by Hinton and Rao utilized a similar paradigm to the tasks in our experiment. They c ompared a timing task to a control counting task with a 16s interval, and demonstrated that counting prod uced equally accurate but less variable estimates than timing (Hinton and Rao, 2004) The most significant neural activation differences between counting and timing were fou nd in regions classically and right cerebellum) for counting and in the SMA for timing (Hi nton and Rao, 2004) However, many ROIs could have overlapped because of the use of chronometric counting in the timing task. We made a deliberate effort to avoid this confound by warning our participants not to repeat such a mistake when performing the C ounting task, which also served as our control similar to the aforementioned study. The most analogous practical application of our paradigm pertains to judging the duration of a traffic light while driving. As part of our investigation, we further consid ered how the consumption of a low dose of alcohol can impair relevant abilities (e.g., neural effects on brain function still remains elusive, human studies have indispu tably established the adverse effects of ethanol associated with three general areas: i) psychomotor functioning involving motor speed and hand eye coordination (Hiltunen, 1997) ii) visual perception involving identifying an object at a long distance and assessing the position of moving stimuli (Levin et al., 1998) and iii) cogni tive capacities

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55 involving immediate memory recall as well as conceptual and abstract thought processing (Tarter et al., 1971) In particular relevance to this study, prior research has also shown that chronic alcoh olics with lesions to the lateral cerebellar hemispheres demonstrated deficiencies in the timing component of a paced tapping task (Ivry et al., 1988) We assert that there is still insufficient knowledge about the acute effects of low doses of alcohol on the capabilities of humans to discern short time intervals. The current study ha s two primary aims: ( i ) to isolate the changes in BOLD signal associated with interval timing as compared to counting in the realistic multi second range (4 6s), and ( ii ) to examine the acute effects of a low dosage of alcohol (0.25g/kg) on the neural co mponents of the perception of time passage We further h ypothesized that a low dose of alcohol would more clearly distinguish the similar cognitive processes of counting and neural timing Materials and Methods Participants Eight healthy, right handed adult male volunteer s ( age s 21 32) signed written, informed consent for the protocol approved by the Institutional Review Board at our University and thus participated in two separate runs of the experiment (16 total functional scans) At the time of the scan, none of the volunteer s reported taking any psychiatric medication or having any history of alcoholism or neurological disorders. All volunteer s were financially compensated $20/hr USD. Experimental Protocol A virtual Traffic Light (TL) stimulus ( Figure 4 1 ) implemented with E Prime (Psychology Software Tools) was used for the experimental conditions involving both Counting and Timing tasks. Specifically, participants were asked to either count or

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56 estimate short time intervals using a visual stimulus consisting of a square diagram with four colored circles (blue yellow, red, and green). For each trial, a single colored light condition, and all four lights were equally but randomly used during each block. The TL paradigm con sisted of two tasks: Counting (control condition) and Timing (experimental condition) presented in a simple block design. For the Counting task, the participants were asked to determine if the light had flashed (at a frequency of 1 Hz) either 4 or 6 times for each trial. In the Timing task, the light remained steadily illuminated and the participants had to judge whether the interval lasted for either 4 or 6 seconds. The participants indicated their selection by using either a thumb or index finger press on a right handed button response glove. Response accuracy and reaction times were recorded with an Integrated Functional Imaging System (IFIS: MRI Devices). Each Counting and Timing block consisted of 8 trials (9s each) followed by a resting block displayin g a fixation cross for 15 seconds. Both tasks were first practiced outside of the scanner. Thus, the effects resulting from the accommodation factor of learning the task were minimized. After mastering each task, participants were positioned in the scanner to begin the experimental condition. In both the Counting and Timing tasks, three fourths of the trials in the block set were long lasting (6 flashes or 6 seconds) and one fourth of the trials were short lasting (4 flashes or 4 seconds). The entire functi onal scan of each run lasted approximately 6 minutes and consisted of two Counting blocks (16 total trials) and two Timing blocks (16 total trials). The categorical sequence of the blocks was alternated between each Pre Drink and Post Drink run and was cou nterbalanced across subjects.

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57 Both experimental tasks were performed inside of the scanner both before and after drinking an alcoholic cocktail. Upon completing the Pre Drink scan, all subjects were served a 200mL cocktail with a low dosage (0.25g/kg body weight) of 80 pf Stolichnaya Vodka mixed with Diet 7UP (no caffeine or sugar). The alcohol consumed served a cocktail with 75mL of alcohol while a man weighing 150lbs was served a cocktail with 54mL of alcohol. Participants were asked to drink the cocktail at their own normal pace. After they had completed consuming the cocktail, the participants were allotted a five minute rest period before re entering the scanner for the second experimental session. Twenty minutes passed before the second experimental task was started. Thus, each participant completed two separate runs of the functional tasks: ( i ) immediately before drinking the alcoholic cocktail, and ( ii ) twenty five mi nutes after finishing the cockt ail ( Figure 4 2). This was done in order to conduct the experimental task during the declining BAC phase (Lyons et al., 1998) Blood Alcohol Concentration (BAC) measurements were taken outside the scanner at the beginning of the first session (i.e., before alcohol administration), 5 minutes after finish ing the cocktail, and at the end of the experiment (32 min. after finishing the cocktail) using the Digital Alcohol Breath Analyzer AlcoScan CA2000 (Craig Medical Distribution). Imaging Parameters A 3T head dedicated scanner (Siemens Allegra; Munich, Germa ny) was used for all magnetic resonance imaging. T1 weighted 3D anatomical images were acquired with a MPRAGE sequence in the following dimensions: Matrix =256x256, TR=1.5s, TE=4.38ms, FA=8 FOV=240mm, 160 slices, slice thickness=1.2mm Functional

