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Heavy drinking and HIV infection are independently associated with damage to the brain's white matter. The purpose of the current study was to investigate whether current alcohol consumption, HIV infection, and associated characteristics were associated with indices of white matter microstructural integrity in people living with HIV (PLWH) and seronegative individuals. PLWH and controls were categorized as non-drinkers, moderate drinkers, or heavy drinkers. White matter fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were assessed using diffusion tensor imaging (DTI). Voxelwise analyses using tract-based spatial statistics were followed by confirmatory region-of-interest (ROI) analyses. Data from 108 participants (62 PLWH, 46 controls) were suitable for analysis. Average age (± standard deviation) was 45.2 ± 11.1 years, and the sample was 42% female. The majority of PLWH were on antiretroviral therapy (94%) and were virally suppressed (69%). PLWH and controls did not differ on substance use. Heavier alcohol intake was significantly associated with lower FA and higher RD in widespread areas. Heavy drinking was significantly associated with higher AD in a small region. The main effect of HIV was not significant, but a significant HIV-age interaction was observed. Follow-up ROI analyses confirmed the main effect of drinking group and HIV-age interaction. In conclusion, results are consistent with a dose-dependent association of alcohol use with lower white matter microstructural coherence. Concordance between FA and RD findings suggests dysmyelination as a mechanism. Findings underscore the need to address unhealthy alcohol use in HIV-positive and seronegative individuals, the consequences of which may be exacerbated by aging.

A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging. Supplementary Information The online version contains supplementary material available at 10.1007/s11682-021-00548-y.

The ENIGMA‐DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder‐oriented working groups used the ENIGMA‐DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive–compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA‐defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross‐diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large‐scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross‐diagnosis features.

Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to microstructural changes in the brain that occur with normal aging and Alzheimer’s disease (AD). There is much interest in which dMRI measures are most strongly correlated with clinical measures of AD severity, such as the clinical dementia rating (CDR), and biological processes that may be disrupted in AD, such as brain amyloid load measured using PET. Of these processes, some can be targeted using novel drugs. Since 2016, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has collected dMRI data from three scanner manufacturers across 58 sites using 7 different protocols that vary in angular resolution, scan duration, and in the number and distribution of diffusion-weighted gradients. Here, we assessed dMRI data from 730 of those individuals (447 cognitively normal controls, 214 with mild cognitive impairment, 69 with dementia; age: 74.1±7.9 years; 381 female/349 male). To harmonize data from different protocols, we applied ComBat, ComBat-GAM, and CovBat to dMRI metrics from 28 white matter regions of interest. We ranked all dMRI metrics in order of the strength of clinically relevant associations, and assessed how this depended on the harmonization methods employed. dMRI metrics were associated with age and clinical impairment, but also with amyloid positivity. All harmonization methods gave comparable results while enabling data integration across multiple scanners and protocols.

BackgroundHuman immunodeficiency virus type-1 (HIV) infection can be controlled with combination antiretroviral therapy (cART), but neurocognitive impairment remains common even in chronic and treated HIV-infected (HIV+) cohorts. Identifying the neuroanatomical pathways associated with infection has the potential to delineate novel neuropathological processes underlying persisting deficits, yet individual neuroimaging studies have yielded inconsistent findings. The ENIGMA-HIV Working Group was established to harmonize data from diverse studies to identify the common effects of HIV-infection on brain structure.MethodsData were pooled from 12 independent neuroHIV studies from Africa, Asia, Australia, Europe, and North America. Volume estimates for eight subcortical brain regions were extracted from T1-weighted MRI from 1,044 HIV+ adults (aged 22-81 years; 72.4% on cART; 70.3% male; 41.6% with detectable viral load (dVL)), to identify associations with plasma markers reflecting current immunosuppression (CD4+ T-cell count) or dVL. Follow-up analyses stratified data by cART status and sex. Bonferroni correction was used to determine statistical significance.FindingsLower current CD4+ count was associated with smaller hippocampal (β= 20.3 mm3per 100 cells/mm3;p= 0.0001) and thalamic volumes (β= 29.3;p= 0.003); in the subset of participants not on cART, it was associated with smaller putamen volumes (β= 65.1;p= 0.0009). On average, a dVL was associated with smaller hippocampal (Cohen’sd= 0.24;p= 0.0003) and amygdala volumes (d= 0.18;p= 0.0058).InterpretationIn HIV+ individuals across five continents, smaller limbic volumes were consistently associated with current plasma markers. As we assessed cohorts with different inclusion/exclusion criteria and demographic distributions, these deficits may represent a generalizable brain-signature of HIV infection in the cART era. Our findings support the importance of achieving viral suppression and immune restoration for maintaining brain health.FundingThis work was supported, in part, by NIH grant U54 EB020403.Research in ContextEvidence before this studyHIV type-1 infection can be managed with antiretroviral therapy, however neurocognitive impairment persists even in treated HIV+ individuals. Given the challenges associated with standardized cognitive testing, there is a need to identify quantitative markers of central nervous system impairment. A number of neuroimaging studies have reported brain abnormalities in HIV-infected patients; however, prior studies investigating associations between CD4+ T-cell count or HIV viral load and subcortical brain volume report variable effect sizes and regional distributions of effects, limiting the generalizability of the conclusions drawn to date. We have conducted a literature search for reports in English language journals published until June 2019, using the following search terms: HIV AND subcortical AND neuroimaging AND brain AND viral load AND RNA AND CD4. After removing studies that were not applicable, there were 30 studies investigating CD4+ T-cell count and viral load associations with subcortical brain structure.Added value of the studyThe aim of the current study was to investigate structural brain associations with two biomarkers universally used to monitor immune function and treatment response, namely plasma RNA viral load and CD4+ T-cell counts. Prior analyses have been performed in smaller, heterogeneous cohorts, but by combining data across cohorts, we can identify consistent associations between brain volume and indicators of HIV infection across cohorts. The ENIGMA-HIV Working Group was established to identify common neurobiological signatures of the HIV-infected brain by harmonizing data analysis from HIV neuroimaging studies worldwide. The value of this dataset is that it is well-powered and representative of many HIV+ people living in the cART era.Implications of all the available evidenceOur results provide robust evidence that despite demographic and clinical heterogeneity among HIV-infected individuals, brain abnormalities are consistently linked to HIV viral load and immunosuppression. This supports the importance of achieving viral suppression and immune system restoration in maintaining brain health in people living with HIV. The vulnerability of limbic regions, found in this study, extends beyond the classically implicated regions of the basal ganglia; this suggests that these regions remain an important target of cART era HIV research, especially given their heightened vulnerability to age-associated atrophy and neurodegeneration.

