1 AGE AND HIV EFFECTS ON CEREBRAL WHITE MATTER AND COGNITIVE FUNCTIONING By TALIA R. SEIDER A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2014
2 2014 Talia R. Seider
3 ACKNOWLEDGMENTS This work was supported by the National Institute of Health (Grant R01 MH074368) and the Lifespan/Tufts/Brown Center for AIDS Research (Grant P30 AI042853). Research was facilitated by the infrastructure and resources provided by the Lifespan/Tufts/Brown Center for AIDS Research and The Miriam Hospital Immunology Center. This work was also facilitated by the author's mother, who provided emotional support and practical edits, her father and siblings, who provided encouragement and humor throughout the extended writing process, and her mentor and chair, Dr. Ronald Cohen, whose patience and wisdom are inspiring, whose energy is contagious, a nd who creates a nurturing and enriching environment for all those who have the privilege of working with him. The author would also like to recognize Tiffany Cummings, who provided visual ratings of white matter damage to validate the method used in the c urrent research, and the author's supervisory committee for their priceless guidance and for their endurance in reading such a lengthy thesis. Thank you.
4 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ ... 3 LIST OF TABLES ................................ ................................ ................................ ............. 6 LIST OF FIGURES ................................ ................................ ................................ ........... 7 ABSTRACT ................................ ................................ ................................ ...................... 8 CHAPTER 1 INTRODUCTI ON ................................ ................................ ................................ ..... 10 Introduction to the Problem ................................ ................................ ...................... 10 Background ................................ ................................ ................................ .............. 10 Cerebral White Matter and Cognition in Healthy Aging ................................ ........... 12 White Matter and Cognitive Aging ................................ ................................ ..... 12 White Matter Disease ................................ ................................ ........................ 12 White Matter and Cognition ................................ ................................ ............... 15 Neuropathological and Neurocognitive Manifestations of HIV ................................ 17 White Matter and HIV/AIDS ................................ ................................ ............... 17 White Matter and Cognition in HIV/AIDS ................................ ........................... 19 Specific Aims ................................ ................................ ................................ ........... 21 Specific Aim 1 ................................ ................................ ................................ .... 21 Specific Aim 2 ................................ ................................ ................................ .... 22 Specific Aim 3 ................................ ................................ ................................ .... 22 Specific Aim 4 ................................ ................................ ................................ .... 22 2 METHODS ................................ ................................ ................................ ............... 24 Participants ................................ ................................ ................................ .............. 24 Inclusion/Exclus ion Criteria ................................ ................................ ............... 24 Sample Characteristics ................................ ................................ ..................... 24 Neurocognitive Assessment ................................ ................................ .................... 25 MRI Data Acquisition ................................ ................................ ............................... 26 MRI Data Analysis ................................ ................................ ................................ ... 27 White Matter Hyperintensity Analysis ................................ ................................ 27 Diffusion Tensor Analysis ................................ ................................ .................. 29 Segmentation of White Matter Regions of Interest ................................ ........... 29 Statistical Analyses ................................ ................................ ................................ .. 30 Aim 1: Effects of Age and HIV on White Matter ................................ ................ 30 Aim 2: Relationship Between WMH and FA ................................ ...................... 31 Aim 3: Effects of HIV Associated Clinical Factors on White Matter .................. 32 Aim 4: Relationship Between White Matter and Cognitive Functioning ............ 32
5 3 RESULTS ................................ ................................ ................................ ................ 38 Participant Characteristics ................................ ................................ ....................... 38 Aim 1: Effects of age and HIV on White Matter ................................ ....................... 38 Aim 2: Relationship Between WMH and FA ................................ ............................ 42 Aim 3: Effects of HIV Associated Clinical Factors on White Matter ......................... 43 Aim 4: Relationship Between White Matter and Cogni tive Functioning ................... 43 4 DISCUSSION ................................ ................................ ................................ .......... 47 Aim 1: Effects of Age and HIV on White Matter ................................ ....................... 47 Aim 2: Relationship Between WMH and FA ................................ ............................ 51 Aim 3: Effects of HIV Associated Clinical Factors on White Matter ......................... 52 Aim 4: Relationship Between White Matter and Cognitive Functioning ................... 53 Limitations and Future Directions ................................ ................................ ............ 56 Conclusions ................................ ................................ ................................ ............. 57 LIST OF REFERENCES ................................ ................................ ................................ 58 BIOGRAPHICAL SKETCH ................................ ................................ ............................. 72
6 LIST OF TABLES Table page 2 1 Sample characteristics ................................ ................................ ........................ 33 2 2 Cognitive assessment measures grouped by domain ................................ ......... 34 2 3 White matter regions of interest grouped by lobe ................................ ................ 36 3 1 Regression coefficients for FA in fron tal white matter regions ............................ 45 3 2 Cognitive testing means and standard deviations ................................ ............... 46
7 LIST OF FIGURES Figure page 2 1 White matter hyperintensity (WMH) mask ................................ ........................... 35 2 2 Cortical representation of FreeSurfer automatic segmentation ........................... 35 2 3 White matter regions of interest ................................ ................................ .......... 37 3 1 White matter hyperintensities (WMH) as a function of age for HIV and HIV+ participants ................................ ................................ ................................ .......... 45
8 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Master of Science AGE AND HIV EFFECTS ON CEREBRAL WHITE MATTER AND COGNITIVE FUNCTIONING By Talia R. Seider May 2014 Chair: Ronald Cohen Major: Clinical Psychology The Human Immunodeficiency Virus 1 (HIV) and aging are both known risk factors for cerebral white matter deterioration and declines in neurocognitive functioning. This study investigated HIV effects on white matter and cognitive functioning in the context of aging 103 HIV seropositive (HIV+) and 63 seronegative (HIV ) individuals without dementia aged 23 79 received neurocognitive assessments and magnetic resonance imaging (MRI). Fractional anisotropy (FA) was used t o assess white matter integrity and w hite matter hyperintensities (WMH) were quantified to measure degree of white matter damage. Linear regression predict ed FA and WMH from age, HIV status, and the age by HIV interaction. Correlational analyses analyze d the relationship between WMH and FA. H IV associated clinical factors predicted WMH and FA in separate linear regression models to further investigate HIV effects on white matter structure. Finally, the associations of neurocognitive variables with WMH and with FA were analyzed using multivaria te and univariate regressions, respectively A significant age by HIV interaction revealed a significant effect of age on WMH volume for HIV+ participants only. WMH volume was negatively correlated with FA in
9 frontal and parietal regions, and this relatio nship was stronger when examining only subjects with HIV. G reater HIV disease severity and Hepatitis C coinfection were associated with greater WMH volumes but not with FA. Finally, WMH volume was negatively associated with scores on speeded psychomotor te sting, FA was positively assoc iated with learning and memory, and a ssociations were str onger in HIV+ participants only versus the group as a whole. Age and HIV interact to produce cerebral white matter damage. WMH and FA are associated with different aspects of cognitive functioning. WMH and FA are more strongly related to each other and to cognitive functioning in HIV+ participants, suggesting that they are showing greater age related chan ges than controls. Possible mechanisms are discussed and future directions addressed.
10 CHAPTER 1 INTRODUCTION Introduction to the Problem The Human Immunodeficiency Virus 1 (HIV) affects over 35 million people worldwide 1 with 50,000 new cases each year in the United States alone 2 It is a public healt h concern that cost the United States an estimated $36.4 billion in 2002 3 Although the availability of combination antiretroviral therapies (cART) has dramatically reduced morbidity and mortality rates, a large portion of individuals experience HIV associated neurocognitive disturbances that impact quality of life for patients and their families. HIV directly infects the brain and, based on clinical presentation and early autopsy studies, attacks primarily subcortical structures such as the basal ganglia. Later evidence suggests that, as people live longer with HIV, effects of the virus are broadening to involve cortical structures as well 4 Not only are neurological changes associated with HIV evolving, but in the current era of widely available cART, the cognitive characteristics typically associated with HIV are also changing 5 HIV is evolving from a severe medical condition with a poor prognosis to a chronic, manageable illness. As the HIV infected population ages, it is important to see how the neurological and cognitive profile of these patients is evolving, and how those characteristics change in the context of older age. Background HIV causes suppression of human immune response through destruction of immune cells. As the virus progresses, it causes a reduction of CD4+ T cells, a type of white blood cell that coordinates immune response, and thus leaves the host immunocompromised and vulnerable to infectious disease. Opportunistic infections are
11 those that affect people with compromised immune system function, and have been common amon g HIV positive (HIV+) people particularly before the availability of antiretroviral medications. A diagnosis of Acquired Immune Deficiency Syndrome (AIDS) is made in HIV+ patients if an opportunistic infection occurs or if CD4 cell count drops below 200 indicating compromised immune function. In addition to suppressing the replication of immune cells throughout the body, HIV direct ly a ffects the central nervous system (CNS). HIV enters the brain soon after infection 6, 7 and spreads from infe cted mo nocytes to uninfected microglia and astrocytes which are types of glial cells. Glial cells provide support and protect neurons as well as maintain homeostasis I nfected glial cells activate an immune response that causes direct damage, including se cretion of endogenous neurotoxins and neurotoxic viral proteins, and may also stimulate uninfected neurons 8 The blood brain barrier (BBB) permeability becomes affected, making it easier for infected proteins and cytokines to infiltrate the brain. Furthermore, infected glial cel ls become dysfunctional and are no longer able to maintain myelin and homeostasis 9 At the pathological level, people with HIV and AIDS show generalized atrophy, especially in subcortical regions, and white matter damage. This is consi stent with the behavior changes seen in these patients. Early research showed that as many as 50% of patients exhibited a collection of neurological symptoms known as the AIDS Dementia Complex (ADC) 10 14 The ADC was characterized as a "subcortical de mentia" because, though symptoms were diffuse, a primary component was slowing of cognitive processing, speech, and movement. Also common were concentration difficulties and behavioral changes including apathy and withdrawal.