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58 images w ere acquired with a gradient echo EPI sequence sensitive to the BOLD signal in the following dimensions: Matrix=64x64, TR=3.0s, TE= 30ms, FA=90 FOV=240mm, 36 slices, slice thickness=3.8mm without gaps Data Analysis Standardized paired sample t tests and a One Way ANOVA (Statistical Package for the Social Sciences, Inc.) were utilized to assess for significant differences in performance of the behavioral tasks. BrainVoyager v. 4.9.6 (Brain Innovations) software was used to analyze the imaging data. First, t he functional images from each participant were first co registered with the 3D anatomic images and then normalized into Talairach space (Talairach&Tournoux, 1988) The resulting 3D functional data then underwent motion correction, linear trend removal, spatial smoothing (5.7 mm FWHM Gaussian filter), and temporal filtering with a low pass filter of 0.08 Hz (30 cycles/time course). The functional imaging data acquired from the wh ole group (n=8) during both runs was incorporated into a general linear model (GLM) which produced voxel wise statistical activation maps based on the block design of our paradigm The predictors were estimated hemodynamic responses to the Counting and Tim ing tasks during both Pre Drink and Post Drink states. Contrasts between the predictors were used to evaluate the relative contribution of each condition to variance in the BOLD signal. The BOLD responses for each condition were visualized using time locke d averaging of the percent signal change relative to the baseline resting condition. A strict statistical threshold was set to p <0 .001 (regional corrected ) and the minimum cluster size was 200mm 3 when comparing the BOLD signal between the tasks (Counting vs. Timing). However, we lowered the threshold to p <0 .05 (regional corrected ) and also lowered the

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59 minimum cluster size to 100mm 3 when comparing the BOLD signal across st ates (Pre Drink vs. Post Drink) for either the Counting or Timing tasks. Results Beha vioral Data A BAC of 0.0 was recorded for all participan ts at the start of each session. The average BAC measurements at five minutes after finishing the cocktail were 0.07 0.04 ( M SD) and a t the termination of the experiment (32 min. after finishing the c ocktail), the average BAC measurements were 0.05 0.03 The accuracy of response for the Counting task in both the Pre Drink state and the Post Drink state was 99.2% 2.2%. The accuracy of responses for the Timing task was 96.2% 4.5% for the Pre Drink st ate and 88.3% 17.8% for the Post Drink state (see Figure 4 3) .There were no significant differences in terms of the accuracy of responses between the Pre Drink and Post Drink states ( p < 0.05) for either the Counting ( t = 1.0) or Timing ( t = 0.26) tasks. There were also no significant differences found in terms of the accuracy of responses between the Timing and Counting tasks ( p < 0.05) for either the Pre Drink state ( t =0.11 ) or Post Drink state ( t = 0.13). The data compiled from the ANOVA further confirmed that there were no significant differences between the means from these four conditions (F=2.43, df 1 =3, df 2 =28). The mean reaction time in the Counting task was 662 144 milliseconds Pre Drink and 580 113 milliseconds Post Drink. In the Timing task, the mean reaction time was 805 136 milliseconds in the Pre Drink state and 752 164 milliseconds in the Post Drink state (see Figure 4 4 ) There were no significant differences in terms of the mean reaction times between the Pre Drink and Post Drink states ( p < 0.0 5) in either the

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60 Counting ( t = 0.22) or Timing ( t = 0.49) tasks Overall, t he mean reaction time of the Counting tasks was shorter than the mean reaction time of the Timing tasks ( p <0.05) but did not significantly differ in the Pre Drink state ( t = 0.06) .H owever in the Post Drink state, the mean reaction time of the Timing task was significantly longer than the Counting task ( t = 0.03). The data compiled from the ANOVA further confirmed that there were significant differences between the means from these four cond itions (F=4.01, df 1 =3, df 2 = 28). Imaging Data We compared changes in BOLD signal in the Timing task to changes in BOLD signal in the Counting task within each state (Pre Drink and Post Drink) using a very strict threshold criteria of p ( cor. ) < 0 .001 and a minimum cluster size of 200 voxels. During the Pre Drink state, we observed significantly larger BOLD signal increases in the right secondary somatomotor cortex, left cerebellum, right inferior parietal lobe, bilateral insula, medial frontal gyrus, bilater al prefrontal cortex, and ventrolateral thalamus for the Timing task relative to the Counting task (see Table 4 1 ). We did not find any regions that showed a higher BOLD signal for the Counting task relative to the Timing task in the Pre Drink state. Durin g the Post Drink state, we observed significantly larger BOLD signal increases in the left cerebellum, right inferior parietal lobe, right insula, and medial frontal gyrus f or the Timing task relative to the Counting task We also found a higher BOLD signa l for the Count ing task relative to the Tim ing task in the bilateral amygdal ae bilateral secondary striate cortices, and bilateral temporal sulci in the Post Drink state (see Table 4 2 ). We lo wered the threshold criteria to p (cor.)< 0 .05 and a minimum clu ster size of 100 voxels when comparing the state dependent changes in BOLD signal between the

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61 Pre and Post Drink runs. When subtracting the BOLD signal changes in the P re Drink Timing condition from the P ost Drink Timing condition, we observed two separat e clusters of activation increases in the right superior parietal lobe and deactivation in the right basal ganglia (see Table 4 3 ). When subtracting the BOLD signal changes in the P re Drink Counting condition from the P ost Drink Counting condition, we obse rved activation increases in the left middle frontal gyrus, posterior cingulate gryus, left precentral gy r u s right superior parietal lobe, and lef t superior temporal gyrus and one small cluster of deactivation in the right middle frontal gyrus (see Table 4 4). Discussion The salient issue preventing a consensus regarding the underpinnings of temporal processing is that there are currently two opposing views involving its dynamics. One theory, initially proposed by John Gibbon (Gibbon et al., 1984) and supported by more recent contemp oraries (Macar et al., 2006) attempts to explain the relationship between selective attention and neural timing metaphor. This theory suggest s that the duration of a perceived interval of time is dependent of the number of pulses emitted by an internal clock. A rival th eory postulated by Matell and Meck now challenges this concept (Matell and Meck, 2004) Their alternative model is computations based on coincidence detection (Matell and Meck, 20 04) They contend that neurons in the striatum synchronize the release of dopamine from the substantia nigra and the corresponding patterns of neuronal firing within the network (Matell and Meck, 2004) Our preliminary results are compatible with both models. O ur findings revealed three separate clusters of activation in the prefrontal cortex ( Figure 4 5 ) .Our first finding consisted of a cluster in the left SMA (BA6) and a cluster in