Key Points Question Are HIV plasma markers that are universally used to monitor immune function and treatment response associated with subcortical brain volumes in clinically and demographically heterogeneous HIV-infected individuals? Findings In this cross-sectional study of 1203 HIV-infected adults, lower current CD4+ T-cell counts were associated with smaller hippocampal and thalamic volumes independent of treatment status, although in the subset of participants not receiving treatment, they were associated with smaller putamen volumes. Across all participants, detectable viral load was associated with smaller hippocampal volumes, but in the subset of participants receiving HIV treatment, detectable viral load was also associated with smaller amygdala volumes. Meaning In a heterogeneous population of HIV-infected individuals, volumes of structures in the limbic system were consistently associated with plasma markers., Importance Despite more widely accessible combination antiretroviral therapy (cART), HIV-1 infection remains a global public health challenge. Even in treated patients with chronic HIV infection, neurocognitive impairment often persists, affecting quality of life. Identifying the neuroanatomical pathways associated with infection in vivo may delineate the neuropathologic processes underlying these deficits. However, published neuroimaging findings from relatively small, heterogeneous cohorts are inconsistent, limiting the generalizability of the conclusions drawn to date. Objective To examine structural brain associations with the most commonly collected clinical assessments of HIV burden (CD4+ T-cell count and viral load), which are generalizable across demographically and clinically diverse HIV-infected individuals worldwide. Design, Setting, and Participants This cross-sectional study established the HIV Working Group within the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) consortium to pool and harmonize data from existing HIV neuroimaging studies. In total, data from 1295 HIV-positive adults were contributed from 13 studies across Africa, Asia, Australia, Europe, and North America. Regional and whole brain segmentations were extracted from data sets as contributing studies joined the consortium on a rolling basis from November 1, 2014, to December 31, 2019. Main Outcomes and Measures Volume estimates for 8 subcortical brain regions were extracted from T1-weighted magnetic resonance images to identify associations with blood plasma markers of current immunosuppression (CD4+ T-cell counts) or detectable plasma viral load (dVL) in HIV-positive participants. Post hoc sensitivity analyses stratified data by cART status. Results After quality assurance, data from 1203 HIV-positive individuals (mean [SD] age, 45.7 [11.5] years; 880 [73.2%] male; 897 [74.6%] taking cART) remained. Lower current CD4+ cell counts were associated with smaller hippocampal (mean [SE] β = 16.66 [4.72] mm3 per 100 cells/mm3; P, This cross-sectional study examines structural brain associations with CD4+ T-cell count and viral load in HIV-positive individuals taking and not taking combination antiretroviral therapy.

In the "cART era" of more widely available and accessible treatment, aging and HIV-related comorbidities, including symptoms of brain dysfunction, remain common among HIV-infected individuals on suppressive treatment. A better understanding of the neurobiological consequences of HIV infection is essential for developing thorough treatment guidelines and for optimizing long-term neuropsychological outcomes and overall brain health. In this chapter, we first summarize magnetic resonance imaging (MRI) methods used in over two decades of neuroHIV research. These methods evaluate brain volumetric differences and circuitry disruptions in adults living with HIV, and help map clinical correlations with brain function and tissue microstructure. We then introduce and discuss aging and associated neurological complications in people living with HIV, and processes by which infection may contribute to the risk for late-onset dementias. We describe how new technologies and large-scale international collaborations are helping to disentangle the effect of genetic and environmental risk factors on brain aging and neurodegenerative diseases. We provide insights into how these advances, which are now at the forefront of Alzheimer's disease research, may advance the field of neuroHIV. We conclude with a summary of how we see the field of neuroHIV research advancing in the decades to come and highlight potential clinical implications.