12 The first antiretroviral ther apies were introduced in the 1980s, and the use of combination antiretroviral therapy (cART) became widespread in the later 1990s. Since the advent of antiretroviral therapies, the re were reductions in morbidity and mortality, and the clinical profile of the disease began to change. I ncidence of dementia n HIV+ individuals decreased considerably 15 Still, HIV continues to be a public health concern with major quality of life effects on patients and their families 16, 17 Although dementia incidence has declined, HIV associated neurocognitive disorder (HAND) persists in as muc h as 50% of individuals 18 20 and studies show that the odds of HAND are up to three times greater in individuals over age 50 21, 22 Age as a risk factor for HAND is especially important in the cART era, as the number of individuals surviving with HIV beyond the age of 50 is ra pidly increasing 23, 24 Therefore, there is a compelling need to study the effects of HIV on brain structure and function within the context of aging. Cerebral White Matter and Cognitio n in Healthy Aging White Matter and Cognitive Aging Brain tissue can be divided into grey matter, composed of cell bodies, and white matter, composed of glial cells and myelinated axons. Myelinated axons transmit information between brain regions, so the primary function of white matter is to facilitate communication between cortical regions and also between the cortex and subcortical structures. Changes in white matter structure have been associated with cognitive changes in healthy aging, and are thus th ought to underlie or at least contribute to age associated cognitive decline 25 White Matter Disease White matter disease in the brain is a consequence of chronic ischemia and direct damage from toxins leaking through broken down membranes 26, 27 Chronic
13 ischemia is the result of endothelial damage, or injury to the inner lining of blood vessels, which leads to rest ricted blood flow to brain tissue. Risk factors for endothelial damage include high blood pressure and diabetes. Endothelial damage also causes a breakdown of the blood brain barrier, which allows potentially harmful substances in blood plasma to more easi ly access brain tissue. White matter damage can be examined using magnetic resonance imaging (MRI). In fluid attenuated inversion recovery (FLAIR) sequences, grey matter appears lighter on MRIs, white matter appears darker, cerebrospinal fluid (CSF) appe ars black, and diseased white matter appears bright white, or hyperintense. White matter hyperintensities (WMH) on MRIs are thus markers of white matter damage. WMH is synonymous with leukoariosis, and other names for white matter disease include abnormal white matter, small vessel disease, and white matter changes. The total amount of white matter disease has been referred to as white matter burden or white matter load. Pathological findings in regions with WMH include myelin pallor (histologically abnorma l white matter), mild gliosis (a marker of glial cell response to damage), and decreased tissue density associated with loss of myelin and axons 28 Older age is consistently found to be one of the most important independent predictors of WMH burden 26, 29 32 Prevalence of white matter damage in the healthy aging population is difficult to determine, as most studies use qualitative rating scales, which can have variable definitions for what constitutes WMH and its degree of severity. Nevertheless, there is general agreement that they exist in about one third of people over the age of 60 and almost all people over the age of 80 33 35 WM H in frontal and parietal regions have been especially associated with older age and greater cognitive
14 dysfunction 36 39 and longitudinal studies show greater age relat ed decline in anterior versus posterior white matter regions 40 43 Diffusion tensor imaging (DTI ) assesses white matter structure differently, by measuring the diffusion of water molecules in the tissue. Since white matter is made up of myelinated axons, water flows more quickly in the direction of the axonal projections than in the perpendicular dir ection. When axonal networks are disrupted, water flows more freely rather than unidirectionally. This occurs if myelin is damaged or absent, as well as in the presence of edema, which can result from ischemia or neuroinflammation 44 Originally presented in 1994 45 this imaging method has become increasingly popular as it yields measurements that are capable of detecting very subtle white matter changes. The most commonl y used DTI metric is fractional anisotropy (FA). The more anisotropic, or unidirectional, the flow of water molecules in white matter, the higher the integrity and the lower the damage. Therefore, higher FA is indicative of healthier white matter and less damage. Lower FA has been noted in the presence of normal appearing white matter and is therefore seen as a more sensitive measurement of white matter damage than WMH 46 Declines in FA have also been associated with increased age 47, 48 with ant erior regions showing the greatest changes 49 51 While FA and WMH are related, they do not measure the same properties of white matter structure. FA measures microstructural aspects of white matter, whereas WMH show macroscopic structural damage that reflects more permanent lesions. Studies have shown decreased FA in areas of WMH 52 and in normal appearing white matter voxels as they increase in proximity to WMH 53 Yet the negative correlations
15 seen between FA and WMH are much weaker than would be expected if they reflected the same degenerative process 54 In one study of 112 older participants r anging from cognitively normal to demented, a lower summary score representing whole brain FA was significantly associated with higher WMH volumes in all lobes of the brain, but WMH volumes were associated with several cognitive outcomes and with dementia diagnosis whereas global FA was not significantly linked to any cognitive indices or presence of dementia 38 The discrepancy between the two measures' associations with cognitive functioning shows that they are markers of different neurological indices. Another study found that lower baseline FA and higher FLAIR intensity were independent predictors of future conversion from normal white matter to WMH over time, showing that not only are these measures u nique in what they represent, but they are also independently important 55 FA cannot simply replace WMH as a measurement of white matter, as WMH predicted future white matter damage, even when FA was in the model. It is therefore important to look at both measures when studying white matter damage in aging and diseas e. White Matter and Cognition It has been well established from a large body of research that WMH load is strongly associated with performance on timed measures 56 60 Rapid process ing is required in many tests on motor and executive functioning so it is not surprising that WMH have been associated with those domains as well 58, 60 There is also research that shows WMH to be related to episodic memory 58, 61 though it is not entirely clear that these deficits exist in the absence of cognitive slowing. Furthermore, several studies have shown strong relationships between WMH and processing speed in the absence of significant associations with other aspects of cognitive functioning 56, 62 showing that
16 processing speed is likely the first cognitive ability to be affected by changes in white matter measured by WMH. It has been suggested that there is a threshold effect such that level of WMH does not associate with cognitive functioning unless it is of a certain severity 63 65 Still, a meta analytic study concluded that among non demented elderly individuals, WMH associations with cognition are modest in general and strongest for psychomotor processin g speed and executive functions 66 The association between WMH and frontal lobe function s may be explained by the fact that WMH represent disruptions to white matter circuits, which tend to converge in the frontal lobes 67, 68 Minor changes in FA have historically been associated with functions of frontal and subcortical brain regions. Specifically, studies of cognitively normal subjects across a wide range of ages show t hat lower overall FA has been associated with worse information processing and motor speed 54 and deficits in executive functions 50 including working memory 69 One study of healthy and cognitiv ely normal adults aged 18 83 showed that whole brain FA correlated modestly with memory and executive functions and correlated to the greatest extent with processing speed 25 Thus, processing speed and executive functions are some of the ear liest cognitive constructs to be affected by changes in FA. This may be explained by the fact that frontal white matter is composed of many long subcortical fiber projections, whereas posterior white matter has more short connections known as U fibers, lea ving frontal white matter more vulnerable to the effects of aging and other risk factors 70, 71 and thus explaining the connection between frontal lobe functions (i.e. executive functions) and early changes in FA 72
17 Neuropathological and Neurocognitive Manifestations of HIV White Matter and HIV/AIDS Early neuropathological studies of AIDS patients showed prevalent white matter damage at autopsy. Navia et al. examined the brains of 70 AIDS patients microscopically and found that 64 of them (91.4%) had diffuse pallor of the white matter 73 Pallor was variable in regional intensity, but "had a generally diffuse appearance that encompassed wide irregular areas of white matter." Neuroimaging studies provided supporting evidence that diffuse white matter abnormalities occur in patients with AI DS. Grant et al. examined MRIs of 23 relatively young men with AIDS or AIDS related complex (mean age 35) and found that about 43% of patients had multiple discrete areas of WMH and/or large WMH areas 74 Olsen and colleagues reviewed MRIs from 365 AIDS patients (mean age = 38, range 23 69) and found that 112 (31%) had signal abnormalities confined to white matter 75 Since WM H do no t typically appear in healthy brains until more advanced age typically 60s 80s, the fact that both studies showed such high prevalence of WMH in patients with mean ages in the 30s is significant and shows that WMH occur earlier in people with AIDS. Several clinica l risk factors have been linked to white matter damage in HIV/AIDS, particularly greater disease severity 76 For example, pathology has been associated with plasma viral load 77 especially in periventricular areas 78 Current CD4 cell count has also been linked with great er amounts of abnormal appearing white matter on MRI, such that patients who have AIDS (CD4 cell count <200) have greater amounts of white matter damage 79 81 and are more likely to develop white matter hyperintensities over one year 82 History of greater disease severity as measured by CD4 nadir, the lowest historical CD4 count, has been shown to be a significant risk
18 factor as well 77, 83 Autopsy studies examining the severity of HIV infection in the central nervous system also found relationships between greater viral burden in the brain and larger amounts of abnormal white matter on M RI 84, 85 Coinfection with Hepatitis C Virus (HCV) is another risk factor that has been associated with white matter changes 83 Finally, older age has been shown to relate to greater white matter damage in HIV 86 Studies have shown that patients with AIDS frequently have white matter damage, that the damage occurs at younger ages in these patients than it does in people without HIV, and that it is g enerally diffuse and widespread; however, HIV+ individuals do not necessarily h ave more white matter damage than seronegative controls. In case controlled studies from the pre cART era, white matter damage did not differentiate between participants with and without HIV 81, 87 Research from the post cART era showed that HIV+ participants with dementia had signifi cant neuronal injury, but non demented participants with HIV were not significantly different from the seronegative controls 88 These studies suggest that overall white matter damage may not differentiate between HIV infected and uninfected participants, except in the case of greater disease severity. Since W MH measurements do not reliably differentiate between people with and without HIV, many researchers have turned to DTI to measure white matter differences between these populations, as these measurements are more sensitive to subtle changes in white matter DTI studies show that HIV impacts white matter integrity throughout the brain. Case controlled studies show that HIV is associated with lower FA globally 89 and in specific areas such as the corpus callosum 90 92 internal capsules 93 and white matter in the frontal 90, 93 and parietal lobes 90 Still, some research does not
19 show significant FA differences between people with HIV and seronegative contro ls 94 Substantial evidence indicates that greater disease severity is an important risk factor for larger abnormalities on DTI 46, 90, 94 97 White Matter and Cognition in HIV/AIDS Some studies in the pre cART era showed relationships between white matter damage and cognition though in general, observations were made regarding the presence or absence of dementia rather than regarding specific cognitive functions Of the 112 patients with white matter abnormalities in Olsen's 1998 study, 60 had clinical data, and the researchers found diffuse, widespread white matter damage to be associated with the AIDS Dementia Complex. Other types of white matter damage (patchy, focal, and punctate ) were not significantly associated with the presence of dementia 75 In Aylward's research, greater white matter loss was associated with the presence of dementia 98 In Navia's study, however, 46 out of the 70 patients studied had dementia, and there wer e no indications that those with dementia had greater severity of white matter pallor 73 A study by McArthur and colleagues also found no association between degree of white matter damage and cognitive functioning 87 Importantly, in this study, only 14 patients had symptomatic HIV. Therefore, it may be the case that associations between white matter damage and cognition are only observable in cases of dementia or extreme cognitive dysfunction. In part due to non sig nificant results in the literature, and in part due to the advent of imaging methods that detect more subtle white matter damage such as DTI, researchers focus less on WMH in the post cART era. With successful antiretroviral treatment, there are fewer deme ntia cases and fewer findings showing relationships between WMH and cognition. One study of WMH in the post cART era found that more
20 white matter abnormalities was significantly associated with lower scores on the HIV dementia scale and poorer visuoconstru ctive coordination 78 but participants spanned a range of cognitive abilities. There is a paucity of research on WMH a nd cognitive functioning in non demented, successfully treated populations. Recent studies tend to focus on DTI parameters and how they relate to cognitive functioning in HIV. Tate and colleagues found that lower global FA was significantly associated wit h lower scores in processing speed, motor speed, and executive function, and that FA was more strongly related to cognitive functioning in HIV+ subjects compared to seronegative controls 89 Zhu et al. found that lower FA was significantly related to lower pro cessing speed and verbal fluency scores in a non demented HIV+ population, and noted that verbal fluency had stronger associations with FA in the left hemisphere, while processing speed had widespread associations with FA involving both hemispheres 99 Other studies of HIV+ populat ions have found significant relationships between FA and verbal and visual memory, working memory, visuoconstruction, and motor speed 92, 97 These findings parallel the relationship between FA and subcortical and frontal lobe functions in studies of healthy aging. Still, not all research reports significant associations between FA and cognitive functioning in HIV 100, 101 While there were many pre cART studies showing relationships between greater disease severity, extensive white matter damage, and severe neurocognitive dysfunction, patients treated with antiretrovirals are living without developing AIDS, have relatively intact white matter, and have lower rates of dementia. It is therefor e necessary to investigate the relationship between HIV, white matter, and cognitive
21 functioning in the context of well controlled HIV. Furthermore, as people are living longer with HIV, studies of age effects in these populations are becoming increasingly relevant. Specific Aims Specific Aim 1 The primary aim of this investigation was to evaluate the effects of age and HIV status on white matter in a non demented population. Advanced age and HIV have both been shown to be risk factors for greater WMH load and decreased FA, so the goal was to see the impact of each risk factor and whether they interact to produce such changes in white matter. Hypothesis 1: It was hypothesized that age and HIV status would interact in their associations with white matter da mage. Research shows that WMH burden is not significantly different in individuals with HIV versus healthy controls, unless perhaps in the case of current immune deficiency. Since most HIV+ subjects had intact immune function at the time of enrollment, HIV status was not expected to be significantly related to WMH volume. Instead, HIV s tatus was expected to impact the relationship between age and WMH volume, such that age would have a greater effect on WMH volume in people with HIV. Hypothesis 2: Subjects with HIV were predicted to have lower FA than seronegative controls, particularly in frontal areas, based on HIV research showing the frontal lobe to be preferentially affected. Additionally, age was expected to have a stronger association with frontal and parietal FA in the HIV+ group, as indicated by significant age by HIV interaction s.
22 Specific Aim 2 The second aim of the study was to investigate the relationship between white matter damage as measured by WMH and FA, in the cohort as a whole and in the participants with HIV. Given that these measures reflect different aspects of whi te matter structure, it is important to consider both when evaluating white matter changes in HIV. Hypothesis: Greater WMH volume was hypothesized to be associated with decreased FA in all parts of the brain. Based on research showing greater ranges of wh ite matter damage in HIV versus controls, the relationship between WMH and FA was expected to be stronger in participants with HIV. Specific Aim 3 This study also aims to investigate which HIV associated clinical factors relate most strongly to white matte r damage in HIV+ individuals. Hypothesis: Indices of disease severity were expected to be the most strongly associated with WMH volume and regional FA. In particular, lower current CD4, positive HCV status, and history of AIDS were predicted to be signifi cantly linked with greater WMH volume and lower FA values. Specific Aim 4 The final goal of the study was to determine the relationship between the white matter measures (WMH and FA) and cognitive functioning in the cohort as a whole and in individuals wi th HIV specifically. Hypothesis 1: Given that WMH have been linked most consistently with processing speed, and that the current study cohort was non demented and had relatively low levels of overt white matter damage, WMH volume was expected to have
23 the strongest associations with processing speed tests, with greater WMH relating to poorer performance, but not any other cognitive domains. Some of the executive functioning measures require speeded processing, so it was predicted that there might be some re lationships between speeded executive functioning tasks and WMH. For the same reason, possible WMH effects on motor processing were anticipated. Hypothesis 2: Global FA was expected to relate to processing speed and executive functioning measures as these cognitive domains are historically linked to measures of white matter integrity.