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62 the left ventrolateral nucleus of the thalamus to have a higher BOLD signal in the Timing task relative to the Counting task during the Pre Drink state but not during the Pos t Drink state Several previous fMRI studies with similar timing task paradigms have obtained comparable results. Asking participants to differentiate 0.6s vs. 5 s demonstrated an increased BOLD signal in the rostral and medial prefrontal cortex and in the left SMA (Rubia et al., 1998) while asking participants to discern between 0.6s vs. 3s highlighted the activation of the bilateral insula, bilateral DLPFC, right SMA, and right inferio r parietal lobe (Lewis and Miall, 2003) E mploying an auditory synchronization task suggested a possible network including the caudal SMA, left putamen, and left ventrolateral thalamus (Rao et al., 1997) In the second finding, we report the bilateral amygdal ae ( Figure 4 6 ) to have revealed a higher BOLD signal in the Counting task relative to the Timing task only during the Post Drink state. The amygdal a has been documen ted as a critical part of the primitive subcortical circuit that becomes activated in response to unpleasant emotions and fear (Wright and Liu, 2006a) Here, we speculate that the Timing task deactivated the amygdal a because it engaged the participants with a more complex cognitive process compared to the Counting task and recruited projections from the more highly developed neocortical structures. Perhaps the most important finding shows that the right superior parietal lobe ( Figure 4 7 ) emitted a higher BOLD signal in the Post Drink state than in the Pre Drink state for both task s This neural structure is most commonly activated during periods of sustain ed visuospatial processing (Giessing et al., 2004) maintaining concentration on a shifting target (Vandenberghe et al., 2001) and in reorientin proprioception

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63 to a changing environment (Shimada et al., 2005) I n some precarious situations (e.g., adjusting to traffic lights w hile driving), an impairment of the functions just mentioned could result in dangerous outcomes due to diminished capabilities in the mental coordination of vision, timing and motor tasks (Calhoun et al., 2004) The increased activation in the Post Drink state may therefore reflect an increased demand for recruiting extra cognitive resources essential to repeat an equivalent task performance after alcohol consumption. The behav ioral data that we acquired pertaining to the mean reaction times during the Post Drink state further support this conclusion. The mean reaction time of the Timing task was longer than the Counting task in the Post Drink state but not in the Pre Drink stat e. Furthermore, t he mean reaction time for the responses in the Post Drink state were slightly faster in both the Counting and Timing tasks, suggesting that subjects may have been over compensating for a false sense of in ebria tion. We recognize that many l imitations arise from the complexity of our investigation. Simulating an environment and task that ideally resembles the multi dimensional process of driving is quite challenging, especially considering the tightly confined space inside the scanner and the safety rules prohibiting any materials containing metallic elements from entering the environment of the scanner. In general, the logistics of fMRI scanning limited our sample size and restricted the creativity of the design of our experimental paradigm. The action of consuming the cocktail forced us to bring the participants outs ide the scanner in between runs. Although we did counterbalance the Timing and Counting tasks within each run, the act of consuming an alcoholic beverage (even a low dose) also p revented us from altering the sequence of the Pre Drink and

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64 Post Drink runs. The acute effects of consuming a low dose of alcohol will normally begin to induce the greatest changes in BAC 20 minutes after drinking but depending upon levels of tolerance i t is possible to experience changes in regional cerebral blood flow for several more hours (Sano et al., 1993; Lyons et al., 1998) Thus, the only alternative to avoiding this bias would have been to scan all parti cipants on two separate days for each run; however, a two day schedule would have introduced even more confounding variables related to diet, sleep, and mental state that could all adversely affect the BOLD signal. L astly, we relied on measuring BAC level s with a commercial breathalyzer as opposed to incorporating a technique more sensitive to the alcohol elimination rate such as the Mellanby clamping approach (O'Connor et al., 1998) which is far more invasive. In conclusion, the interconnections and communications within an integrated network of brain regions are responsible for neural timing. Ou r study showed that the left cerebellum, right inferior parietal lobe, right insula, medial frontal gyrus, and right superior parietal lobe are among the most notable structures involved. In obtaining these findings, we applied a very practical and origina l TL stimulus in a novel paradigm that integrated the consumption of a low dose of alcohol with the challenge of distinguishing the cognitive aspects between the tasks of Timing and Counting. The behavioral and imaging data that we collected support severa l aspects from the models presented in previous studies found in the literature, but also introduce many new questions to examine and explore. Our findings were based on accurately evaluating time perception which is only one aspect of performing the mult iple complicated tasks associated with driving. Automobile accidents that are primarily caused by alcohol

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65 intoxication can be categorized as resulting from decreases in either motor coordination or inhibitory control. However, other factors such as age, ge nder, and subjective self perception of intoxication greatly contribute to the relative risk of fatality arising from this state of disinhibition and impulsivity and should be investigated separately. We also recommend that future studies compare the resul ts of temporal processing task performance using both a placebo condition and a higher dose of alcohol. Ultimately, such research may improve the present understanding of the pathways that lead to the diminished capacities resulting from both acute and chr onic alcohol intoxication of the brain Hopefully, understanding the harmful impact of short term alcohol administration on tasks that heavily rely on accurate temporal processing will contribute to the long term prevention of its unfortunate and deleterio us consequences.