Few studies have examined systemic mitochondrial function in conjunction with brain imaging in human immunodeficiency virus (HIV) disease. Oxidative phosphorylation enzyme protein levels of peripheral blood mononuclear cells were measured in association with neuroimaging indices in 28 HIV+ individuals. T1-weighted magnetic resonance imaging yielded volumes of seven brain regions of interest; diffusion tensor imaging determined fractional anisotropy (FA) and mean diffusivity (MD) in the corpus callosum (CC). Higher nicotinamide adenine dinucleotide dehydrogenase levels correlated with lower volumes of thalamus (p = .005) and cerebral white matter (p = .049) and, in the CC, with lower FA (p = .011, body; p = .005, genu; p = .009, total CC) and higher MD (p = .023, body; p = .035, genu; p = .019, splenium; p = .014, total CC). Greater cytochrome c oxidase levels correlated with lower thalamic (p = .034) and cerebellar gray matter (p = .021) volumes. The results indicate that systemic mitochondrial cellular bioenergetics are associated with brain health in HIV.

Recent evidence suggests the aging process is accelerated by HIV. Degradation of white matter (WM) has been independently associated with HIV and healthy aging. Thus, WM may be vulnerable to joint effects of HIV and aging. Diffusion-weighted imaging (DWI) was conducted with HIV-seropositive (n = 72) and HIV-seronegative (n = 34) adults. DWI data underwent tractography, which was parcellated into 18 WM tracts of interest (TOIs). Functional Analysis of Diffusion Tensor Tract Statistics (FADTTS) regression was conducted assessing the joint effect of advanced age and HIV on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) along TOI fibers. In addition to main effects of age and HIV on WM microstructure, the interactive effect of age and HIV was significantly related to lower FA and higher MD, AD, and RD across all TOIs. The location of findings was consistent with the clinical presentation of HIV-associated neurocognitive disorders. While older age is related to poorer WM microstructure, its detrimental effect on WM is stronger among HIV+ relative to HIV- individuals. Loss of WM integrity in the context of advancing age may place HIV+ individuals at increased risk for brain and cognitive compromise.

The Alzheimer’s Disease Neuroimaging Initiative (phase 3; ADNI3) is collecting multisite diffusion MRI (dMRI) data using protocols optimized for different scanner vendors, including one multi-shell protocol, to better understand disease effects. Here, we analyzed multi-shell scans from 56 ADNI3 participants (age: $74.3 \pm 7.5$ yrs; 17F/49M). We evaluated whether multi-shell dMRI measures computed from neurite orientation dispersion and density imaging (NODDI) and diffusion kurtosis imaging (DKI) differentiated people with mild cognitive impairment from healthy controls with higher sensitivity than standard diffusion tensor imaging (DTI) measures. We also assessed the effects of various multi-shell derived dMRI samples on the sensitivity of DTI measures. While we did not identify large differences in effect sizes among tensor-based, NODDI, or DKI measures, we did detect greater effect sizes from DTI measures estimated using multi-shell data converted to single-shell HARDI compared to those fit using the subset of $48 b=1000s /$mm $^{2}$ volumes, typical of DTI.

Growing evidence points to persistent neurological injury in chronic HIV infection. It remains unclear whether chronically HIV-infected individuals on combined antiretroviral therapy (cART) develop progressive brain injury and impaired neurocognitive function despite successful viral suppression and immunological restoration. In a longitudinal neuroimaging study for the HIV Neuroimaging Consortium (HIVNC), we mapped the annual rate of change of regional brain volumes in 155 chronically infected and treated HIV+ participants (mean age 48.0 ± 8.9 yrs; 83.9% Male), using tensor-based morphometry between two time points (mean time interval: 1.0 ± 0.5 yrs). We tested for associations between rates of brain tissue loss and clinical measures of disease severity (nadir or baseline CD4+ cell count and baseline HIV plasma RNA concentration), HIV duration, cART CNS penetration-effectiveness scores, age, as well as change in AIDS Dementia Complex stage. We found significant brain tissue loss across HIV+ participants, including those neuro-asymptomatic with undetectable viral load, largely localized to subcortical regions. Measures of disease severity, age, and neurocognitive decline were associated with greater atrophy. Chronically HIV-infected and treated individuals may undergo progressive brain tissue despite stable and effective cART, which may contribute to neurocognitive decline. Understanding neurological complications of chronic infection, and identifying factors associated with atrophy may help inform strategies to maintain brain health in people living with HIV.