24 CHAPTER 2 METHODS Participants Participants were recruited from the outpatient Immunology Center of the Miriam Hospital and the Brown University Center for AIDS Research as part of an NIH sponsored study of HIV associated brain dysfunction. Seronegative (HIV ) controls were either recru ited because they were family, friends, and visitors of the seropositive (HIV+) participants, or they responded to fliers posted in the community. The study was approved by the IRB and informed consent was obtained from all participants. Inclusion/Exclusion Criteria Prospective subjects were excluded if they had any of the following: history of major head injury; neurologic condition such as dementia, seizure disorder, stroke, or opportunistic brain infection; major psychiatric illness tha t might effect brain function such as schizophrenia, untreated bipolar disorder, or any other psychotic or thought disorder; or current illicit drug use as defined by substance dependence in the past six months or a positive urine toxicology screen for coc aine, opiates, or illicit stimulants or sedatives. HIV infection was documented by enzyme linked immunosorbent assay (ELISA) and confirmed by Western blot. Participants were evaluated for active hepatitis C virus (HCV) infection, which was defined as detec table serum HCV by polymerase chain reaction. Sample Characteristics 166 subjects, consisting of 103 HIV+ subjects and 63 HIV controls, received a full neuropsychological assessment and MRI scans. Demographic and clinical characteristics of the sample are presented in Table 2 1. Participants ranged from 23
25 79 years, but the oldest participant with HIV was 65. The Center for Epidemiologic Studies Depression Scale (CES D) was administered to assess for depression 102 With a maximum score of 60, scores 16 through 26 indicate mild depression and scores 27 and above indicate major depression 103 Based on these criteria, 49% of the sample showed minimal or no depression, 26% had mild depression, and 25% had major depression. The average HIV+ participant met criteria for mild depression, whereas the average seronega tive control did not. Lifetime drug use was assessed using the Kreek McHugh Schluger Kellogg (KMSK) scale 104 Using these criteria, the life time rates of substance dependence for the HIV+ participants were 50.5% for alcohol, 77.8% for cocaine, and 16.5% for opiates. For the HIV participants, the rates of lifetime substance dependence history were 41.3% for alcohol, 22.2% for cocaine, and 6.3% for opiates. Among the HIV+ participants, the average time since diagnosis was about 13 years, 83.5% were on cART, 37% were HCV+ at the time of enrollment, and 60% had a history of AIDS defined as CD4 nadir < 200. 68% of HIV+ participants did not have detectable plasma HIV RNA at the time of enrollment and the average current CD4 cell count was 455. These indicate a low burden of infection in the majority of participants when they enrolled in the study. 9.7% of HIV+ participants had a CD4 count below 20 0 at the time of enrollment, but 60% of those participants had undetectable plasma HIV RNA. Overall, this was a relatively healthy sample of people with HIV. Neurocognitive Assessment Neurocognitive functioning was assessed in five domains: processing spee d, attention/working memory/executive functioning, learning, memory, verbal fluency, and motor speed. Assessment measures were chosen and divided into cognitive domains based on research showing their sensitivity to H IV associated neurocognitive disorder
26 ( H AND ) 105, 106 Tests included the Trail Making Test, Parts A and B 107 the Digit Symbol Coding, Symbol Search, and Netter Number Sequencing subtests of the Wechsler Adult Intelligence Scale Third Editio n (WAIS III) 108 the Stroop Color and Word Test 109 the Hopkins Verbal Learning Test Revised (HVLT R) 110 the Brief Visuospatial Memory Test Revised (BVMT R) 111 the Controlled Oral Word Associatio n Test (COWAT) 112 and the Grooved Pegboard Test 113 Neurocognitive measures grouped by domain are presented with brief descriptions in Table 2 2. Raw scores were converted into t scores corrected for age, educ ation, and gender based on published norms 110, 111, 114 Composite scores were calculated for each cognitive domain by taking the average t score for all measures within the domain. MRI Data Acquisition Magnetic resonance imaging for all subjects was acqu ired using a Siemens Tim Trio 3T imager located at the Brown University MRI Research Facility in a process previously described 115 High resolution structural MRI of the whole brain was acquired in the sagittal plane usi ng a T1 weighted MPRAGE pulse sequence and in the axial plane using a T2 weighted fluid attenuated inversion recovery ( FLAIR ) TSE sequence. The T1 weighted sequence had TE/TR = 3.06/2,250 ms, flip angle = 9Â¡, slice thickness = 0.86 mm. The FLAIR sequence h ad TE/TR 149/9,000 ms, flip angle = 120Â¡, slice thickness = 3 mm no gap, interleaved. Diffusion weighted images (DWI) covered the whole brain and were acquired using a double spin echo echo planar pulse sequence in the axial plane with TE = 103 ms, TR = 10 .060 ms, in plane resolution = 1.77x1.77 mm, and slice thickness = 1.8 mm. DWI with b value = 1,000 s/mm 2 were acquired in 64 diffusion gradient directions. Ten images with no diffusion encoding (non DWI) were acquired as baseline for diffusion tensor fitt ing.
27 MRI Data Analysis White Matter Hyperintensity Analysis White matter hyperintensities (WMH) were quantified using a semi automated approach developed at Brown University by Dr. Steven Correia. Cortical reconstruction and volumetric segmentation was pe rformed on T1 weighted images with the FreeS urfer image analysis suite, which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/) 116 Automatic segmentation (aseg) files and skull stripped T1 images were obtained using this process. A binary mask was created from the skull stripped T1 using the FMRIB Software Library (FSL) 117 119 All image registrations were performed using FMIB's Linear Image Registration Tool (FLIRT) 120 122 FLIRT is a fully automated tool for linear (affine) brain registration, and is a robust and accurate way to arrange the pixels of two images so that they are in the same space and have the same dimensions. Rigid body registrations between the FLAIR and skull stripped T1 files were performed with 6 degrees of fre edom (df) and a mutual information cost function. The resulting transformations were applied to the binary brain mask and aseg files, using nearest neighbor interpolation to preserve the integer labels. The 3dcalc program from the Analysis of Functional Ne uroImages (AFNI) software 123, 124 was used to create a non white matter mask from the aseg file by combining the ventricles, cortical grey matter, brainstem, and cerebellum. The FLAIR image was multiplied by the binary brain mask to remove the skull. The skull stripped FLAIR was then processed using FMRIB's Automated Segmentation Tool (FAST) 125 to bias correct the image in case of field inhomogeneity. FAST automatically segments 3D brain images into different tissue types while also correcting for spatial in tensity variations (i.e., bias field or RF inhomogeneities), and it was for the latter purpose that the
28 program was used. The bias corrected skull stripped FLAIR image was then binarized, and the non white matter mask was subtracted to create a white matte r mask. The binary brain mask that had been created from the skull stripped T1 was eroded to eliminate residual skull and cerebrospinal fluid (CSF) and then multiplied by the white matter mask. This eroded binary white matter mask was then multiplied by th e bias corrected, skull stripped FLAIR to create the final white matter mask. A threshold was created for each brain at 1.25 standard deviations above the median intensity This threshold was chosen through an iterative process with visual inspection as an optimal compromise between too conservative, resulting in exclusion of WMH voxels, and too liberal, resulting in inclusion of voxels that did not contain WMH. A WMH mask of all voxels that exceeded the threshold intensity within the white matter was creat ed using 3dcalc. To minimize false positives from voxels included due to minor movement artifact or rimming effects, all clusters smaller than 5 voxels were removed. WMH masks were then manually edited according to strict rules to remove false WMH inclusio ns in a consistent fashion across brains. Figure 2 1 shows adjacent FLAIR images of a brain from the current sample with moderate WMH and the edited WMH mask overlaid on the one FLAIR image. Voxels were then summed to create a final WMH volume To account for individual differences in head size, WMH volumes were adjusted based on intracranial volume (ICV) by creating a percentage (WMH/ICV*100). Thus, in all analyses, WMH refers to the percentage of ICV. To determine measurement reliability of this WMH volu me quantification process, 14 FLAIR images with varying degrees of WMH burden were rated using a well
29 validated visual rating scale 126 Correlation between methods was 0.83, indicating high inter method reliability. Diffusion Tensor Analysis Diffusion tensor analysi s was performed using FSL and in a process previously described 115 To summarize, non DWI images were co registered to correct for movement using FSL FLIRT rigid body registrations and 6 df. The images were then averaged to create a baseline for subsequent tensor fitting for the purpose of increasing signal to noise ratio. To account for movement and distortions induced by the eddy current, th e 64 DWIs were registered to the non DWI baseline image using 12 parameter affine registrations. The diffusion gradient vectors for each individual DWI were adjusted according to the corresponding affine transformations to account for the spatial transform ations 127 In order to avoid negative eigenvalues, diffusion tensor estimations were performed using a nonlinear iterative method 123 The three principal eigenvectors were then computed, as were associated eigenvalues of the tensor characterizing the diffusion ellipsoid. Fractional anisotropy (FA) was derived from the eigenvalues using standard formulas 128 Segmentation of White Matter Regions of Interest FreeSurfer was used to automatically segment the brain tissue using cortical landmarks on T1 weighted MRIs 129, 130 Figure 2 2 depicts cortical representations of the automatic segmentation. Subcortical white matter voxels were segmented and labeled based on proximity to the identified cortical landmar ks in a process previously described in detail 37, 130 28 white matter regions of interest (ROIs) were identified in each hemisphere, including ten regions in the frontal lobe, nine in the temporal lobe, five in the parietal lobe, and four in the occipital lobe. Table 2 2 lists the ROIs, grouped by
30 lobe, and Figure 2 3 shows white matter underlying each ROI. Bilateral ROIs were combined to reduce the number of necessary comparisons and increase th e number of voxels in each ROI. T1 images were registered to average non DWI images using FLIRT, with rigid bod y and mutual information parameters. The resulting transformations were applied to the segmented ROIs using nearest neighbor interpolation. An FA threshold of 0.3 was applied to the transformed ROIs to exclude any voxels that were not white matter. White matter integrity in each ROI was measured by calculating average FA values for each region. ROI FA values are used in analyses, as well as average FA values computed for each of the four lobes. Statistical Analyses All statistical analyses were performed u sing IBM SPSS Statistics version 21. Alpha was set at .05, two tailed, unless otherwise specified. Differences in demographic and clinical variables between HIV+ and HIV participants were examined using analysis of variance (ANOVA) for continuous variable s and Pearson's x 2 tests for categorical variables. The same methods were used to analyze differences between participants whose data were included in WMH analyses and those who had missing or uninterpretable data. Aim 1: Effects of Age and HIV on White Ma tter To assess the interaction effects of age and HIV status, a n age by HIV interaction term was created by multiplying the age and HIV variables and the interaction term was then orthogonalized to avoid multicollinearity among predictors. This was achiev ed by regressing the effects of age and HIV out of the product term in a process called residual centering 131, 132 To explain, a linear regression was conducted
31 with age and HIV as independent variables and the age by HIV product term as the dependent variable, and the unstandardized residuals from the regression were saved as a new variable. This new continuous variable represents the unique interaction between age and HIV that is not related to age or HIV individuall y. A linear regression was performed to examine how age, HIV status, and their interaction relate to WMH. The dependent variable in the regression analysis was WMH and the independent variables were age, HIV status, and the orthogonalized age by HIV produ ct term. To examine how age, HIV status, and their interaction affect white matter integrity, separate linear regression analyses were conducted for each of the 28 white matter ROIs, such that the FA for each ROI was the dependent variable, and age, HIV s tat us, and the orthogonalized age by HIV interaction term were the independent variables. To reduce the chance for type 1 error, the Bonferroni correction was applied to the familywise error rate of 0.05, controlling for multiple comparisons within each lo be of the brain. This allowed for more conservative tests of statistical significance while maintaining adequate statistical power. Thus, alpha was set at 0.005 for FA analyses of frontal regions, .006 for FA analyses of temporal regions, .01 for analyses of parietal regions, and .0125 for analyses of occipital regions. Aim 2: Relationship Between WMH and FA To analyze the relationship between WMH and FA, correlational analyses were performed between WMH volumes and mean FA values for each lobe of the brain. Thus, WMH was correlated with frontal FA, parietal FA, temporal FA, and occipital FA. The Bonferroni correction was used to control for multiple comparisons and alpha was set at 0.0125.