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66 Table 4 1 Pre Drink brain activation : Timing > Counting Region BA Side x y z Size t score Supplementary Motor Area 6 R 43 4 24 519 6.35 Cerebellum L 32 76 25 1222 6.38 Cerebellum L 32 52 32 278 6.25 Inferior Parietal Lobe 40 R 48 47 32 472 6.15 Insula R 33 16 7 800 6.54 Insula L 38 15 5 2659 6.68 Medial Frontal Gyrus 6 M 1 8 51 1184 6.28 Middle Frontal Gyrus 10/46 R 33 44 20 910 6.43 Middle Frontal Gyrus 9 L 29 50 18 2641 6.71 Superior Frontal Gyrus 9 R 23 49 2 8 1139 6.17 VL of Thalamus M 14 14 17 242 5.97 P ( number of 1mm 3 voxels. X, Y, and Z refer to Talairach coordinates

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67 Table 4 2 Post Drink brain a ctivation: Timing > Counting REGION BA Side x y z Size t score Amygdale R 18 8 14 320 6.32 Amygdale L 23 6 9 1546 6.47 Cerebellum L 24 54 38 1125 6.13 Inferior Frontal Gyrus 47 R 28 19 19 347 6.17 Inferior Parietal Lobe 40 R 53 52 39 971 6.22 Inferior Temporal Gyrus 20 R 40 37 20 229 6.83 Insula R 37 18 6 616 6.12 Medial Frontal Gyrus 6 M 3 13 49 1331 6.40 Middle Frontal Gyrus 8 L 27 22 34 735 6.06 Middle Temporal Gyrus 39 L 55 58 12 282 6.28 Secondary Striate Cortex 19 R 47 67 3 220 6 .00 Secondary Striate Cortex 19 L 44 66 9 475 6.16 P ( number of 1mm 3 voxels. X, Y, and Z refer to Talairach coordinates

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68 Table 4 3 Timing associated brain activation: Post Drink > Pre Drink Region BA Side x y z Size t score Basal Ganglia R 21 18 3 219 5.20 Superior Parietal Lobe 5/7 R 29 44 61 580 5.89 Superior Parietal Lobe 7 R 16 58 55 531 5.33 P ( corr.) < 0.050. Only clusters > 100 voxels are shown. BA = B number of 1mm 3 voxels. X, Y, and Z refer to Talairach coordinates.

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69 Table 4 4 Counting associated brain activation: Post Drink > Pre Drink REGION BA Side x y z Size t score Middle Frontal Gyrus 10 L 29 33 4 142 5.23 Middle Front al Gyrus 10 R 25 48 2 121 5.36 Posterior Cingulate Gyrus 29 M 4 46 17 163 5.29 Precentral Gyrus 4 L 20 36 62 157 5.46 Superior Parietal Lobe 7 R 20 57 54 199 5.21 Superior Temporal Gyrus 22/42 L 53 30 14 262 5.25 P ( corr.) < 0.050. Only cluste number of 1mm 3 voxels. X, Y, and Z refer to Talairach coordinates

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70 Figure 4 1 Schematic of Virtual TL Stimulus The counting and timing tasks are both displayed with a square diagram consi sting of four colored circles (blue, yellow, red, and green) on a screen with a black background. Each light can either be presented as (a) off, (b) on, or (c) flashing. (a) OFF: the baseline at ated for 4s or for 6s; repeated 4 or 6 times.

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71 Figure 4 2 Timeline of Experimental Paradigm After completing the initial Pre Drink scan, the participants exited the s canner and were served a 200mL cocktail consisting of diet 7UP 80 pf Stolichnaya Vodka according to 0.25g/kg of their body weight. Blood alcohol measurements were taken before the session started, 5 minutes after finishing the cocktail, and at the end of t he session (31 minutes after finishing the cocktail). The same TL tasks were performed again 25 minutes after drinking the cocktail.

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72 Figure 4 3 Behavioral Data Response Accuracy. The eight participants averaged 99.2% 2.2% correct in the cou nting task both before and after drinking the cocktail. In the timing task, the subjects averaged 96.2% 2.2% correct before drinking the cocktail and 88.3% 17.8% correct after drinking the cocktail

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73 Figure 4 4 Behavioral Data React ion Time. The average reaction time in the counting task before drinking the cocktail was 662 144 milliseconds and the average reaction time after drinking the cocktail was 580 113 milliseconds. The average reaction time in the timing task was 805 136 m illiseconds before drinking the cocktail and 752 164 milliseconds after drinking the cocktail. The error bars represent the standard deviations.

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74 A) B) C) Figure 4 5 Brain activation during Pre Drink; Timing > Counting Within the specified regions of the prefrontal cortex A: (33, 44, 20) & (23, 49, 28) and C: ( 29, 50, 18), the time locked averaged BOLD responses for the Timing task (orange line) are consistently higher than for the C ounting task (blue line) during the Pre Drink state. B: The colored regions in the prefrontal cortex of the coronal slice represent areas of significant BOLD signal increases for the Timing task > Counting task during the Pre Drink state.

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75 Figur e 4 6. Brain activation during Post Drink; Counting > Timing Within the specified regions of the amygdales A: (18, 8, 14) and C: ( 23, 6, 9), the time locked averaged BOLD responses for the Counting task (blue line) are consistently higher than for th e Timing task (orange line) during the Post Drink state. B: The colored regions in the amygdal a of the coronal slice represent areas of significant BOLD signal increases for the Counting task > Timing task during the Post Drink state.