Background There are contradicting reports on the associations between Apolipoprotein E4 (ApoE e4) and brain outcomes in HIV with some evidence that relationships may be greatest in older age groups. Methods We assessed cognition in 76 clinically stable HIV-infected participants over age 60 and genotyped ApoE. Sixty-one of these subjects underwent structural brain magnetic resonance imaging and diffusion tensor imaging. Results The median age of the participants was 64 years (range: 60-84) and the median estimated duration of HIV infection was 22 years. Apo e4 carriers (n = 19) were similar to noncarriers (n = 57) in sex (95% vs. 96% male), and education (16.0 vs. 16.2 years) ApoE e4 carriers demonstrated greater deficits in cognitive performance in the executive domain (P = 0.045) and had reduced fractional anisotropy and increased mean diffusivity throughout large white matter tracts within the brain compared with noncarriers. Tensor-based morphometry analyses revealed ventricular expansion and atrophy in the posterior corpus callosum, thalamus, and brainstem among HIV-infected ApoE e4 carriers compared with e4 noncarriers. Conclusions In this sample of older HIV-infected individuals, having at least 1 ApoE e4 allele was associated with decreased cognitive performance in the executive functioning domain, reduced brain white matter integrity, and brain atrophy. Brain atrophy was most prominent in the posterior corpus callosum, thalamus, and brainstem. This pattern of cognitive deficit, atrophy, and damage to white matter integrity was similar to that described in HIV, suggesting an exacerbation of HIV-related pathology; although emergence of other age-associated neurodegenerative disorders cannot be excluded.

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer’s Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4±7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimer’s disease: the Alzheimer’s Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

Alterations in subcortical brain structures have been reported in adults with HIV and, to a lesser extent, pediatric cohorts. The extent of longitudinal structural abnormalities in children with perinatal HIV infection (PaHIV) remains unclear. We modeled subcortical morphometry from whole brain structural magnetic resonance imaging (1.5 T) scans of 43 Thai children with PaHIV (baseline age = 11.09±2.36 years) and 50 HIV− children (11.26±2.80 years) using volumetric and surface-based shape analyses. The PaHIV sample were randomized to initiate combination antiretroviral treatment (cART) when CD4 counts were 15–24% (immediate: n = 22) or when CD4

Background: Despite more widely accessible combination antiretroviral therapy (cART), the human immunodeficiency virus type-1 (HIV) infection remains a global public health challenge. Even in treated and chronically HIV-infected (HIV+) individuals, neurocognitive impairment often persists, affecting quality of life. Identifying the neuroanatomical pathways associated with infection in-vivo may delineate the neuropathological processes underlying these deficits. However, to date, neuroimaging findings are inconsistent. Methods: We established the ENIGMA-HIV Working Group to retrospectively pool and harmonize heterogeneous data from 12 HIV neuroimaging studies across Africa, Asia, Australia, Europe, and North America. Volume estimates for eight subcortical brain regions were extracted from T1- weighted MRI from 1,044 HIV+ adults (aged 22-81 years; 72·4% on cART; 70·3% male; 41·6% with detectable viral load; dVL), to identify associations with plasma markers reflecting current immunosuppression (CD4+ T-cell counts) or dVL. Follow-up analyses stratified data by cART status and sex. Findings: After Bonferroni correction, lower current CD4+ counts were associated with smaller hippocampal (β=20·3 mm3 per 100 cells/mm3; p=0·0001) and thalamic volumes (β=29.3; p=0·003); in participants not on cART, they were associated with smaller putamen volumes (β=65·1; p=0·0009). dVLs were associated with smaller hippocampi (Cohen's d=0·24; p=0·0003) and amygdala (d=0·18; p=0·0058). Interpretation: In a globally representative population of HIV+ individuals, volumes of structures in the limbic system were consistently associated with current plasma markers. Deficits in these regions may represent a generalizable brain signature of HIV-infection in the cART era. Our findings support the role of viral suppression and immune restoration in maintaining brain health. Funding Statement: Funding for the ENIGMA Center for Worldwide Medicine Imaging and Genomics was provided by the NIH Big Data to Knowledge Program (BD2K; U54 EB020403). Additional funding for this work was provided by NIH grants R01AG059874 (NJ), R01MH117601 (NJ), T32AG058507 (TMN and CRKC), 5T32MH073526 (CRKC), MH083553 (CHH), MH19535 (CHH), R01HL095135 (KJK), MH095661 (TK), UL1RR033176 (TK), UL1TR000124 (TK), MH19535 (TK), NS080655 (BAN), K01AA025306 (ECP), R01HL095135 (CMS), U54MD007584 (CMS), K23MH095661 (ADT), K23AG032872 (VV), R01NS061696 (VV), K24MH098759 (VV), K01AG050707 (AJW), P01AA019072 (RAC), R01MH074368 (RAC), P30 AI042853 (RAC), and by R01MH085604 (RP). This study was also supported by the SA Medical Research Council (DJS), NHMRC APP568746 and APP1045400 (LAC), and the European Research Council Advanced Grant MedYMA 2011- 291080 (XP). Declaration of Interests: PMT, TMN, NJ, and CRKC received partial research support from Biogen, Inc., for work unrelated to the topic of this manuscript. BKN has received an honorarium from MedLink Neurology. VV has served as a consultant for Merck and ViiV Healthcare in the past 3 years, not related to this work. JA has received honoraria for participating in advisory meetings for ViiV Healthcare, Gilead, Merck, Roche and AbbVie. The remaining authors all declare no conflicts of interest. Ethics Approval Statement: Each study obtained approval from their local ethics committee or institutional review board; participants signed an informed consent form at each participating site.