32 To test the hypothesis that the relationship between WMH and FA would be stronger in the HIV+ group, the same correlations were executed among only the HIV+ participants. Aim 3: Effects of HIV Associated Clinical Factors on White Matter To better characterize the relationship between HIV and white matter, linear regr ession analyses were conducted with HIV associated clinical variables as predictors and measures of structural white matter as outcome variables. Specifically, each regression included HIV duration and current CD4 level as continuous predictors and HIV RNA (detectable/undetectab le), HCV coinfection (infected/ uninfected), cART use (on cART/not on cART), and history of AIDS (CD4 nadir <200/CD4 nadir 200) as dichotomous predictors. Five regression analyses were run for the five white matter outcome variables : total WMH, and average FA for each of the four lobes. Aim 4: Relationship Between White Matter and Cognitive Functioning To investigate the relationship between WMH and cognitive functioning, multivariate regression analyses were performed with WMH as t he predictor and demographically corrected t scores for all tests within a cognitive domain as the dependent variables. Follow up univariate regression analyses were conducted in the case of significant multivariate outcomes to assess the relationship betw een the WMH and the individual cognitive tests for the given domain. To delineate the relationship between global FA and cognitive functioning, linear regression analyses were conducted for each cognitive domain where independent variables were mean FA va lues for all four lobes and the dependent variable was one of the six cognitive composite scores. WMH and FA analyses were run initially for the group as a whole, and then for individuals with HIV only.
33 Ta ble 2 1. Sample characteristics HIV+ ( n = 103) HIV ( n = 63) Demographic characteristics Mean (SD) Range Mean (SD) Range Age (years) 45.4 (9.9) 23 65 45.2 (13.3) 24 79 Education (years)* 12.5 (2.2) 6 18 13.7 (3.4) 6 20 % male 63.1 55.6 Race/ethnicityÂ¤ % Caucasian 51.5 73.0 % African American 26.2 15.9 % Latino 8.7 3.2 % Asian 1.0 1.6 % other race 12.6 6.3 Psychiatric characteristics CES D score* 21.1 (12.9) 0 53 13.6 (12.4) 0 48 % history alcohol dependence 50.5 41.3 % history cocaine dependence* 77.8 22.2 % history opiate dependence 16.5 6.3 Clinical characteristics % with active HCV* 36.9 11.1 HIV duration (years) 12.8 (7.0) 0 26 % undetectable HIV RNA 68.0 Current CD4 455 (242) 56 1320 % with history of AIDS 59.8 % on cART 83.5 One way ANOVA or Pearson's x 2 HIV+ vs. HIV p < .05 Â¤ Ethnicity analyzed in terms of overall composition, p < .05 Note: History of AIDS defined as CD4 nadir < 200.
34 Table 2 2. Cognitive assess ment measures grouped by domain Measures Brief Description Processing Speed Digit Symbol Coding Subject records symbols associated with numbers 1 9 based on a key at the top of the page Symbol Search Subject marks yes' or no' based on whether one of two target symbols matches a symbol in an adjacent set of four symbols Trail Making Test Part A Subject draws a line connecting 25 encircled numbers distributed on a page in order from 1 to 25 as quickly as possible Attention/Working Memory/Executive Stroop interference Subject reads the color of the ink in which words are printed, inhibiting the learned automatic response of reading the word, as quickly as possible Letter Number Sequencing Subject hears a list of numbers and letters and must remember items and recall nu mbers first, in order, then letters, alphabetically Trail Making Test Part B Subject draws a line connecting 25 encircled numbers and letters distributed on a page, switching between numbers and letters in order as quickly as possible Learning HVLT R total recall Sum of 3 learning trials, for each of which subject is read 12 words and immediately recalls as many words as possible BVMT R total recall Sum of 3 learning trials, for each of which subject sees 6 geometric figures at once for 10 seconds an d immediately draws as many figures as possible Memory HVLT R delayed recall Subject recalls as many words as possible from the learned list of 12 words after a 25 minute delay BVMT R delayed recall Subject draws as many figures as possible from the learned list of 6 figures after a 25 minute delay Verbal FAS letter fluency Subject names as many words as possible in 60 seconds beginning with the target letter Category fluency Subject names as many animals as possible in 60 seconds Motor Grooved Pegboard dominant hand Subject fits pegs into grooves on a pegboard as quickly as possible with the dominant hand Grooved Pegboard non dominant hand Subject fits pegs into grooves on a pegboard as quickly as possible with the non dominant hand
35 Figure 2 1. White matter hyperintensity (WMH) mask overlaid on a FLAIR image (left) adjacent to original FLAIR image (right), showing the results of the semi automated WMH quantification method used in the current study. Figure 2 2. Cortical representation of FreeSurfer automatic segmentation showing lateral (left) and medial (right) views. Yellow asterisks indicate cortex around the perimeter of the central sulcus that has been inflated to visually represent the entire su rface area. This image was previously published by Desikan et al. 130
36 Table 2 3. White m atter regions of interest grouped by lobe Frontal Temporal Parietal Occipital Caudal middle frontal gyrus Entorhinal cortex Inferior parietal cortex Cuneus cortex Lateral orbitofrontal cortex Fusiform gyrus Postcentral gyrus Lateral occipital cortex Medial orbitofrontal cortex Inferior temporal gyrus Precuneus cortex Lingual gyrus Paracentral lobule Insular cortex Superior parietal cortex Pericalcarine cortex Pars opercularis Middle temporal gyrus Supramarginal gyrus Pars orbitalis Parahippocampal gyrus Pars triangularis Superior temporal gyrus Precentral gyrus Transverse temporal cortex Rostral middle frontal gyrus Superior frontal gyrus Banks of the superior temporal sulcus
37 Figure 2 3 White matter regions of interest segmented from T1 images and registered to diffusion image space, displayed against axial FA slices. Images are labeled with slice numbers ( Z ) relative to the most inferior acquired slice. This image was previously published by Gongvatana et al. 115
38 CHAPTER 3 RESULTS Participant Characteristics HIV participants had more years of education ( F [1, 164] = 7.61, p = .006) and lower rates of active HCV ( F [1, 164] = 14.1, p < .001) than the HIV+ group. Although there were more Caucasians without HIV than with, a Pearson's x 2 analysis showed no significant group differences in racial and ethnic composition overall ( x 2 [4, N = 166] = 8.4, p > .05). HIV+ participants had a mean C ES D depression score of 21.1, which was higher than the mean score of 13.6 for the seronegative participants ( F [1, 160] = 13.3, p < .001). HIV+ participants had significantly higher rates of lifetime cocaine dependence ( x 2 [1, N = 166] = 15.6, p < .001). White matter hyperintensity (WMH) data were unavailable for 14 subjects: two due to missing data, one due to poor prescription at MRI acquisition, and 11 due to large movement artifacts and poor image quality. There were no significant differences between subjects who did and did not have WMH data in terms of demographic variables or HIV associated clinical characteristics. Those that did not have WMH data had significantly higher depression scores on the CES D ( F [1, 160] = 8.5, p = .004) and lower speed composite t scores ( F [1, 164] = 6.6, p = .011). Aim 1: Effects of age and HIV on White Matter WMH: Age, HIV status, and the age by HIV interaction explained 7.4% of the variability in white matter hyperintensity (WMH) volume (R 2 = .074, F [3, 148] = 3.9 4, p = .01). T here was a significant main effect of age ( = .184, p = .022), such that older age was associated with greater WMH volume. The main effect of HIV was not significant ( = .027, p = .738) However, the interaction of age by HIV was significan t ( = .212, p =
39 .008). To deconstruct the interaction term, follow up regression analyses were conducted for HIV+ and HIV participants separately with age as the independent variable and WMH as the outcome variable. For the HIV group, age was not signif icantly associated with WMH volume ( = .076, p = .573 ), while for the HIV+ group, older age was significantly associated with greater WMH volume ( = .286, p = .005). Figure 3 1 depicts WMH volume as a function of age for HIV+ and HIV groups. WMH volume is presented as a percentage of intracranial volume (ICV), and best fit lines are displayed with 95% confidence bands. To further evaluate the effect of age and HIV on WMH volume, groups were dichotomized by age into younger (below age 55) and older (55 and over) subjects. In younger participants, the HIV subjects showed greater WMH volumes than HIV+ subjects ( F [1, 119] = 5.716, p = .018), whereas in the old er participants, those with HIV had greater WMH volumes ( F [1, 29] = 4.137, p = .05 ). Given that HIV+ and HIV participants differed significantly on a number of demographic and clinical variables, namely education, current Hepatitis C virus (HCV) infecti on, depression (CES D) and lifetime history of cocaine dependence, correlational analyses were conducted to examine the relationship between these variables and WMH. HCV was the only variable that correlated significantly with WMH ( r = .263, p = .001). The refore, a hierarchical regression was run to see whether accounting for current HCV status altered the effects of age, HIV, and the age by HIV interaction on WMH. In the first model, HCV explained 26.3% of the variance in WMH (R 2 = .263, F [1, 150] = 11.16 0, p = .001). In the second model, age, HIV, and the age by HIV interaction were added as predictors. The second model explained 34.6% of the variance in WMH
40 (R 2 = .346, F [4, 147] = 5.009, p = .001) and was a significant improvement from the first model ( p = .041). The main effect of age became non significant ( = .150, p = .058), the main effect of HIV remained non significant, and the age by HIV interaction remained significant ( = .171, p = .032). To explain the main effect further, linear regression analyses were performed for HIV and HIV+ groups separately to examine the independent effect of age on WMH after accounting for the effects of HCV. Again, age did not significantly relate to WMH in the HIV group, whereas age uniquely explained significan t variability in WMH for the HIV+ group, even after controlling for HCV ( = .219, p = .031). Frontal FA: Regression models using age, HIV status, and the age by HIV interaction as predictors and fractional anisotropy (FA) as outcome measures were signifi cant at p < .05 for all frontal regions of interest (ROIs). Using the Bonferroni adjusted alpha of .005, the regression models remained significant for the caudal middle frontal region (R 2 = .097, F [3, 162] = 5.834, p = .001), lateral orbitofrontal region (R 2 = .096, F [3, 162] = 5.717, p = .001), and the superior frontal region (R 2 = .125, F [3, 162] = 7.749, p < .001). The model was marginally significant for the rostral middle frontal region (R 2 = .075, F [3, 162] = 4.360, p = .006). Table 3 1 displays the regression coefficients for each of these regions. Age was a significant predictor in each of these models, with older age significantly relating to lower FA scores. Positive HIV status was significantly associated with lower FA in the caudal middle fr ontal and superior frontal regions, and the association was marginally significant in the rostral middle frontal region. The age by HIV interaction term was marginally significant in the rostral middle frontal region. Follow up univariate regression models examining the relationship
41 between age and rostral middle frontal FA for HIV+ and HIV groups separately revealed a significant effect of age in the HIV+ group only ( = .340, p < .001). The four demographic and clinical variables that differed significa ntly between HIV groups were correlated with frontal regions to examine potential confounding effects of these variables on the relationships between age, HIV status, and FA. 40 comparisons were made, so alpha was adjusted to p = .001. There were no signif icant correlations at this level. The correlation between HCV and caudal middle frontal FA was close to significance ( r = .220, p = .004), so a hierarchical regression was conducted to examine whether accounting for HCV effects changed the way age, HIV, and their interaction were related to FA in this region. The first regression model with caudal middle frontal FA as the dependent variable included only HCV as a predictor, and the second model included HCV, age, HIV, and the age by HIV interaction as predictors. The second model significantly improved upon the first model ( p = .009), explained 33.8% of the variability in FA (R 2 = .3 38, F [4, 161] = 5.180, p = .001), and the relationships between age, HIV, and FA showed a similar pattern as before. Older age was associated with lower FA ( = .206, p = .007) as was positive HIV status ( = .010, p = .033), and the age by HIV interact ion was not significant. HCV was no longer a significant predictor of caudal middle frontal FA once age, HIV, and the age by HIV interaction were added to the model. Temporal FA: After adjusting alpha for multiple comparisons, regression equations were no t significant for any temporal regions. The regression equation for FA in the middle temporal region was significant at p = .05.