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76 Figur e 4 7 Brain activation during Timing Task; Post Drink > Pre Drink Within the specified region of the superior parietal lobe A: (29 44, 61), the time locked averaged BOLD responses in the Post Drink state (orange line) are consistently higher than in the Pre Drink state (blue line) for the Timing tasks. B: The colored regions in the superior parietal lobe of the coronal slice represent areas of significant BOLD signal increases for the Post Drink state > Pre Drink state during the Timing task.

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77 CHAPTER 5 INVESTIGATING THE NEURAL CORRELATES OF SMOKE CRAVING IN THE BRAIN Background The deleterious effects of nicotine addiction have been well documented yet it persists among the leading concerns confronting the medical health community at a global level. Whil e we have progressed to a high level of understanding in terms of the pathologies related to excessive smoking, the neuronal networks and pathways in the brain associated with smoke craving continue to be an area needing further investigation Previous re search shows that smokers exposed to smoking related cues have demonstrated increased craving as well as distinct patterns of brain activation. exposed to cigarette stimuli. These responses are attributed to learning processes (e.g. classical conditioning) and are associated with motivational factors that maintain nicotine dependence (Carter et al., 2009) In nicotine dependent subjects, cues related to smoking elicit activity in brain regions linked to attention, memory, emotion, and motivation (Smolka et al., 2006) Positive correlations have been found between craving and metabolism in the OFC, DLPC, and anterior insula bilaterally (Lim et al., 2005) Greater ventral striatum NAc activation in smokers compared to non smokers was pr esented with smoking related cues (David et al., 2007) The stage of withdrawal and nicotine anticipation produce (1) different motor prepatory and inhibi tory response processing and (2) different craving related processing (Gloria et al., 2009)

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78 Cigarette smoking has been linked to a number of personality characteristics, including impulsivity. Smokers tend to endorse high levels of impulsivity, and more impulsive smokers have greater diff iculty quitting, but little is known about potential explanatory mechanisms (Doran et al., 2009) While most cigarette smokers endorse a desire to quit smoking only about 14% to 49% will achieve abstinence after 6 months o r more of treatment (Br ody, 2006) Cue induced craving plays an important role in smoking and relapse and likely in other addictions as well ; and this urging sense of c raving to smoke is often conceptualized and measured as a tonic, slowly changing state induced by abstinence. Exposure to smoking related cues can trigger relapse in smokers attempting to maintain abstinence (McClernon et al., 2009) Cue exposure therapy (CET), which refers to the repeated exposure to drug related cues in order to extinguish this learned association, has increasingly been claimed as a effective method for treating addiction including cigarette smoking (Moon and Lee, 2009) Observational field studies indicate such cue induced cravings are substantia lly responsible for relapse to smoking but that smoking can often be averted by coping responses (Ferguson and Shiffman, 2009) Environmental cues (e.g., the sight of a cigarette) have long been recognized as important triggers for craving in smokers (Tong et al., 2007) Theoretical models of addiction suggest that attentional bias for substance related cues should be associated with self reported craving (Field et al., 2009) Thus, we designed our paradigm accordingly and incorporated beh avioral responses to our visual stimuli consisting of cigarette related cues in addition to the functional imaging data acquired during this time of exposure A novel aspect of our design would be recognized if our results could

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79 potentially display a liner trend by using the two points: H M and M L from both the imaging and behavioral data To our knowledge, no other studies in the literature have attempted to include a middle range of stimuli. Our two main objectives were to: i) ceived craving level and emotions induced by a three tiered gradient of visual cues, High > Medium > Low among 3 groups of subjects: Dep Sat and NS ; ii) correlate the behavioral data from the evaluation of the stimuli with the fMRI activations in the brain areas related to craving, including the insula, amygdala, ventral BG, OFC, and dlPFC. We hypothesized that t of regions in the brain when exposed to various levels of emotional stimuli and within th e established models of addiction pathways. Materials and Methods Participants Participants were recruited from advertisements at the campus of Beijing University that requested both smokers and non smokers. The study included 45 male s (15 in each group), aged 28.4 + 3.5 yrs. They were selected based on their responses to a questionnaire used to determine their frequency of smoking and habitually related behavior. They were then grouped into the following categories: i) Deprived smoker an addicted smoker t hat was prohibited from smoking for at least 10 hr prior to experiment, ii) Satisfied smoker an addicted smoker that was allowed to smoke as usual including one cigarette before scanning

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80 iii) Non smoker someone that has smoked less than 5x in his lifetime. We defined an addicted smoker as smoking an average of 15 cigarettes per day for at least two years. Experimental Protocol We used a block design, in which a group of photos were randomly selected from High M Low s from a group of contrast processed photos The photographs were previously categorized based on the level of craving induced by the content. For example, a photograph of a person smoking a ure 5 1 ). The p hotographs were displayed on a screen for 3.7 seconds followed by a 0.3 second interval. Self reported data regarding the level of craving induced by the photographs wa s collected outside the scanner aft er both sessions were completed The e ntire functional scan consist ed of two sessions with 117 volumes each. There were 6 blocks in each session: one for each category and its corresponding contrast processed set. Each b lock consisted of 12 volumes and 6 trials (24s). There were resting intervals of 4 volumes (8s) in between each block and resting periods of 12 volumes at the beginning and end of each scanning session. Outside of the scanner the participants were asked to respond to a survey based on a Likert scale after completing the two functional runs. The questions addressed the The content of the photos was identical to the phot os viewed during the functional scanning but was presented in a randomized order and did not contain any contrast smashed photos.