Background: Despite more widely accessible combination antiretroviral therapy (cART), the human immunodeficiency virus type-1 (HIV) infection remains a global public health challenge. Even in treated and chronically HIV-infected (HIV+) individuals, neurocognitive impairment often persists, affecting quality of life. Identifying the neuroanatomical pathways associated with infection in-vivo may delineate the neuropathological processes underlying these deficits. However, to date, neuroimaging findings are inconsistent. Methods: We established the ENIGMA-HIV Working Group to retrospectively pool and harmonize heterogeneous data from 12 HIV neuroimaging studies across Africa, Asia, Australia, Europe, and North America. Volume estimates for eight subcortical brain regions were extracted from T1- weighted MRI from 1,044 HIV+ adults (aged 22-81 years; 72·4% on cART; 70·3% male; 41·6% with detectable viral load; dVL), to identify associations with plasma markers reflecting current immunosuppression (CD4+ T-cell counts) or dVL. Follow-up analyses stratified data by cART status and sex. Findings: After Bonferroni correction, lower current CD4+ counts were associated with smaller hippocampal (β=20·3 mm3 per 100 cells/mm3; p=0·0001) and thalamic volumes (β=29.3; p=0·003); in participants not on cART, they were associated with smaller putamen volumes (β=65·1; p=0·0009). dVLs were associated with smaller hippocampi (Cohen's d=0·24; p=0·0003) and amygdala (d=0·18; p=0·0058). Interpretation: In a globally representative population of HIV+ individuals, volumes of structures in the limbic system were consistently associated with current plasma markers. Deficits in these regions may represent a generalizable brain signature of HIV-infection in the cART-era. Our findings support the role of viral suppression and immune restoration in maintaining brain health. Funding Statement: Funding for the ENIGMA Center for Worldwide Medicine Imaging and Genomics was provided by the NIH Big Data to Knowledge Program (BD2K; U54 EB020403). Additional funding for this work was provided by NIH grants R01AG059874 (NJ), R01MH117601 (NJ), T32AG058507 (TMN and CRKC), 5T32MH073526 (CRKC), MH083553 (CHH), MH19535 (CHH), R01HL095135 (KJK), MH095661 (TK), UL1RR033176 (TK), UL1TR000124 (TK), MH19535 (TK), NS080655 (BAN), K01AA025306 (ECP), R01HL095135 (CMS), U54MD007584 (CMS), K23MH095661 (ADT), K23AG032872 (VV), R01NS061696 (VV), K24MH098759 (VV), K01AG050707 (AJW), P01AA019072 (RAC), R01MH074368 (RAC), P30 AI042853 (RAC), and by R01MH085604 (RP). This study was also supported by the SA Medical Research Council (DJS), NHMRC APP568746 and APP1045400 (LAC), and the European Research Council Advanced Grant MedYMA 2011- 291080 (XP). Declaration of Interests: PMT, TMN, NJ, and CRKC received partial research support from Biogen, Inc., for work unrelated to the topic of this manuscript. BKN has received an honorarium from MedLink Neurology. VV has served as a consultant for Merck and ViiV Healthcare in the past 3 years, not related to this work. JA has received honoraria for participating in advisory meetings for ViiV Healthcare, Gilead, Merck, Roche and AbbVie. The remaining authors all declare no conflicts of interest. Ethics Approval Statement: Each study obtained approval from their local ethics committee or institutional review board; participants signed an informed consent form at each participating site.

Diffusion-weighted MRI (dMRI) offers a range of measures that are sensitive to brain aging and neurodegeneration. Here we analyzed data from 318 participants (mean age: 75.4±7.9 years; 143 men/175 women) from the third phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI3), who were each scanned with one of six different diffusion MRI protocols using scanners from three different manufacturers. We computed 4 standard diffusion tensor imaging (DTI) anisotropy and diffusivity indices, and one advanced anisotropy index based on the tensor distribution function (TDF), in 24 white matter regions of interest. Modeling protocol effects, we ranked the diffusion indices for their strength of correlation with 3 standard clinical measures of cognitive impairment: the ADAS-Cog, MMSE, and sum-of-boxes Clinical Dementia Rating. Across all dMRI indices and cognitive measures, the cingulum-hippocampal region and the uncinate showed some of the strongest associations with cognitive impairment; largest effect sizes were detected with axial diffusivity (AxDDTI). While fractional anisotropy (FA) derived from the DTI model was the weakest in detecting associations with cognitive measures, FA derived from the TDF detected widespread, robust associations. Protocol differences affected dMRI indices; however by modeling protocol effects, we were able to pool dMRI data from multiple acquisition protocols and detect consistent associations with cognitive impairment and age. dMRI indices computed from the upgraded scanning protocols in ADNI3 were sensitive to cognitive impairment in brain aging, offering a benchmark to compare to future multi-shell or multi-compartment diffusion indices.

