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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.

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).

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model—combining genotype, educational level, and clinical diagnosis.

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.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores., Highlights • DTI scans in ADNI2 provide numerous biomarkers of Alzheimer's disease. • FA, MD, AxD, and RD measures all detect MCI and AD white matter deficits. • DTI FA and diffusivity measures are correlated with clinical cognitive scores. • FA is the least sensitive DTI measure for detecting AD related differences. • WM in the temporal lobe, corpus callosum and cingulum is repeatedly implicated.