42 Parietal FA: Regression equations were significant for FA in the inferior parietal region, superior parietal region, and supra marginal parietal region at p = .05, but after alpha adjustments, only the inferior parietal regression equation remained significant (R 2 = .088, F [3, 162] = 5.203, p = .002). There was a significant main effect of age such that older age was associated w ith lower inferior parietal FA ( = .280, p < .001), while the HIV main effect and the age by HIV interaction were not significant. The four demographic and clinical variables that differed significantly between HIV groups were correlated with parietal r egions to examine potential confounding effects of these variables on the relationships between age, HIV status, and FA. 20 comparisons were made, so alpha was adjusted to p = .0025. There were no significant or near significant correlations at this level. Occipital FA: Regression equations did not significantly explain FA in any of the occipital white matter areas. Aim 2: Relationship Between WMH and FA Correlational analyses for the group as a whole revealed significant correlations between WMH and FA i n frontal, parietal, and occipital regions at the p = .05 level, but after Bonferroni adjustments set alpha to p = .0125, only the WMH relationships with frontal FA and parietal FA remained significant. Greater WMH was associated with lower FA in the front al lobe s ( r = .245, p = .002) and lower FA in the parietal lobe s ( r = .246, p = .002). When examining the relationship between WMH and FA in only the participants with HIV, greater WMH volume was again significantly correlated with lower FA in the front al ( r = .290, p = .004) and parietal ( r = .275, p = .007) lobes. Correlations were stronger in the HIV+ group than in the combined group
43 Aim 3: Effects of HIV Associated Clinical Factors on White Matter HIV associated clinical factors (HIV duration, de tectable HIV RNA, current CD4, cART use, current HCV, and history of AIDS) explained 20.1% of the variance in WMH (R 2 = .201, F [6, 85] = 3.569, p = .003). Current CD4 count, HCV status, and a lifetime history of AIDS uniquely explained significant variabi lity in WMH. Greater WMH volume was associated with higher current CD4 levels ( = .317, p = .005), positive current HCV status ( = .309, p = .004), and a positive history of AIDS ( = .276, p = .025). HIV associated clinical factors did not significantl y explain average FA for any lobe of the brain. Aim 4: Relationship Between White Matter and Cognitive Functioning Table 3 2 displays cognitive testing means and standard deviations for participants grouped by HIV status For the group as a whole, multivariate regressions with WMH were marginally significant for speeded motor tests ( 2 partial = .038, F [2, 147] = 2.893, p = .059), but statistically significant for processing speed tests only. WMH explained 7.2% of the variance in processing speed performance ( 2 partial = .072, F [3, 148] = 3.820, p = .011). Follow up univariate regression analyses revealed a significant relationship between greater WMH and lower scores on Symbol Search (R 2 = .066, F [1, 150] = 10.581, p = .001, B = 13.297), but no significant relationships between WMH and either Digit Symbol Coding or Trail Making Test Part A. For HIV+ participants only, multivariate regressions with WMH were significant for tests of processing speed ( 2 partial = .115, F [3, 91] = 3.925, p = .01 1) and speeded motor functioning ( 2 partial = .068, F [2, 91] = 3.323, p = .040). WMH explained 11.5% of the variance in performance on processing speed tests, and follow up univariate regressions revealed greater WMH to be significantly associated with lo wer
44 performance on Symbol Search (R 2 = .094, F [1, 93] = 9.685, p = .002, B = 13.536), but not on Digit Symbol or Trails A. WMH explained 6.8% of the variance in speeded motor performance, but follow up univariate regressions did not produce significant a ssociations between WMH and Grooved Pegboard performance with either the dominant or non dominant hand. For the group as a whole, regression analyses with global FA marginally significant for memory (R 2 = .055, F [4, 161] = 2.341, p = .057), but not signi ficant for any other cognitive domain. For HIV+ participants only, regression analyses with FA revealed that mean FA for all four lobes explained significant variability in learning performance (R 2 = .122, F [4, 98] = 3.392, p = .012) and memory performan ce (R 2 = .131, F [4, 98] = 3.697, p = .008). There were no significant unique contributions from average FA of any of the four lobes to learning performance, but occipital FA explained significant unique variability in memory (B = 220.30, p = .040). Given the finding that there may be a threshold effect such that WMH is only related to cognitive functioning at high levels of WMH burden 63, 64 WMH was split into deciles and WMH analyses were repeated with subjects in the top decile. There were 15 individ uals (12 HIV+, 3 HIV ) in the top decile. For those subjects, multivariate regression analyses were only significant for attention/working memory/executive functioning tasks ( 2 partial = .749, F [3, 8] = 7.949, p = .009). Follow up univariate regression an alyses did not produce any significant relationships between WMH and individual test performance.
45 Figure 3 1. White matter hyperintensities (WMH) as a function of age for HIV and HIV+ participants. WMH are presented as a percentage of total intracranial volume (ICV) Linear fit lines are displayed with 95% confidence bands. Table 3 1. Regression coefficients for FA in frontal white matter regions Age HIV Age by HIV # p p p Caudal middle frontal .224 .003* .208 .006* .105 .165 Lateral orbitofrontal .279 <.001* .104 .166 .119 .115 Superior frontal .269 <.001* .210 .005* .138 .065 Rostral middle frontal .197 .010* .147 .054 .149 .052 p < .05
46 Table 3 2. Cognitive testing means and standard deviations for HIV+ and HIV groups Cognitive Tests and Composites HIV+ ( n = 103) HIV ( n = 63) p Processing Speed 47.0 (6.7) 49.7 (7.1) .018 Digit Symbol Coding 44.8 (8.0) 48.8 (9.0) 003* Symbol Search 47.2 (8.6) 50.7 (8.5) .012 Trail Making Test Part A 49.1 (9.7) 49.5 (10.1) .799 Att ention/Working Memory/Exec utive 47.9 (6.2) 48.8 (6.6) .407 Stroop interference 47.4 (6.6) 49.5 (7.5) .107 Letter Number Sequencing 47.0 (8.8) 49.2 (8.4) .114 Trail Making Test Part B 48.5 (9.2) 48.1 (9.7) .840 Learning 39.3 (10.2) 44.0 (11.1) .006 HVLT R total recall 38.1 (10.7) 41.9 (11.7) .032* BVMT R total recall 40.5 (14.4) 46.1 (13.2) 013* Memory 39.6 (13.3) 45.1 (11.4) .0 07* HVLT R delayed recall 36.3 (13.8) 42.2 (12.7) .007* BVMT R delayed recall 42.6 (17.9) 48.0 (13.6) .0 43* Verbal 49.6 (7.4) 50.0 (8.3) 714 FAS letter fluency 47.9 (9.7) 49.3 (8.6) .3 5 0 Category fluency 51.2 (8.6) 50.8 (11.1) 751 Motor 45.5 (10.9) 45.1 (10.6) 793 Grooved Pegboard dominant hand 45.2 (12.2) 44.2 (11.9) 621 Grooved Pegboard non dominant 45.9 (11.5) 45.9 (10.8) 993 All means are demographically corrected t scores Scores are presented as mean (standard deviation) p < .05
47 CHAPTER 4 DISCUSSION Results of the current study in a cohort of medically stable HIV positive (HIV+) subjects without dementia and seronegative (HIV ) controls revealed several findings. First, age and HIV interact to produce greater white matter hyperintensity (WMH) volumes in older people with HIV. Older age and positive HIV status were independently associated with lower frontal FA, and age with lower parietal FA Second, greater overall WMH volume was associated with lower FA in frontal and parietal lobes, and this relatio nship was stronger when examining HIV+ subjects only rather than the group as a whole. Third, greater disease severity and Hepatitis C virus (HCV) comorbidity were significantly related to greater WMH volume in HIV+ subjects. Fourth, greater WMH load was a ssociated with poorer performance on speeded motor and cognitive functioning measures, with a stronge r association in HIV+ subjects. In addition, l ower global FA was associated with poorer learning and memory performance in the HIV+ group only. Finally, an alyses of WMH effects on cognitive performance for individuals in the top decile of WMH volume showed significant negative effects of WMH on executive functioning tasks. Aim 1: Effects of Age and HIV on White Matter This study found that there is a strong er association between age and WMH volume in HIV+ subjects versus seronegative controls suggesting a synergistic effect of age and HIV on white matter damage While greater age was linked to greater WMH load in the group as a whole, the association was no t significant in HIV subjects alone, whereas in HIV+ participants, older age was significantly associated with greater WMH volume.
48 The main effect of HIV was not significant in the cohort as a whole, consistent with prior research showing no significant differences between asymptomatic seropositive individuals and seronegative controls in WMH load 81, 87, 88 Age was significantly related to WMH volume in HIV+ participants, however. This finding supports previous research showing that greater age is related to greater severity of WMH load 86 and lower white matter volumes 133 in this population. Seropositive subjects showed a greater range of W M H vo lumes than HIV controls, especially at older ages, as illustrated by Figure 3 1. In younger participants, HIV actually had more WMH than HIV+ participants, but HIV+ subjects displayed greater increases in WMH volume with age than the control subjects, sh owing age effects before the subjects without HIV. This significant interaction effect was also observed for FA measurements of white matter integrity in the rostral middle frontal region, where older age did not predict FA values in HIV participants, but was associated with lower FA in HIV+ subjects. The concept that HIV and age interact as risk factors is controversial. Age and HIV have been shown have interactive neurological and cognitive effects 134 136 though some research suggests that the effects are additive rather than synergistic 133, 137, 138 Age and HIV both indi vidually impact neurological structure and function, and there are reasons to believe that they would work in combination to produce greater changes in older infected individuals. Both HIV and aging are associated with processing speed and executive functi oning declines. They have similar patterns of atrophy and white matter changes that affect particularly frontal striatal networks. They also are both associated with signs of inflammation, impaired blood brain barrier functioning, and decline in cellular disposal of toxins 24, 139 HI V may also increase the brain's
49 susceptibility to neurodegeneration 140, 141 HIV shares risk factors with many common neurodegenerative diseases. Both HIV and Alzheimer's disease (AD) show increased amyloid and tau deposition 142, 143 there are increased ra tes of cardiovascular disease and metabolic disturbances in both HIV and vascular d ementia 144 and volumetric losses in the substantia nigra occ ur in both HIV and Parkinson's d isease 145 Much research has been conducted on cerebral amyloid and tau in HIV, since they may be common pathogenic mechanism shared with AD, and there is debate as to whether these changes in HIV+ individuals represent AD. It should be noted that HIV may facilitate the aggregation of amyloid and the incidence of cerebral tau independent of any signs of AD, and the cha racteristics of amyloid have been shown to be different in HIV and AD 146, 147 Evidence for the interactive effects of HIV and age come from the finding that neurocognitive impairments among older people with HIV are more prevalent than would be expected from HIV or age related risks alone 148 The significant age by HIV interactions in predicting WMH volumes and frontal FA in the current study provide a dditional evidence, as do prior studies showing significant age by HIV interactions in association with greater functional activation abnormalities in left frontal brain regions 136 and greater declines in memory 135 psychomotor speed 134 and daily functioning 149 in older subjects with HIV Other s tudies using quantitative measures of WMH show that greater age is associated with greater hyperintensity volume, even in healthy younger cohorts 65, 150 This was not the case in the current cohort of healthy controls. This may be attributed to small sample size, as there were only 63 subjects without HIV studied. Also, this group
50 had a limited range of WMH volumes, as displayed by the narrow 95% confidence interval bands in Figure 3 1, and were not showing high levels of white matter damage overall The low level of WMH is not surprising given that the mean age of HIV participants was 45 and the maximum age was 74. Studies show that WMH are minimal or absent in the majority of brains until age 60, and not ubiquitous until after age 80 33 35 Older age was significantly associated with lower FA in several frontal regions of interest (ROIs) and one parietal region, which corroborates previous evidence that normal aging shows preferential F A declines in anterior brain regions 49 51 Positive HIV status was signifi cantly associated with lower FA only in frontal regions, including the caudal middle frontal, superior frontal, and rostral middle frontal ROIs. This finding is consistent with research showing early changes in frontal FA in HIV+ patients. Much of the research in FA examines particular pathways or fiber bundles. The method used in the current study looked at larger ROIs separated by cortical landmarks, limiting the number of direct comparisons that can be made with previou s findings. However, several studies have aggregated FA measures by lobe and have found similar results. Pomara and colleagues, for instance, looked at FA for frontal, parietal, and temporal lobes, and found only significant differences in frontal FA betwe en HIV+ and seronegative participants 93 Importantly, participants were similar to those in the current study, with stable medical status and a mean age of 40. Xuan et al. also used lobe wide aggregate FA measures and found significant differences in both frontal and parietal FA 90 How ever, the patients in the Xuan study had AIDS, and AIDS patients have been shown to have more widespread differences in FA compared to asymptomatic people with HIV 46, 96 Since FA differences were seen in the frontal lobe in the absence of
51 signifi cant parietal, temporal, or occipital differences, it can be concluded that frontal white matter is the first to be affected in HIV+ individuals similar to the pattern of FA changes in normal aging Aim 2: Relationship Between WMH and FA Greater global W MH volume was found to be significantly associated with lower FA measures in frontal and parietal lobes, with stronger associations in the HIV+ participants than in the group as a whole. In line with the hypothesis, higher WMH values were associated with l ower FA values, but WMH volume was expected to relate to FA in all brain areas, and findings show associations only with anterior brain regions. This could be due to the fact that individuals in this study were non demented, and FA changes tend to occur in anterior brain regions before posterior regions in normal aging 49 51 Studies that include people who span a wide range of cognitive abilities show widespread associations between FA and WMH 151 but few studies have examined the relationship between WMH and FA in non demented populations, and none have looked at the association between average FA per brain lo be and global WMH. Theoretically, subcortical WMH and frontal FA should be related. Most long association fibers in the brain project to the frontal lobe, and frontal subcortical white matter tracts comprise one of the largest networks in the brain. This network receives its blood supply from relatively long and narrow branches of the anterior and middle cerebral arteries, leaving it vulnerable to ischemic injury 71 Therefore, when WMH show evidence of subcortical white matter injury, anterior brain regions are be expected to show downstream effects. Despite the similarities and close relationship be tween these structural white matter measures, WMH and FA provide different information about white matter, and
52 the current divergent results are not surprising. FA can measure the integrity of healthy, undisturbed white matter, whereas WMH necessarily meas ures outright damage. FA can decrease with subtle white matter changes that occur in the absence of WMH 152 Also, FA decreases do not necessarily precede WMH development, as WMH and FA differentially predict future conversion of a normal white matter voxel to a hyperintensity 55 The lack of association between WMH volume and FA in temporal and occipital regions supports the fact that, while related, WMH and FA are measurements of two different white matter properties. As predicted, WMH FA associations were stronger in HIV+ subjects versus th e group as a whole. This is likely due to the fact that HIV+ participants showed a wider range of white matter damage than seronegative controls, allowing for stronger associations between the two structural measures of white matter. Aim 3: Effects of HI V Associated Clinical Factors on White Matter Since age showed stronger relationships with measures of white matter in HIV+ participants, it was germane to determine which aspects of HIV related most strongly to WMH and FA. Consistent with prior research 79, 153 measures of greater disease severity were significantly related to greater WMH volume. Not only was greater c urrent disease severity (CD4 levels) related to greater WMH load, but greater history of immune compromise (history of AIDS) was as well. Lower CD4 cell count, a marker of suppressed immune function, is related to greater viral replication in the body and a higher amount of the virus in the brain. Higher neural viral loads have previously been associated with greater white matter abnormalities 84, 85 Thus, regardless of whether the immune suppression is current or historical, it is associated with lasting white matter damage.