BACKGROUND: Alcohol use disorder (AUD) is prevalent among individuals diagnosed with human immunodeficiency virus (HIV), and both HIV and alcohol use have been shown to negatively affect the integrity of white matter pathways in the brain. Behavioral, functional, and anatomical impairments have been linked independently to HIV and alcohol use, and these impairments have bases in specific frontally-mediated pathways within the brain. METHODS: Magnetic Resonance Imaging (MRI) data were acquired for 37 HIV+ participants without dementia or hepatitis C. Imaging data were processed through the FreeSurfer and TraCULA pipelines to obtain four bilateral frontal white matter tracts for each participant. Diffusion metrics of white matter integrity along the highest probability pathway for each tract were analyzed with respect to demographics, disease-specific variables, and reported substance use. RESULTS: Significantly increased Axial Diffusivity (AD; decreased axonal integrity) and a trending increase in Mean Diffusivity (MD) were observed along the Anterior Thalamic Radiation (ATR) in participants with a history of AUD. A diagnosis of AUD explained over 36% of the variance in diffusivity along the Anterior Thalamic Radiation overall when accounting for clinical variables including Nadir CD4 and age-adjusted HIV infection length. CONCLUSIONS: The present study provides evidence of HIV-related associations between alcohol use and indicators of axonal integrity loss along the ATR, a frontal pathway involved in the inhibition of addictive or unwanted behaviors. Reduced axonal integrity of this pathway was greatest in HIV+ participants with an AUD, even when considering the effect of age-adjusted disease length and severity (Nadir CD4). This finding implicates a potential biological mechanism linking reduced integrity of frontal white matter to the high prevalence of AUD in an HIV+ population without dementia or hepatitis C.

Copyright © 2015 Wolters Kluwer Health, Inc. Background: Perinatal use of combination antiretroviral therapy dramatically reduces vertical (mother-to-child) transmission of HIV but has led to a growing population of children with perinatal HIV-exposure but uninfected (HEU). HIV can cause neurological injury among children born with infection, but the neuroanatomical and developmental effects in HEU children are poorly understood. Methods: We used structural magnetic resonance imaging with diffusion tensor imaging to compare brain anatomy between 30 HEU and 33 age-matched HIV-unexposed and uninfected (HUU) children from Thailand. Maps of brain volume and microstructural anatomy were compared across groups; associations were tested between neuroimaging measures and concurrent neuropsychological test performance. Results: Mean (standard deviation) age of children was 10.3 (2.8) years, and 58% were male. All were enrolled in school and lived with family members. Intelligence quotient (IQ) did not differ between groups. Caretaker education levels did not differ, but income was higher for HUU (P < 0.001). We did not detect group differences in brain volume or diffusion tensor imaging metrics, after controlling for sociodemographic factors. The mean (95% confidence interval) fractional anisotropy in the corpus callosum was 0.375 (0.368-0.381) in HEU compared with 0.370 (0.364-0.375) in HUU. Higher fractional anisotropy and lower mean diffusivity were each associated with higher IQ scores in analyses with both groups combined. Conclusions: No differences in neuroanatomical or brain integrity measures were detectable in HEU children compared with age-matched and sex-matched controls (HUU children). Expected associations between brain integrity measures and IQ scores were identified suggesting sufficient power to detect subtle associations that were present.

We compare a variety of different anatomical connectivity measures, including several novel ones, that may help in distinguishing Alzheimer’s disease patients from controls. We studied diffusion-weighted MRI from 200 subjects scanned as part of the Alzheimer’s disease Neuroimaging Initiative (ADNI). We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3D lattice. Based on these two kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures’ ability to discriminate disease with a repeated stratified 10-fold cross-validated classifier, using support vector machines (SVMs), a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls, and people with early- or late-stage mild cognitive impairment (MCI), or Alzheimer’s disease (AD).

BACKGROUND: Children with vertically acquired human immunodeficiency virus (HIV) exhibit persistent cognitive impairments, yet the corresponding neuroimaging signature of vertical infection remains unclear. METHODS: Fifty healthy control children and 51 vertically infected children were included in the study. The HIV-infected group consisted of survivors who had not received antiretroviral therapy (ART) at birth. The HIV-infected group averaged 11.4 (2.5) years of age, with a median CD4 count of 683 cells/mm(3). Most (71%) of the HIV-infected children were on ART for a median of 34 months (range: 33–42) with HIV RNA

Obesity is a crucial public health issue in developed countries, with implications for cardiovascular and brain health as we age. A number of commonly-carried genetic variants are associated with obesity. Here we aim to see whether variants in obesity-associated genes - NEGR1, FTO, MTCH2, MC4R, LRRN6C, MAP2K5, FAIM2, SEC16B, ETV5, BDNF- AS, ATXN2L, ATP2A1, KCTD15, and TNN13K - are associated with white matter microstructural properties, assessed by high angular resolution diffusion imaging (HARDI) in young healthy adults between 20 and 30. years of age from the Queensland Twin Imaging study (QTIM). We began with a multi-locus approach testing how a number of common genetic risk factors for obesity at the single nucleotide polymorphism (SNP) level may jointly influence white matter integrity throughout the brain and found a wide spread genetic effect. Risk allele rs2815752 in NEGR1 was most associated with lower white matter integrity across a substantial portion of the brain. Across the area of significance in the bilateral posterior corona radiata, each additional copy of the risk allele was associated with a 2.2% lower average FA. This is the first study to find an association between an obesity risk gene and differences in white matter integrity. As our subjects were young and healthy, our results suggest that NEGR1 has effects on brain structure independent of its effect on obesity.