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8 1 Imaging Parameters A 3T Signa General Electric scanner was used for all magnetic resonance imaging. T1 weighted 3D anatomical images were acquired with a MPRAGE sequence in the following dimensions: Matrix =256x256, TR= auto TE=m in FA= 12 FOV=2 8 0mm, 1 56 slices, slice thickness=1.0 mm Functional images were acquired with a gradient echo EPI sequence sensitive to the BOLD signal i n the following dimensions: Matrix=64x64, TR= 2 .0s, TE= 30ms, FA=90 FOV=240mm, 2 6 slices, slice thickness= 4.0 mm without gaps Data Analysis The imaging data was acquired using a 3T Siemens Allegra scanner. All group data were preprocessed and analyzed usin g Statistical Parametric Mapping 5 (SPM5, http://www.fil.ion.uclac.uk/spm ).Images were first corrected for within scan acquisition time differences between slices and then realigned to the first volume to correct for interscan head motions. Next, we spatially normalized the realigned images to the standard EPI template and re sampled them to a voxel size of 3mm3mm3 mm. Finally, the functional images were spatially smoothed with a Gaussian kernel of 6mm6mm6mm FWHM to decrease spatial noise. Further statistical analysis of smoothed data from group 1was carried out using SPM5. A random effect one sample t test(P < 0.0 01) was used to examine the group result of the stimulation. Significance levels in the behavioral data were determined using a repeated measures ANOVA from the software SPSS ( Statistical Package for the Social Sciences) and a post hoc analysis that includ ed the Tukey test.

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82 Results Behavioral Data The behavioral dat a was acquired outside the scanner from the self reported Likert scale based surveys. The intensity of craving and liking indicated similar trends for the deprived smokers and satisfied smokers. As expected, the individuals of these groups are addicted smokers who should respond to the stimuli in the photographs accordingly. The non smokers expressed a significantly less intensity of craving for the high and medium categories of photos. This resu lt was also expected as the individuals from this group are not likely to be aroused or excited by the stimuli in th e photographs (Fig ure 5 2 ) Although, a linear trend can be seen in response to both craving and smoking, a closer examination of this data reveals a disassociation between the two emotions. An inverse association was depicted for the emotion of disgust. Here, the nonsmokers showed significantly higher scores compared to the addicted groups of deprived and satisfied smokers (Fig ure 5 3). Imag ing Data The p reliminary results that we obtained indicate that our findings of brain activation in response to smoke craving coincide with the established mesolimbic reward circuit pathway of addiction as well as with the common neural network of visuospa tial processing. When comparing within the same groups and across conditions at a threshold level of P(corr) > 0.01, we found BOLD signal increases in the Parahippocampus, Caudate, Cingulum, and T halamus (Figure 5 4). When comparing within the same conditi ons and across groups at a threshold level of P(corr) > 0.01, we found BOLD signal increases in the Precuneus bilaterally, Superior Frontal Gyrus (SFG)

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83 bilaterally, Right Inferior Frontal Gyrus (IFG), and Rig ht Supramarginal Gyrus (Figure 5 5). Discussion Many similar studies in the literature have found activations in the same brain areas to the findings shown in our results. The severity of nicotine dependence and intensity of craving are independently associated with cue induced brain activation in separ ate neuronal networks (Smolka et al., 2006) In smokers, the fMRI signal was greater after exposure to smoking related images than after exposure to neutral images in mesolimbic dopamine reward circuits known to be activated by addictive drugs (right posterior amygdale, posterior hippocampus, VTA, and medial thalamus) as well as in areas related to visuospatial attention (bilateral prefrontal and parietal cortex and right fusiform gyrus) (Due et al., 2002) Another more rece nt study has demonstrated that in cigarette smokers, the most commonly reported areas of brain activation during visual cigarette cue exposure are the prefrontal, anterior cingulated, and visual cortices (Brody et a l., 2007) In general terms, nicotine improves attention in smokers by enhancing activation in areas traditionally associated with visual attention, arousal, and motor activation (Lawrence et al., 2002) Smokers w ith lower levels of nicotine dependence showed greater maintained attention and faster approach responses to smoking related cues. Longer gaze times for smoking cues were associated not only with lower levels of nicotine dependence, but also with higher le vels of craving (Mogg et al., 2005) Brain responses to smoking cues, while relatively stable at the group level following short term abstinence, may be modulated by individual differences in craving in response to

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84 abstinenc e particularly in regions sub serving attention and modulation (McClernon et al., 2005) The structures in the neural network pertaining to visuospatial processing (Figure 5 4) including the precuneus, superior frontal gyrus, right inferior frontal gyrus, and right supramarginal gyrus are findings that frequently a ppear in the results of many previous studies Abstinence induced changes in craving (abstinence satiety) were positively correlated with changes in HDR to smoking cues in frontal regions including left inferior frontal gyrus, left ventral anterior cingu late gyrus (vAC C ) and the bilateral middle frontal gyrus, and the same study also revealed larger responses to smoking compared to controls in the superior frontal gyrus (McClernon et al., 2005) In the contrast condition activation was found in the cigarette cue resist (compared with the cigarette cue craving cond ition) in the left dorsal anterior cingu l ate cortex (ACC), posterior cingulated cortex (PCC), and precuneus. Lower MRI signal for the cigarette cue resist condition was found in the cuneus bilaterally, left occipital gyrus, and right postcentral gyrus (Brody et al., 2007) And most relevant to clinical applications, the comparison of the pre CET regions to those of post CET detected the inferior frontal gyrus and superior frontal gyrus. These regions are consistent with previous studies of activated brain regions related to nicotine craving, and virtual reality CET seems to be an effective method of treating nicotine craving (Moon and Lee, 2009) The structures in the neural network pertaining to the mesolim bic pathway (Figure 5 4) including the parahippocampus, caudate, cingulated, and thalamus are all findings well supported in the literature. In nicotine deprived smokers, both reward and attention circuits (amygdala, hippocampus, VTA, and thalamus) were ac tivated by