53 HCV coinfection is common in HIV+ populations, and the current study corroborates prior research showing an association between HCV and greater white matter abnormal ities in seropositive individuals 83 HCV has also been shown to relate to poorer neuropsychological functioning in people with HIV 154, 155 Give n that participants in the current sample did not have advanced liver disease, HCV effects on liver function are not sufficient to explain the association between the virus and WMH. However, HCV has been found to invade the central nervous system 156 and is associated with gre ater levels of proinflammatory chemokines 157 Like HIV, HCV infects monocytes and macrophages 158 potenti ally creating an additive or synergistic effect with HIV on white matter 159 Unexpectedly, HIV associated clinical factors did not predict FA measures. Previous research has shown associations between HCV coinfection and lower FA throughout the brain, as well as lower CD4 associations with lower parietal FA and cART use with higher temporal FA 115 Studies examining specific fiber tracts show greater current and historical disease severity to be associated with l ower integrity in the corpus callosum 46, 160 Discrepancies could be explained by the methodology used in the current study, which merged FA values for entire lobes of the brain, potentially masking effects found in particular white matter structures. Aim 4: Relationship Between White Matter and Cognitive Functioning As hypothesized, WMH correlated most strongly with measures of speeded performance. WMH related only to speeded cognitive processes in the group as a whole, but to both cognitive and motor spe ed in the HIV+ subjects. Since HIV is typically associated with poorer psychomotor speed relative to controls, it is likely that greater variability in performance in the HIV+ group led to stronger relationships between white
54 matter measurements and psycho motor functioning. Follow up analyses revealed that the association between WMH and speed measures was driven by WMH effects on Symbol Search performance rather than performance on Digit Symbol or Trails A. Lack of association between WMH and Digit Symbol may be explained by the fact that this test relies on memory skills as well as on speeded processing 161 and memory is not as closely linked with WMH. A construct validity test of Trails A suggests that it requires mainly visuoperceptual abilities 162 Symbol Search may rely more primarily on visual searching skills, which is a more basic measure of processing speed and thus might explain the unique association with WMH volume. It is somewhat surprising that in participants with the greatest degree of WMH load, WMH volume was associated with executive functioning and not with processing or motor speed. This makes sense when considering tha t, at low levels of white matter damage, WMH volume was associated with processing speed, but not with executive functioning. This suggests that speeded processing is more vulnerable to white matter changes, so in a group with higher levels of white matter burden, speeded processing is likely affected in all or most members of that group, whereas only some will show executive functioning deficits. Limited processing speed range and greater executive functioning range in this group with greater white matter damage likely explains why greater WMH volume was associated with lower executive functioning scores but not lower motor or cognitive speed scores. Incongruent with expectations, FA was associated with learning and memory performance and showed no signifi cant associations with processing speed or executive functioning tasks. This was only the case when examining HIV+ individuals,
55 as associations were not significant for the group as a whole. Findings are consistent with research showing that relationships between FA and cognitive functioning are stronger in HIV+ versus seronegative controls 89 though previously reported FA associations were with executive functioning and psychomotor processing speed. Prior studies have also shown that episodic memory is relate d to FA in various regions of the brain 61 though occipital FA is not typically implicated. Yet results of the present study show FA of the occipital lobe to be the most strongly associated with learning and memory performance. This may be explained by the fa ct that learning and memory composite scores were comprised of performance on two tests, one of which was auditory list learning and one of which was visual picture learning. The fronto occipital fasciculus fiber bundles connect the frontal lobe to specifi c occipital areas that engage in tasks requiring information retrieval from visual memory 163 Thus, greater FA in those occipital areas could be responsible for better visual learning and memory performance. Further investigation of specific fiber bundles would be necessary to confirm this premise. Consis tent with prior research 38 the current study showed that WMH and FA were not associated with the same cognitive processes. In this case, WMH was most closely associated with processing speed, followed by m otor speed and executive functioning, whereas FA was most closely associated with learning and memory. This provides further evidence that WMH and FA measure two different aspects of white matter. Importantly, this was examined in a population that is know n for having the earliest cognitive dysfunction in areas of processing speed and executive functioning rather than memory, and these were the functions associated with WMH. This is likely
56 because WMH m easures white matter pathology, whereas FA captures bot h normal variability in white matter structure as well as changes related to age and disease. Thus, WMH may be a better marker of cognitive dysfunction in this population. Limitations and Future Directions There are several limitations to the current study that would benefit from attention in future research. First, FA measurements were not restricted to specific fiber tracts, but rather represented aggregated white matter integrity of structural ROIs. This may provide a less sensitive measure of white matt er integrity, and future studies are needed to determine the relationship between WMH and FA of particular fiber tracts in HIV+ populations. Also, scalar DTI metrics were used, while new methods (e.g. Track Based Spatial Statistics) offer the possibility o f more refinement in the FA metrics relative to effective connectivity. Second, WMH was not specific to particular ROIs, producing a gross measure of overall damage. Evidence indicates WMH location is important, with periventricular WMH relating to cogniti ve functioning differently that subcortical WMH or punctate foci, so it would be useful to examine how regional WMH relate to FA and cognition. Third analyses were cross sectional, making it impossible to infer causality. Future research is needed to dete rmine how baseline WMH volume and FA can individually and in combination predict future cognitive decline in healthy aging and in people aging with HIV and other diseases. Finally, WMH are common in people with cardiovascular disease and diabetes, both of which are more prevalent in people with HIV, and neither of which were investigated in the present study. Upcoming studies should examine how vascular risk factors contribute to WMH and cognitive functioning in HIV.
57 Conclusions HIV and age act in combina tion to produce greater white matter changes in older people with HIV, and those white matter changes are associated with lower performance in a variety of cognitive functions. These findings are relevant for individuals aging with HIV, as cognitive status is predictive of everyday functional ability as well as morbidity and mortality. Greater current and historical disease severity were important risk factors for white matter damage, emphasizing the need for consistent treatment to bolster immune function. HCV coinfection was also a significant risk factor for white matter damage, highlighting the significance of comorbid conditions in HIV associated neurological and neurocognitive dysfunction. FA and WMH are related, but are associated with different cogn itive functions. This is significant in the current era when many researchers are abandoning WMH measurements for markers of subtler white matter changes. While FA will continue to be an important biomarker of white matter damage in HIV, WMH are also relev ant markers because they provide different information. This was true in a non demented population, showing that both FA and WMH are important early indicators of brain changes in healthy and diseased aging. Given that the current study population was rela tively healthy and functionally intact, findings reflect early changes in white matter and cognition. This gives an opportunity to intervene before the development of significant functional impairments. As the population with HIV ages, it will continue to be important to investigate the effects of the disease, comorbid conditions, and age on brain structure and function.
58 LIST OF REFERENCES 1. UNAIDS. Global report: UNAIDS report on the global AIDS epidemic 2013: Joint United Nations Programme on HIV/AIDS (UNAIDS), 2013. 2. CDC. HIV Suveillance Supplemental Report2012 December 2012. 3. Hutchinson AB, Farnham PG, Dean HD, et al. The economic burden of HIV in the United States in the era of highly active antiretroviral therapy: evidence of c ontinuing racial and ethnic differences. Journal of acquired immune deficiency syndromes 2006;43:451 457. 4. Gongvatana A, Harezlak J, Buchthal S, et al. Progressive cerebral injury in the setting of chronic HIV infection and antiretroviral therapy. Journal of neurovirology 2013;19:209 218. 5. Rackstraw S. HIV related neurocognitive impairment -a review. Psychology, health & medicine 2011;16:548 563. 6. Davis LE, Hjelle BL, Miller VE, et al. Early viral brain invasion in iatrogenic human immunodeficiency virus i nfection. Neurology 1992;42:1736 1739. 7. An SF, Groves M, Gray F, Scaravilli F. Early entry and widespread cellular involvement of HIV 1 DNA in brains of HIV 1 positive asymptomatic individuals. Journal of neuropathology and experimental neurology 1999;58:1 156 1162. 8. Rock RB, Gekker G, Hu S, et al. Role of microglia in central nervous system infections. Clinical microbiology reviews 2004;17:942 964, table of contents. 9. Minagar A, Shapshak P, Fujimura R, Ownby R, Heyes M, Eisdorfer C. The role of macrophage/ microglia and astrocytes in the pathogenesis of three neurologic disorders: HIV associated dementia, Alzheimer disease, and multiple sclerosis. J Neurol Sci 2002;202:13 23. 10. Navia BA, Jordan BD, Price RW. The AIDS dementia complex: I. Clinical features. An nals of neurology 1986;19:517 524. 11. Portegies P, de Gans J, Lange JM, et al. Declining incidence of AIDS dementia complex after introduction of zidovudine treatment. Bmj 1989;299:819 821. 12. Casadei GP, Barberis M, Oreste PL, Caggese L, Schlacht I. [Neuropathology of the acquired immunodeficiency syndrome]. Pathologica 1989;81:481 498.
59 13. Tozzi V, Balestra P, Lorenzini P, et al. Prevalence and risk factors for human immunodeficiency virus associated neurocognitive impairment, 1996 to 2002: results fro m an urban observational cohort. Journal of neurovirology 2005;11:265 273. 14. Ances BM, Ellis RJ. Dementia and neurocognitive disorders due to HIV 1 infection. Seminars in neurology 2007;27:86 92. 15. McArthur JC. HIV dementia: an evolving disease. J Neuroimmun ol 2004;157:3 10. 16. Tate D, Paul RH, Flanigan TP, et al. The impact of apathy and depression on quality of life in patients infected with HIV. AIDS Patient Care STDS 2003;17:115 120. 17. Osowiecki DM, Cohen RA, Morrow KM, et al. Neurocognitive and psychologica l contributions to quality of life in HIV 1 infected women. Aids 2000;14:1327 1332. 18. Heaton RK, Franklin DR, Ellis RJ, et al. HIV associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nat ure, and predictors. J Neurovirol 2011;17:3 16. 19. Sacktor N. The epidemiology of human immunodeficiency virus associated neurological disease in the era of highly active antiretroviral therapy. J Neurovirol 2002;8 Suppl 2:115 121. 20. Tozzi V, Balestra P, Bellagamba R, et al. Persistence of neuropsychologic deficits despite long term highly active antiretroviral therapy in patients with HIV related neurocognitive impairment: prevalence and risk factors. J Acquir Immune Defic Syndr 2007;45:174 182. 21. Becker J T, Lopez OL, Dew MA, Aizenstein HJ. Prevalence of cognitive disorders differs as a function of age in HIV virus infection. Aids 2004;18 Suppl 1:S11 18. 22. Valcour V, Shikuma C, Shiramizu B, et al. Higher frequency of dementia in older HIV 1 individuals: the Hawaii Aging with HIV 1 Cohort. Neurology 2004;63:822 827. 23. High KP, Brennan Ing M, Clifford DB, et al. HIV and aging: state of knowledge and areas of critical need for research. A report to the NIH Office of AIDS Research by the HIV and Aging Working Grou p. J Acquir Immune Defic Syndr 2012;60 Suppl 1:S1 18.