Microstructural measures from diffusion MRI have been used for classification purposes in neurodegenerative and psychiatric conditions. Novel diffusion reconstruction models can lead to better and more accurate measures of tissue properties: each measure provides different information on white matter microstructure in the brain, revealing different signs of disease. The diversity of computable measures makes it necessary to develop novel classification procedures to capture all of the available information from each measure. Here we introduce a multichannel regularized logistic regression algorithm that classifies individuals’ diagnostic status based on several microstructural measures, derived from their diffusion MRI scans. With the aid of a TV-L1 prior, which ensures sparsity in the classification model, the resulting linear models point to the most classifying brain regions for each of the diffusion MRI measures, giving the method additional descriptive power. We apply our regularized regression approach to classify Alzheimer’s disease patients and healthy controls in the ADNI dataset, based on their diffusion MRI data.

Fractional anisotropy derived from the single-tensor model (FADTI) in diffusion MRI (dMRI) is the most widely used metric to characterize white matter (WM) micro-architecture in disease, despite known limitations in regions with extensive fiber crossing. Due to time constraints and interest in collecting multiple clinical samples and MRI scan types, complex HARDI acquisition protocols are rare in clinical population dMRI studies. Under such constraints, the tensor distribution function (TDF) can still be used to reconstruct multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. Here we set out to better profile WM deficits in Alzheimer's disease (AD) by comparing the standard FADTI and TDF-derived FA (FATDF) in (1) WM network connectivity and voxel-based analyses of diagnostic differences, and (2) for picking up associations with clinical cognitive ratings and hippocampal volume. Ultimately, the TDF approach may be more sensitive and accurate than corresponding DTI-derived measures.

Cognitive decline accompanies many debilitating illnesses, including Alzheimer’s disease (AD). In old age, brain tissue loss also occurs along with cognitive decline. Although blood tests are easier to perform than brain MRI, few studies compare brain scans to standard blood tests to see which kinds of information best predict future decline. In 504 older adults from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we first used linear regression to assess the relative value of different types of data to predict cognitive decline, including 196 blood panel biomarkers, 249 MRI biomarkers obtained from the FreeSurfer software, demographics, and the AD-risk gene APOE. A subset of MRI biomarkers was the strongest predictor. There was no specific blood marker that increased predictive accuracy on its own, we found that a novel unsupervised learning method, CorEx, captured weak correlations among blood markers, and the resulting clusters offered unique predictive power.

Diffusion magnetic resonance imaging (dMRI) has had a great impact on the study of the human brain connectome. Tractography methods allow for the reconstruction of white matter fiber tracts and bundles across the brain, by tracing the estimated direction of water diffusion across neighboring voxels. The tracts can then be used in conjunction with cortical parcellations to create structural connectivity matrices, to map the pattern and distribution of connections between cortical regions. However, the reliability of connectivity matrices is unclear. Tractography results depend on image resolution, and some reconstruction methods used to resolve the voxel-wise microstructure may be more robust to changes in resolution than others, leading to more stable connectivity estimates. We examined the reliability of structural connectivity matrices in 20 healthy young adults imaged with both high and low-resolution dMRI at two time points. We found that the Constrained Spherical Deconvolution (CSD) model produces the most reliable connections for both lower resolution and high resolution scans.

Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations. The approach produces a hierarchical structure and the predictive power of biological variables and latent factors are compared with regression. We found that a group of variables containing the well-known AD risk gene APOE and CSF tau and amyloid levels were highly correlated. This latent factor was the most predictive of cognitive decline and brain atrophy.

Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.

Measures of network topology and connectivity aid the understanding of network breakdown as the brain degenerates in Alzheimer's disease (AD). We analyzed 3-Tesla diffusion-weighted images from 202 patients scanned by the Alzheimer's Disease Neuroimaging Initiative – 50 healthy controls, 72 with early- and 38 with late-stage mild cognitive impairment (eMCI/lMCI) and 42 with AD. Using whole-brain tractography, we reconstructed structural connectivity networks representing connections between pairs of cortical regions. We examined, for the first time in this context, the network's Laplacian matrix and its Fiedler value, describing the network's algebraic connectivity, and the Fiedler vector, used to partition a graph. We assessed algebraic connectivity and four additional supporting metrics, revealing a decrease in network robustness and increasing disarray among nodes as dementia progressed. Network components became more disconnected and segregated, and their modularity increased. These measures are sensitive to diagnostic group differences, and may help understand the complex changes in AD.