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85 exposure to smoking related cues (Due et al., 2002) The Intensity of cue induced craving was significantly associated with greater BOLD activation in mesocorticolimbic areas engaged in incentive motivation and in brain regions related to episodic memory such as the parahippocampus (Smolka et al., 2006) Craving for a cigarette in chronic smokers was correlated with regional cerebral blood flow (rCBF) in the right hippocampus, an area involved in associating envir onmental cues with drugs, and in the left dorsal anterior cingulated gyrus (ACC) an area implicated in drug craving and relapse to drug seeking behavior (Zubieta et al., 2005) Future studies could allow us several different option s of investigating the sense of smoke craving much further. In relation to one of our own previous studies involving low dose alcohol, we could explore the effects of smoking on neural timing. Results suggest that smoking urge may affect t ime perception and that craving smokers over estimate the duration and intensity of their own future smoking urges if they abstain (Sayette et al., 2005) In peo ple expecting to smoke immediately after the scan, smoking cues activated brain areas implicated in arousal, attention, and cognitive control. However, when subjects knew they would not be allowed to smoke for 4 h, there was almost no brain activation in r esponse to smoking cues, despite equivalent reported levels of craving (McBride et al., 2006) We could also incorporate aspects from another one of our own previous studies by applying a multidimensional approach similar to our AdSAM study. Multidimensional scaling (MDS) models demonstrate that smoking cue conditions versus neutral conditions (Carter et al., 2008)

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86 Another alternative study could use all female subjects instead of all male subjects. Women are more likely than men to relapse afte r initiating abstinence from ci garette smoking. The findings indicate that overnight abs tinence produces more negative mood symptoms and cigarette craving in female smokers than in males, and that resumption of smoking produces greater relief from these symptoms in female workers. These differences may contribute to the greater likelihood of relapse when women try to quit smoking (Xu et al., 2008) An exploratory examination of gender revealed that men had higher blood pressure and skin temperature responses than women, and that women had higher responses when viewing videos of wo men smoking than when viewing men smoking (Tong et al., 2007) And yet another alternative study could implement various levels of stressful stimuli. Stress can trigger relapse in abstin ent addicts and can induce drug craving. Stress and reward predicting drug cues act on overlapping brain regions (Dagher et al., 2009) Following stress there was an increased neural response to drug cues in the me dial prefrontal cortex, posterior cingulated cortex, dorsomedial thalamus, medial temporal lobe, caudate nucleus, and primary and association visual areas. These regions are thought to be involved in visual attention and in assigning incentive value to cue s (Dagher et al., 2009) A demonstration linking such a high level of stress to poor cognitive functions would have tremendous implications Clinical case studies by Naqvi et. al; found that smokers with brain dam age involving the insula, a region implicated in conscious urges, were more likely than smokers with brain damage not involving the insula to undergo a disruption of smoking addiction, characterized by the ability to quit smoking easily, immediately, witho ut relapse, and without persistence of the urge to

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87 smoke. This result suggests that the insula is a critical neural substrate in the addiction to smoking (Na qvi et al., 2007) In conclusion, the data we acquired from the surveys helped establish association and disassociation patterns which were then better scrutinized with the increased sensitivity of functional neuroimaging capabilities. The behavioral dat a showed a significant trend across groups Dep>Sat>N S in response to the smoking NS>Sat/Dep for the emotion of The fMRI data revealed increased changes in BOLD signal in the br ain areas involved in visual processing, memory, NAc, and prefrontal cortex The regions in the brain that responded to the stimulation gradient of smoking cues further he lpful targets in terms of the diagnosis and progress of treatment of CET for monitoring and controlling the level of craving in addicted smokers. The regions in the brain that responded to the stimulation gradient of smoking cues further supports the VTA r eward of the diagnosis and progress of treatment of CET for monitoring and controlling the level of craving in addicted smokers.

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88 Figure 5 1 Examples of the vario u s photographic stimuli used in functional runs. There was a total of 12 pictures used for each category.

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89 Figure 5 2 Scoring in response to craving based on Likert scale. Survey data was acquired out side scanner after last session (n=45). Mean S.D.

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90 Figure 5 3 The disa ssociation between craving and liking and the inverse association relation for the emotion of disgust (n=45). CRAVING LIKING DISGUST

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91 Figure 5 4 Patterns of activation in regions of the brain pertaining to the me s olimbic pathway of addiction. Analysis was based on within condition comparisons across groups (P (corr.) < 0.01). A) Transverse slice showing bilateral activation in the Parahippocampus. B) Transverse slice showing bilateral activation in the Caudate and possibly including the NAc. C) Coronal sl ice showing bilateral activation in the Cingulate Gyrus. D) Sagittal slice showing activation in the Thalamus and possibly including the VTA. ( A ) ( B ) ( C ) ( D )

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92 Figure 5 5 Patterns of activation in regions of the brain pertaining to visual processing and maintain atte ntion. Analysis was based on within group comparisons across conditions (P(corr.) < 0.01 ) A) T ransverse slice showing bilater al activation in the P recuneus. B) Transverse slice showing bilateral activation in the S uperior F rontal G yrus (SFG). C) Coronal sli ce showing activation in the Right I nf erior Frontal G yrus (IFG). D) Sagittal slice showing activation in the R ight S upramarginal G yrus. ( A ) ( B ) ( C ) ( D )

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93 CHAPTER 6 GENERAL SUMMARY Unlike previous emotion studies using functional neuroimaging that have focused on either lo cating discrete emotions in the brain or linking emotional response to an external behavior, we investigated brain regions in order to validate a three dimensional construct namely Pleasure, Arousal and Dominance (PAD) of emotion induced by marketing com munication. Emotional responses to five television commercials were measured with Advertisement Self Assessment Manikins (AdSAM ) for PAD and with functional magnetic resonance imaging (fMRI) to identify corresponding patterns of brain activation. We fou nd significant differences in the AdSAM scores on the pleasure and arousal rating scales among the stimuli. Using the AdSAM response as a model for the fMRI image analysis, we showed bilateral activations in the inferior frontal gyri and middle temporal gyri associated with the difference on the pleasure dimension, and activations in the right superior temporal gyrus and right middle frontal gyrus associated with the difference on the arousal dimension. These findings suggest a dimensional approach of con structing emotional changes in the brain and provide a better understanding of human behavior in response to advertising stimuli. We also examine d human brain function related to the perception of short time intervals before and after ingesting a low dosag e of alcohol (0.25g/kg). Functional magnetic resonance imaging (fMRI) was used to measure changes in the blood oxygenation level dependent (BOLD) signal in a novel paradigm that required participants to view a virtual traffic light stimulus while estimatin g the length of a short time interval (Timing task) and in counting a number of flashes (Counting task) as a

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94 control. The study evaluated the influence of alcohol on both behavioral performance and BOLD signal measured using fMRI. Our results demonstrate d that, for both the Pre Drink and Post Drink conditions, the left cerebellum, right inferior parietal lobe (BA 40), right insula, and medial frontal gyrus (BA 6), showed greater BOLD signal increases for Timing than for Counting. Deactivations, with a BOL D signal that represented Timing < Counting, were observed bilaterally in the amygdales, prefrontal cortices, and secondary striate cortices (BA 19) during the Post Drink state only. In the Pre Drink condition, the Timing task caused increased BOLD signal changes relative to the Counting task in the bilateral prefrontal cortex, left insula, right SMA (BA 6), and in the left ventrolateral thalamus. Most notably, the right superior parietal lobe (BA 5/7) showed a BOLD signal increase in the Post Drink state f or both the Counting and Timing tasks, possibly suggesting the recruitment of additional resources to maintain attention following alcohol consumption. These results can provide additional insight regarding the acute cognitive and psychomotor impairments t hat result from exposure to a low dose of alcohol. We designed a novel paradigm for a smoke craving study incorporated behavioral responses (based on a Likert scale) to our visual stimuli consisting of cigarette related cues in addition to the functional i maging data acquired during this time of exposure. We were able to display our results s howing a liner trend because our behavioral and imaging data contain ed two points: H M and M L. To our knowledge, no other studies in the literature have attempted to include a middle range of stimuli. Our two main objectives were to: i) emotions induced by a three tiered gradient of visual cues, High > Medium > Low

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95 among 3 groups of subjects: Dep Sat and NS ; and ii) correlate the behavioral data from the evaluation of the stimuli with the fMRI activations in the brain areas related to craving, including the insula, amygdala, ventral BG, OFC, and dlPFC. We hypothesized that t formulate a network of regions in the brain when exposed to various levels of emotional stimuli and within the established models of addiction pathways. The data we acquired from the surveys helped establish association and disassociation patterns which w ere then better scrutinized with the increased sensitivity of functional neuroimaging capabilities. The behavioral data showed a significant trend across groups Dep rived >Sat isfied >N on S mokers in response to the smoking exposure N on Smokers >Sat isfied /Dep rived for the emotion of changes in BOLD signal in the brain areas involved in visual processing, memory, NAc, and prefrontal cortex The regions in the b rain that responded to the stimulation gradient of smoking cues further supports the VTA reward circuit pathway modeling addiction. treatment of CET for monitoring and contr olling the level of craving in addicted smokers. The regions in the brain that responded to the stimulation gradient of smoking cues serve as helpful targets in terms of th e diagnosis and progress of treatment of CET for monitoring and controlling the level of craving in addicted smokers. In general, we demonstrated that emotions in media communications can be measured in terms of a multidimensional model utilizing fMRI. We applied the fMRI

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96 technique to uncover the neurophysiologic changes in the brain in response to substance dependence (e.g., alcohol and smoking). We combined fMRI data with data from task performance and surveys and showed that changes in brain activation r eflect related behaviors. In the future, I hope to explore more topics tangentially related to these projects. I would like to investigate the neural correlates for the emotion of dominance as expressed in our AdSAM study and investigate neural correlates of disgust involved as a deterrent to smoking. Furthermore, there should be an opportunity to expand our pilot study and investigate the effects of high dose, medium dose, and 0 dose (placebo) alcohol on neural timing and disinhibition using the TL paradi gm. Lastly, it would be most interesting to re examine the results using different analyzing models such as Independent Components Analysis (ICA), Within condition Interregional Covariance Analysis ( WICA ), and Granger Causality A nalysis (GCA) for functiona l connectivity.

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107 BIOGRAPHICAL SKETCH Nelson J. Klahr was born in Pittsburgh, PA but lived in Miami Dade County for most of his childhood and graduated from North Miami B each Senior High School. Afterwards, he remained local and completed his Bachelor of Science degree in b iomedical e ngineering from the University of Miami in Coral Gables, FL. Before pursuing graduate studies, he worked in biomedical sales for Galix Inc. a nd taught Kaplan SAT classes and high school biology and medical research honors classes in the Medical Magnet Program at Stranahan High School in Fort Lauderdale, FL. He continued his passion for teaching as graduate student by serving as a teaching assi stant in N euroanatomy, as an instructor in the SSTP, as a course coordinator in Medical Bioethics, and as a teacher of Anatomy and Environmental Science at nearby Hawthorne Middle High School. As a graduate research assistant, he has represented the Depart ment of Psychiatry at many international conferences including the Organization of Human Brain Mapping (OHBM) and the International Society of Magnetic Resonance Imaging in Medicine (ISMRM). He is currently interviewing for several post doctorial research positions in the field of fMRI and also hopes to continue ties to the field of education. His personal interests include tennis, chess, and bartending.