60 24. Brew BJ, Crowe SM, Landay A, Cysique LA, Guillemin G. Neurodegeneration and ageing in the HAART era. J Neuroimmune Pharmacol 2009;4:163 174. 25. Bendlin BB, Fitzgerald ME, Ries ML, et al. White matter in aging and cognition: a cross sectional study of microstructure in adults aged eighteen to eighty three. Developmental neuropsychology 2010;35:257 277. 26. Grueter BE, Schulz UG. Age related cerebral white matter disease (leukoaraiosis): a review. Postgraduate medical journal 2012;88:79 87. 27. Pantoni L, Garcia JH. Pathogenesis of leukoaraiosis: a review. Stroke; a journal of cerebral circulation 1997;28:652 659. 28. Fazekas F, Kleinert R, Offenbacher H, et al. Pathologic correlates of incidental MRI white matter si gnal hyperintensities. Neurology 1993;43:1683 1689. 29. Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger's report. A review. Stroke; a journal of cerebral circulation 1995;26:1293 1301. 30. Meyer JS, Kawamu ra J, Terayama Y. White matter lesions in the elderly. Journal of the neurological sciences 1992;110:1 7. 31. Ylikoski A, Erkinjuntti T, Raininko R, Sarna S, Sulkava R, Tilvis R. White matter hyperintensities on MRI in the neurologically nondiseased elderly. Analysis of cohorts of consecutive subjects aged 55 to 85 years living at home. Stroke; a journal of cerebral circulation 1995;26:1171 1177. 32. Longstreth WT, Jr., Manolio TA, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke; a journal of cerebral circulation 1996;27:1274 1282. 33. Garde E, Mortensen EL, Krabbe K, Rostrup E, Larsson HB. Relation between age related decline in intelligence and cerebr al white matter hyperintensities in healthy octogenarians: a longitudinal study. Lancet 2000;356:628 634. 34. Bradley W, Waluch V, Brant Zawadzki M, Vadley R, Wycoff R. Patchy periventricular white matter lesions in the elderly: a common observation during NM R imaging. Noninvasive Med Imaging 1984;1:35 41. 35. Hopkins RO, Beck CJ, Burnett DL, Weaver LK, Victoroff J, Bigler ED. Prevalence of white matter hyperintensities in a young healthy population. Journal of neuroimaging : official journal of the American Soci ety of Neuroimaging 2006;16:243 251.
61 36. Salat DH, Lee SY, van der Kouwe AJ, Greve DN, Fischl B, Rosas HD. Age associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage 2009;48:21 28. 37. Salat DH, Gre ve DN, Pacheco JL, et al. Regional white matter volume differences in nondemented aging and Alzheimer's disease. Neuroimage 2009;44:1247 1258. 38. Meier IB, Manly JJ, Provenzano FA, et al. White matter predictors of cognitive functioning in older adults. J In t Neuropsychol Soc 2012;18:414 427. 39. Jernigan TL, Archibald SL, Fennema Notestine C, et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiology of aging 2001;22:581 594. 40. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001;14:21 36. 41. Brickman AM, Zimmerman ME, Paul RH, et al. Regional white matter and neuropsychological functioning across the adult lifespan. Biolo gical psychiatry 2006;60:444 453. 42. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 2003;23:3295 3301. 43. Raz N, Lindenberger U, Rodrigue KM, et al. Regi onal brain changes in aging healthy adults: general trends, individual differences and modifiers. Cerebral cortex 2005;15:1676 1689. 44. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI) based white matter mapping in brain research: a review. Journal of mo lecular neuroscience : MN 2008;34:51 61. 45. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophysical journal 1994;66:259 267. 46. Filippi CG, Ulug AM, Ryan E, Ferrando SJ, van Gorp W. Diffusion tensor imaging of patients with HIV and normal appearing white matter on MR images of the brain. AJNR Am J Neuroradiol 2001;22:277 283. 47. Pfefferbaum A, Sullivan EV, Hedehus M, Lim KO, Adalsteinsson E, Moseley M. Age related decline in brain white matter anisotropy measured with spatially corrected echo planar diffusion tensor imaging. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 2000;44:259 268.
62 48. Abe O, Aoki S, Hayashi N, et al. Norma l aging in the central nervous system: quantitative MR diffusion tensor analysis. Neurobiology of aging 2002;23:433 441. 49. Ardekani S, Kumar A, Bartzokis G, Sinha U. Exploratory voxel based analysis of diffusion indices and hemispheric asymmetry in normal a ging. Magnetic resonance imaging 2007;25:154 167. 50. Grieve SM, Williams LM, Paul RH, Clark CR, Gordon E. Cognitive aging, executive function, and fractional anisotropy: a diffusion tensor MR imaging study. AJNR Am J Neuroradiol 2007;28:226 235. 51. Head D, Buc kner RL, Shimony JS, et al. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cerebral cortex 2004;14:410 423. 52. Jones DK, Lythgoe D, Horsfield MA, Simmons A, Williams SC, Markus HS. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke; a journal of cerebral circulation 1999;30:393 397. 53. Maillard P, Fletcher E, Harvey D, et al. White matter hyperintensity penumbra. Stroke; a journal of cerebral circulation 2011;42:1917 1922. 54. Vernooij MW, Ikram MA, Vrooman HA, et al. White matter microstructural integrity and cognitive function in a general elderly population. Archives of general psychiatry 2009;66:545 553. 55. Maillard P, Carmichael O, Harvey D, et al. FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities. AJNR Am J Neuroradiol 2013;34:5 4 61. 56. de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matte r lesions and cognitive function: the Rotterdam Scan Study. Annals of neurology 2000;47:145 151. 57. Ylikoski R, Ylikoski A, Erkinjuntti T, Sulkava R, Raininko R, Tilvis R. White matter changes in healthy elderly persons correlate with attention and speed of mental processing. Arch Neurol 1993;50:818 824. 58. DeCarli C, Murphy DG, Tranh M, et al. The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology 1995;45:20 77 2084.
63 59. Austrom MG, Thompson RF, Jr., Hendrie HC, et al. Foci of increased T2 signal intensity in MR images of healthy elderly subjects. A follow up study. Journal of the American Geriatrics Society 1990;38:1133 1138. 60. Schmidt R, Fazekas F, Offenbacher H et al. Neuropsychologic correlates of MRI white matter hyperintensities: a study of 150 normal volunteers. Neurology 1993;43:2490 2494. 61. Lockhart SN, Mayda AB, Roach AE, et al. Episodic memory function is associated with multiple measures of white matter integrity in cognitive aging. Frontiers in human neuroscience 2012;6:56. 62. Tate DF, Jefferson AL, Brickman AM, et al. Regional White Matter Signal Abnormalities and Cognitive Correlates Among Geriatric Patients with Treated Cardiovascular Disease. Brain im aging and behavior 2008;2:200 206. 63. Boone KB, Miller BL, Lesser IM, et al. Neuropsychological correlates of white matter lesions in healthy elderly subjects. A threshold effect. Arch Neurol 1992;49:549 554. 64. Price CC, Mitchell SM, Brumback B, et al. MRI le ukoaraiosis thresholds and the phenotypic expression of dementia. Neurology 2012;79:734 740. 65. Vannorsdall TD, Waldstein SR, Kraut M, Pearlson GD, Schretlen DJ. White matter abnormalities and cognition in a community sample. Archives of clinical neuropsycho logy : the official journal of the National Academy of Neuropsychologists 2009;24:209 217. 66. Gunning Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology 2000;14:224 232. 67. Kramer JH, Reed BR, Mungas D, Weiner MW, Chui HC. Executive dysfunction in subcortical ischaemic vascular disease. J Neurol Neurosurg Psychiatry 2002;72:217 220. 68. Tullberg M, Fletcher E, DeCarli C, et al. White matter lesions impair frontal lobe function regardless of their location. Neurology 2004;63:246 253. 69. Charlton RA, Barrick TR, McIntyre DJ, et al. White matter damage on diffusion tensor imaging correlates with age related cognitive decline. Neurology 2006;66:217 222. 70. Salat DH, Tuch DS, Greve DN, et al. Age r elated alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiology of aging 2005;26:1215 1227.
64 71. Campbell JJ, 3rd, Coffey CE. Neuropsychiatric significance of subcortical hyperintensity. J Neuropsychiatry Clin Neurosci 2001;13:261 288. 72. Malloy P, Correia S, Stebbins G, Laidlaw DH. Neuroimaging of white matter in aging and dementia. Clin Neuropsychol 2007;21:73 109. 73. Navia BA, Cho ES, Petito CK, Price RW. The AIDS dementia complex: II. Neuropathology. Annals of neurology 1986;19:525 535. 74. Grant I, Atkinson JH, Hesselink JR, et al. Evidence for early central nervous system involvement in the acquired immunodeficiency syndrome (AIDS) and other human immunodeficiency virus (HIV) infections. Studies with neuropsychologic testi ng and magnetic resonance imaging. Ann Intern Med 1987;107:828 836. 75. Olsen WL, Longo FM, Mills CM, Norman D. White matter disease in AIDS: findings at MR imaging. Radiology 1988;169:445 448. 76. Paul R, Cohen R, Navia B, Tashima K. Relationships between cogni tion and structural neuroimaging findings in adults with human immunodeficiency virus type 1. Neurosci Biobehav Rev 2002;26:353 359. 77. Everall I, Vaida F, Khanlou N, et al. Cliniconeuropathologic correlates of human immunodeficiency virus in the era of anti retroviral therapy. J Neurovirol 2009;15:360 370. 78. Steinbrink F, Evers S, Buerke B, et al. Cognitive impairment in HIV infection is associated with MRI and CSF pattern of neurodegeneration. European journal of neurology : the official journal of the Europe an Federation of Neurological Societies 2013;20:420 428. 79. Hanning U, Husstedt IW, Niederstadt TU, Evers S, Heindel W, Kloska SP. Cerebral signal intensity abnormalities on T2 weighted MR images in HIV patients with highly active antiretroviral therapy: rel ationship with clinical parameters and interval changes. Academic radiology 2011;18:1144 1150. 80. Harrison MJ, Newman SP, Hall Craggs MA, et al. Evidence of CNS impairment in HIV infection: clinical, neuropsychological, EEG, and MRI/MRS study. J Neurol Neuro surg Psychiatry 1998;65:301 307. 81. Bornstein RA, Chakeres D, Brogan M, et al. Magnetic resonance imaging of white matter lesions in HIV infection. The Journal of neuropsychiatry and clinical neurosciences 1992;4:174 178. 82. Dooneief GH, Bello JA, Todak GG, et al. Serial MRI in HIV Infection With and Without Neurologic Impairment. Journal of neuro AIDS 1996;1:49 57.
65 83. Jernigan TL, Archibald SL, Fennema Notestine C, et al. Clinical factors related to brain structure in HIV: the CHARTER study. J Neurovirol 2011;17: 248 257. 84. Heindel WC, Jernigan TL, Archibald SL, Achim CL, Masliah E, Wiley CA. The relationship of quantitative brain magnetic resonance imaging measures to neuropathologic indexes of human immunodeficiency virus infection. Arch Neurol 1994;51:1129 1135. 85. Archibald SL, Masliah E, Fennema Notestine C, et al. Correlation of in vivo neuroimaging abnormalities with postmortem human immunodeficiency virus encephalitis and dendritic loss. Arch Neurol 2004;61:369 376. 86. McMurtray A, Nakamoto B, Shikuma C, Valcour V. Cortical atrophy and white matter hyperintensities in HIV: the Hawaii Aging with HIV Cohort Study. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 2008;17:212 217. 87. McArthur JC, Kumar AJ, Johnson DW, et al. Incidental white matter hyperintensities on magnetic resonance imaging in HIV 1 infection. Multicenter AIDS Cohort Study. J Acquir Immune Defic Syndr 1990;3:252 259. 88. Harezlak J, Buchthal S, Taylor M, et al. Persistence of HIV associated cognitive i mpairment, inflammation, and neuronal injury in era of highly active antiretroviral treatment. AIDS 2011;25:625 633. 89. Tate DF, Conley J, Paul RH, et al. Quantitative diffusion tensor imaging tractography metrics are associated with cognitive performance am ong HIV infected patients. Brain Imaging Behav 2010;4:68 79. 90. Xuan A, Wang GB, Shi DP, Xu JL, Li YL. Initial study of magnetic resonance diffusion tensor imaging in brain white matter of early AIDS patients. Chinese medical journal 2013;126:2720 2724. 91. Thu rnher MM, Castillo M, Stadler A, Rieger A, Schmid B, Sundgren PC. Diffusion tensor MR imaging of the brain in human immunodeficiency virus positive patients. AJNR Am J Neuroradiol 2005;26:2275 2281. 92. Wu Y, Storey P, Cohen BA, Epstein LG, Edelman RR, Ragin AB. Diffusion alterations in corpus callosum of patients with HIV. AJNR Am J Neuroradiol 2006;27:656 660. 93. Pomara N, Crandall DT, Choi SJ, Johnson G, Lim KO. White matter abnormalities in HIV 1 infection: a diffusion tensor imaging study. Psychiatry Res 20 01;106:15 24.