We present a framework for registering cortical surfaces based on tractography-informed structural connectivity. We define connectivity as a continuous kernel on the product space of the cortex, and develop a method for estimating this kernel from tractography fiber models. Next, we formulate the kernel registration problem, and present a means to non-linearly register two brains' continuous connectivity profiles. We apply theoretical results from operator theory to develop an algorithm for decomposing the connectome into its shared and individual components. Lastly, we extend two discrete connectivity measures to the continuous case, and apply our framework to 98 Alzheimer's patients and controls. Our measures show significant differences between the two groups.

Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.

Properties of the brain's structural networks can be analyzed by applying fiber-tracking techniques and network analysis to diffusion MRI. Here we applied exploratory factor analysis (EFA) to anatomical connectivity matrices, to identify brain networks whose properties predicted higher-order cognitive function. Using diffusion MRI scans from 104 healthy young adults, we computed connectivity matrices based on deterministic and probabilistic tractography (with the FACT and Hough transform methods). Both sets of matrices were submitted to factor analysis, to identify sub-networks relevant for predicting cognitive function. The Kaiser-Meyer-Olkin measure and Bartlett's sphericity test were used to recover latent factors from the connectivity matrices, and only the Hough method yielded factorable outputs. Factor scores were related to fluid, crystallized, and spatial intelligence, and processing speed. Middle temporal and lateral prefrontal connectivity measures predicted all cognitive scores, except spatial intelligence. Cognitive performance was not predictable from global connectivity measures, which depended on the tractography method.

We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A flow network is first created using a lattice graph by connecting all lattice points (voxel centers) to all their neighbors by edges. Edge weights are based on the orientation distribution function (ODF) value in the direction of the edge. The maximum-flow is computed based on this flow graph using the flow or the capacity between each region of interest (ROI) pair by following the connected tractography fibers projected onto the flow graph edges. Network measures such as global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity are computed from the flow connectivity matrix. We applied our method to diffusion-weighted images (DWIs) from 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD) and segmented co-registered anatomical MRIs into cortical regions. Experimental results showed better performance compared to the standard fiber-counting methods when distinguishing Alzheimer's disease from normal aging.

Diffusion imaging and brain connectivity analyses can monitor white matter deterioration, revealing how neural pathways break down in aging and Alzheimer's disease (AD). Here we tested how AD disrupts the 'rich club' effect - a network property found in the normal brain - where high-degree nodes in the connectivity network are more heavily interconnected with each other than expected by chance. We analyzed 3-Tesla whole-brain diffusion-weighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. As expected, AD patients had a lower nodal degree (average number of connections) in cortical regions implicated in the disease. Unexpectedly, the normalized rich club coefficient was higher in AD. AD disrupts cortical networks by removing connections; when these networks are thresholded, organizational properties are disrupted leading to additional new biomarkers of AD.

We present a method for studying brain connectivity by simulating a dynamical evolution of the nodes of the network. The nodes are treated as particles, and evolved under a simulated force analogous to gravitational acceleration in the well-known N-body problem. The particle nodes correspond to regions of the cortex. The locations of particles are defined as the centers of the respective regions on the cortex and their masses are proportional to each region's volume. The force of attraction is modeled on the gravitational force, and explicitly made proportional to the elements of a connectivity matrix derived from diffusion imaging data. We present experimental results of the simulation on a population of 110 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of healthy elderly controls, early mild cognitively impaired (eMCI), late MCI (LMCI), and Alzheimer's disease (AD) patients. Results show significant differences in the dynamic properties of connectivity networks in healthy controls, compared to eMCI as well as AD patients.

Alzheimer’s Disease (AD) has long been considered a cortical degenerative disease, but impaired brain connectivity, due to white matter injury, may exacerbate cognitive problems. Predicting brain changes is critically important for early treatment. In a longitudinal diffusion tensor imaging study, we investigated white matter fiber integrity in 19 patients (mean age: 74.7 +/− 8.4 yrs at baseline) displaying early signs of mild cognitive impairment (eMCI). We first examined whether baseline average fractional anisotropy (FA) measures in the corpus callosum (CC) predicted changes in white matter integrity over the following 6 months. We then examined whether “small world” architecture measures - calculated from baseline connectivity maps - predicted white matter changes over the next 6 months. While average CC FA measures at baseline were not associated with future changes in FA, network measures were a sensitive biomarker for predicting white matter changes during this critical time before AD strikes.

Alzheimer's disease (AD) is characterized both by cortical atrophy and disrupted connectivity, resulting in abnormal interactions between neural systems. Diffusion weighted imaging (DWI) and graph theory can be used to evaluate major brain networks, and detect signs of abnormal breakdown in network connectivity. In a longitudinal study using both DWI and standard MRI, we assessed baseline white matter connectivity patterns in 24 early mild cognitive impairment (eMCI) subjects (mean age: 74.5 +/- 8.3 yrs). Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global "small-world" architecture measures. We evaluated whether these network measures predicted future volumetric brain atrophy in eMCI subjects, who are at risk for developing AD, as determined by 3D Jacobian "expansion factor maps" between baseline and 6-month follow-up scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.