66 94. Wright PW, Heaps JM, Shimony JS, Thomas JB, Ances BM. The effects of HIV and combination antiretroviral therapy on white matter integrity. AIDS 2012;26:1501 1508. 95. Pfefferbaum A, Rosenbloom MJ, Rohlfing T, Kemper CA, Deresinski S, Sullivan EV Frontostriatal fiber bundle compromise in HIV infection without dementia. AIDS 2009;23:1977 1985. 96. Gongvatana A, Schweinsburg BC, Taylor MJ, et al. White matter tract injury and cognitive impairment in human immunodeficiency virus infected individuals. J Neurovirol 2009;15:187 195. 97. Ragin AB, Wu Y, Storey P, Cohen BA, Edelman RR, Epstein LG. Diffusion tensor imaging of subcortical brain injury in patients infected with human immunodeficiency virus. Journal of neurovirology 2005;11:292 298. 98. Aylward EH, Br ettschneider PD, McArthur JC, et al. Magnetic resonance imaging measurement of gray matter volume reductions in HIV dementia. Am J Psychiatry 1995;152:987 994. 99. Zhu T, Zhong J, Hu R, et al. Patterns of white matter injury in HIV infection after partial immune reconstitution: a DTI tract based spatial statistics study. J Neurovirol 2013;19:10 23. 100. Stebbins GT, Smith CA, Bartt RE, et al. HIV associated alterations in normal appearing white matter: a voxel wise diffusion tensor imaging study. Journal of acq uired immune deficiency syndromes 2007;46:564 573. 101. Nakamoto BK, Jahanshad N, McMurtray A, et al. Cerebrovascular risk factors and brain microstructural abnormalities on diffusion tensor images in HIV infected individuals. J Neurovirol 2012;18:303 312. 102. Ra dloff LS. The CES D Scale: A self report depression scale for research in the general population. Applied Psychological Measurement 1977;1:385 401. 103. Zich JM, Attkisson CC, Greenfield TK. Screening for depression in primary care clinics: the CES D and the B DI. International journal of psychiatry in medicine 1990;20:259 277. 104. Kellogg SH, McHugh PF, Bell K, et al. The Kreek McHugh Schluger Kellogg scale: a new, rapid method for quantifying substance abuse and its possible applications. Drug and alcohol depende nce 2003;69:137 150. 105. Heaton RK, Grant I, Butters N, et al. The HNRC 500 -neuropsychology of HIV infection at different disease stages. HIV Neurobehavioral Research Center. J Int Neuropsychol Soc 1995;1:231 251.
67 106. Carey CL, Woods SP, Gonzalez R, et al. Predi ctive validity of global deficit scores in detecting neuropsychological impairment in HIV infection. Journal of clinical and experimental neuropsychology 2004;26:307 319. 107. Reitan RM. Trail Making Test. Tucson, AZ: Reitan Neuropsychology Laboratory, 1992. 108. Wechsler D. Wechsler Adult Intelligence Scale III (WAIS III). San Antonio, TX: The Psychological Corporation, 1997. 109. Golden C. Stroop Color and Word Test. Chicago: Stoelting, 1978. 110. Benedict RHB, Schretlen D, Groninger L, Brandt J. Hopkins Verbal Learning Test Revised: Normative Data and Analysis of Inter Form and Test Retest Reliability. The Clinical Neuropsychologist 1998;12:43 55. 111. Benedict RHB, Schretlen D, Groninger L, Dobraski M, Shpritz B. Revision of the Brief Visuospatial Memory Test: Studies of normal performance, reliability, and validity. Psychological Assessment 1996;8:145 153. 112. Benton AL, Hamsher K, Sivan AB. Multilingual Aphasia Examination. Iowa City: AJA Associates, 1994. 113. Klve H. Grooved pegboard. Lafayette, IN: Lafayette Instruments, 19 63. 114. Heaton RK, Miller, W., Taylor, M., & Grant, I. Revised comprehensive norms for an expanded Halstead Reitan battery: Demographically adjusted neuropsychological norms for African American and Caucasian adults. Lutz, FL: Psychological Assessment Resourc es, 2004. 115. Gongvatana A, Cohen RA, Correia S, et al. Clinical contributors to cerebral white matter integrity in HIV infected individuals. Journal of neurovirology 2011;17:477 486. 116. Fischl B. FreeSurfer. Neuroimage 2012;62:774 781. 117. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage 2012;62:782 790. 118. Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage 2009;45:S173 186. 119. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in function al and structural MR image analysis and implementation as FSL. Neuroimage 2004;23 Suppl 1:S208 219.
68 120. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Medical image analysis 2001;5:143 156. 121. Jenkinson M, Bann ister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002;17:825 841. 122. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary based registra tion. Neuroimage 2009;48:63 72. 123. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and biomedical research, an international journal 1996;29:162 173. 124. Cox RW, Hyde JS. Software tools for analysis and visualization of fMRI data. NMR in biomedicine 1997;10:171 178. 125. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE transactions on medical imaging 2001 ;20:45 57. 126. Duara R, Loewenstein DA, Potter E, et al. Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology 2008;71:1986 1992. 127. Alexander DC, Pierpaoli C, Basser PJ, Gee JC. Spatial transformations of diffusion tensor magnetic resonance images. IEEE transactions on medical imaging 2001;20:1131 1139. 128. Basser PJ, Jones DK. Diffusion tensor MRI: theory, experimental design and data analysis a technical review. NMR in biomedicine 2002;15:456 467. 129. Fischl B, Salat DH, van der Kouwe AJ, et al. Sequence independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69 84. 130. Desikan RS, Segonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 2006;31:968 980. 131. Little TD, Bovaird JA, Widaman KF. On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables. Structural Equation Modeling 2006;13:497 5 19.
69 132. Marsh HW, Wen Z, Hau KT, Little TD, Bovaird JA, Widaman KF. Unconstrained structural equation models of latent interactions: Contrasting residual and mean centered approaches. Structural Equation Modeling 2007;14:570 580. 133. Becker JT, Maruca V, Kingsl ey LA, et al. Factors affecting brain structure in men with HIV disease in the post HAART era. Neuroradiology 2012;54:113 121. 134. Sacktor N, Skolasky RL, Cox C, et al. Longitudinal psychomotor speed performance in human immunodeficiency virus seropositive in dividuals: impact of age and serostatus. J Neurovirol 2010;16:335 341. 135. Seider TR, Luo X, Gongvatana A, et al. Verbal memory declines more rapidly with age in HIV infected versus uninfected adults. Submitted for publication. 2014. 136. Chang L, Holt JL, Yakupo v R, Jiang CS, Ernst T. Lower cognitive reserve in the aging human immunodeficiency virus infected brain. Neurobiology of aging 2013;34:1240 1253. 137. Ances BM, Ortega M, Vaida F, Heaps J, Paul R. Independent effects of HIV, aging, and HAART on brain volumetric measures. J Acquir Immune Defic Syndr 2012;59:469 477. 138. Nir TM, Jahanshad N, Busovaca E, et al. Mapping white matter integrity in elderly people with HIV. Human brain mapping 2013. 139. Minghetti L. Role of inflammation in neurodegenerative diseases. Curr Opin Neurol 2005;18:315 321. 140. Lawrence DM, Major EO. HIV 1 and the brain: connections between HIV 1 associated dementia, neuropathology and neuroimmunology. Microbes Infect 2002;4:301 308. 141. Valcour VG, Shikuma CM, Watters MR, Sacktor NC. Cog nitive impairment in older HIV 1 seropositive individuals: prevalence and potential mechanisms. AIDS 2004;18 Suppl 1:S79 86. 142. Gisslen M, Krut J, Andreasson U, et al. Amyloid and tau cerebrospinal fluid biomarkers in HIV infection. BMC Neurol 2009;9:63. 143. An thony IC, Ramage SN, Carnie FW, Simmonds P, Bell JE. Accelerated Tau deposition in the brains of individuals infected with human immunodeficiency virus 1 before and after the advent of highly active anti retroviral therapy. Acta neuropathologica 2006;111:5 29 538.
70 144. Guaraldi G, Orlando G, Zona S, et al. Premature age related comorbidities among HIV infected persons compared with the general population. Clin Infect Dis 2011;53:1120 1126. 145. Brew BJ, Rosenblum M, Cronin K, Price RW. AIDS dementia complex and HIV 1 brain infection: clinical virological correlations. Ann Neurol 1995;38:563 570. 146. Andras IE, Toborek M. Amyloid beta accumulation in HIV 1 infected brain: The role of the blood brain barrier. IUBMB life 2013;65:43 49. 147. Xu J, Ikezu T. The comorbidity of HIV associated neurocognitive disorders and Alzheimer's disease: a foreseeable medical challenge in post HAART era. Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology 2009;4:200 212. 148. Bhatia R, Ryscavage P, T aiwo B. Accelerated aging and human immunodeficiency virus infection: emerging challenges of growing older in the era of successful antiretroviral therapy. J Neurovirol 2012;18:247 255. 149. Morgan EE, Iudicello JE, Weber E, et al. Synergistic effects of HIV i nfection and older age on daily functioning. J Acquir Immune Defic Syndr 2012;61:341 348. 150. King KS, Peshock RM, Rossetti HC, et al. Effect of normal aging versus hypertension, abnormal body mass index, and diabetes mellitus on white matter hyperintensity v olume. Stroke; a journal of cerebral circulation 2014;45:255 257. 151. Chao LL, Decarli C, Kriger S, et al. Associations between white matter hyperintensities and beta amyloid on integrity of projection, association, and limbic fiber tracts measured with diffusion tensor MRI. PloS one 2013;8:e65175. 152. Filippi C, Ulug A, Ryan E, Ferrando S, Van Gorp W. Diffusion tensor imaging of patients with HIV and normal appearing white matter on MR images of the brain. AJNR Am J Neuroradiol 2001;22:277 283. 153. Cohen RA, H arezlak J, Schifitto G, et al. Effects of nadir CD4 count and duration of human immunodeficiency virus infection on brain volumes in the highly active antiretroviral therapy era. Journal of neurovirology 2010;16:25 32. 154. Devlin KN, Gongvatana A, Clark US, e t al. Neurocognitive effects of HIV, hepatitis C, and substance use history. J Int Neuropsychol Soc 2012;18:68 78.
71 155. Cherner M, Letendre S, Heaton RK, et al. Hepatitis C augments cognitive deficits associated with HIV infection and methamphetamine. Neurology 2005;64:1343 1347. 156. Laskus T, Radkowski M, Bednarska A, et al. Detection and analysis of hepatitis C virus sequences in cerebrospinal fluid. Journal of virology 2002;76:10064 10068. 157. Letendre SL, Cherner M, Ellis RJ, et al. The effects of hepatitis C, HIV and methamphetamine dependence on neuropsychological performance: biological correlates of disease. AIDS 2005;19 Suppl 3:S72 78. 158. Laskus T, Radkowski M, Adair DM, Wilkinson J, Scheck AC, Rakela J. Emerging evidence of hepatitis C virus neuroinvasion. AID S 2005;19 Suppl 3:S140 144. 159. Hinkin CH, Castellon SA, Levine AJ, Barclay TR, Singer EJ. Neurocognition in individuals co infected with HIV and hepatitis C. Journal of addictive diseases 2008;27:11 17. 160. Tate DF, Sampat M, Harezlak J, et al. Regional areas a nd widths of the midsagittal corpus callosum among HIV infected patients on stable antiretroviral therapies. J Neurovirol 2011;17:368 379. 161. Joy S, Kaplan E, Fein D. Speed and memory in the WAIS III Digit Symbol -Coding subtest across the adult lifespan. Ar chives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists 2004;19:759 767. 162. Sanchez Cubillo I, Perianez JA, Adrover Roig D, et al. Construct validity of the Trail Making Test: role of task switching, working memory, inhibition/interference control, and visuomotor abilities. J Int Neuropsychol Soc 2009;15:438 450. 163. Catani M, Thiebaut de Schotten M. Atlas of human brain connections. Oxford ; New York: Oxford University Press, 2012.
72 BIOGRAPHICAL SKETCH Talia Seider was born in 1988 in Los Angeles, California. She attended the University of California, B erkeley and earned her Bachelor of Arts in Psychology in 2010. Upon graduation, Talia worked as a study coordinator for a research study on the effects of vascular risk factors on brain structure and function under the mentorship of Dr. Joel Kramer at the University of California, San Francisco Memory and Aging Center. She began graduate work in 2012 in the Department of Clinical and Health Psychology at th e University of Florida under the mentorship of Dr. Ronald Cohen. Her current research focuses on the neurobiological and cognitive effects of aging.
xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID EHN4YIH7R_KRFCJF INGEST_TIME 2014-10-03T22:57:10Z PACKAGE UFE0046778_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC