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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.

Disrupted iron homeostasis is associated with several neurodegenerative diseases, including Alzheimer's disease (AD), and may be partially modulated by genetic risk factors. Here we evaluated whether subcortical iron deposition is associated with ApoE genotype, which substantially affects risk for late-onset AD. We evaluated differences in subcortical quantitative susceptibility mapping (QSM), a type of MRI sensitive to cerebral iron deposition, between either ApoE4 (E3E4+E4E4) or ApoE2 (E2E3+E2E2) carriers and E3 homozygotes (E3E3) in 27,535 participants from the UK Biobank (age: 45-82 years). We found that ApoE4 carriers had higher hippocampal (d=0.036; p=0.012) and amygdalar (d=0.035; p=0.013) magnetic susceptibility, particularly individuals aged 65 years or older, while those carrying ApoE2 (which protects against AD) had higher QSM only in the hippocampus (d=0.05; p=0.006), particularly those under age 65. Secondary diffusion MRI microstructural associations in these regions revealed greater diffusivity and less diffusion restriction in E4 carriers, however no differences were detected in E2 carriers. Disease risk conferred by ApoE4 may be linked with higher subcortical iron burden in conjunction with inflammation or neuronal loss in aging individuals, while ApoE2 associations may not necessarily reflect unhealthy iron deposits earlier in life.

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.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intracellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extracellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.

22q11.2 deletion syndrome (22q11DS)—a neurodevelopmental condition caused by a hemizygous deletion on chromosome 22—is associated with an elevated risk of psychosis and other developmental brain disorders. Prior single-site diffusion magnetic resonance imaging (dMRI) studies have reported altered white matter (WM) microstructure in 22q11DS, but small samples and variable methods have led to contradictory results. Here we present the largest study ever conducted of dMRI-derived measures of WM microstructure in 22q11DS (334 22q11.2 deletion carriers and 260 healthy age- and sex-matched controls; age range 6–52 years). Using harmonization protocols developed by the ENIGMA-DTI working group, we identified widespread reductions in mean, axial and radial diffusivities in 22q11DS, most pronounced in regions with major cortico-cortical and cortico-thalamic fibers: the corona radiata, corpus callosum, superior longitudinal fasciculus, posterior thalamic radiations, and sagittal stratum (Cohen’s d’s ranging from −0.9 to −1.3). Only the posterior limb of the internal capsule (IC), comprised primarily of corticofugal fibers, showed higher axial diffusivity in 22q11DS. 22q11DS patients showed higher mean fractional anisotropy (FA) in callosal and projection fibers (IC and corona radiata) relative to controls, but lower FA than controls in regions with predominantly association fibers. Psychotic illness in 22q11DS was associated with more substantial diffusivity reductions in multiple regions. Overall, these findings indicate large effects of the 22q11.2 deletion on WM microstructure, especially in major cortico-cortical connections. Taken together with findings from animal models, this pattern of abnormalities may reflect disrupted neurogenesis of projection neurons in outer cortical layers. Sin financiación 15.992 JCR (2020) Q1, 10/295 Biochemistry & Molecular Biology 5.071 SJR (2020) Q1, 5/85 Cellular and Molecular Neuroscience No data IDR 2020 UEM

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.

Our aim was to examine the clinical relevance of white matter hyperintensities (WMH) in HIV. We used an automated approach to quantify WMH volume in HIV seropositive (HIV+; n = 65) and HIV seronegative (HIV−; n = 29) adults over age 60. We compared WMH volumes between HIV+ and HIV− groups in cross-sectional and multiple time-point analyses. We also assessed correlations between WMH volumes and cardiovascular, HIV severity, cognitive scores, and diffusion tensor imaging variables. Serostatus groups did not differ in WMH volume, but HIV+ participants had less cerebral white matter (mean: 470.95 [43.24] vs. 497.63 [49.42] mL, p = 0.010). The distribution of WMH volume was skewed in HIV+ with a high proportion (23%) falling above the 95th percentile of WMH volume defined by the HIV− group. Serostatus groups had similar amount of WMH volume growth over time. Total WMH volume directly correlated with measures of hypertension and inversely correlated with measures of global cognition, particularly in executive functioning, and psychomotor speed. Greater WMH volume was associated with poorer brain integrity measured from diffusion tensor imaging (DTI) in the corpus callosum and sagittal stratum. In this group of HIV+ individuals over 60, WMH burden was associated with cardiovascular risk and both worse diffusion MRI and cognition. The median total burden did not differ by serostatus; however, a subset of HIV+ individuals had high WMH burden.

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.

Advances in diffusion MRI (dMRI) have led to discoveries of factors that affect brain microstructure and connectivity in health and disease. The small size of many neuroimaging studies led to concerns about poor reproducibility of research findings, and calls for the comparison and pooling of multi-cohort datasets to establish the consistency of reported effects. Across studies diffusion MRI protocols vary in spatial, angular and q-space resolution, b-value, as well as hardware used—all of which affect measured diffusion parameters. Efforts to compare and pool dMRI measures use meta- or mega- analytical techniques to compensate for these sources of variance. Meta-analytical methods gauge the consistency of effects, and mega-analytical methods involve mathematical or statistical transformations of the data. Here, we review some recent advances that allowed the diffusion community to create large scale population studies with greater rigor and generalizability than was previously attainable by individual studies.

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 MRI (dMRI) is widely used to study the brain’s white matter (WM) microstructure in a range of psychiatric and neurological diseases. As the diffusion tensor model has limitations in brain regions with crossing fibers, novel diffusion MRI reconstruction models may offer more accurate measures of tissue properties, and a better understanding of the brain abnormalities in specific diseases. Here we studied a large sample of 249 participants with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with high rates of developmental neuropsychiatric disorders, and 224 age-matched healthy controls (HC) (age range: 8-35 years). Participants were scanned with dMRI at eight centers worldwide. Using a meta-analytic approach, we assessed the profile of group differences in four diffusion anisotropy measures to better understand the patterns of WM microstructural abnormalities and evaluate their consistency across alternative measures. When assessed in atlas-defined regions of interest, we found statistically significant differences for all anisotropy measures, all showing a widespread but not always coinciding pattern of effects. The tensor distribution function fractional anisotropy (TDF-FA) showed largest effect sizes all in the same direction (greater anisotropy in 22q11DS than HC). Fractional anisotropy based on the tensor model (FA) showed the second largest effect sizes after TDF-FA; some regions showed higher mean values in 22q11DS, but others lower. Generalized fractional anisotropy (GFA) showed the opposite pattern to TDF-FA with most regions showing lower anisotropy in 22q11DS versus HC. Anisotropic power maps (AP) showed the lowest effect sizes also with a mixed pattern of effects across regions. These results were also consistent across skeleton projection methods, with few differences when projecting anisotropy values from voxels sampled on the FA map or projecting values from voxels sampled from each anisotropy map. This study highlights that different mathematical definitions of anisotropy may lead to different profiles of group differences, even in large, well-powered population studies. Further studies of biophysical models derived from multi-shell dMRI and histological validations may help to understand the sources of these differences. 22q11DS is a promising model to study differences among novel anisotropy/dMRI measures, as group differences are relatively large and there exist animal models suitable for histological validation.

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.

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3-Tesla whole-brain diffusion-weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative-50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole-brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the "rich club" - a network property where high-degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low-degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step-wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline.

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.

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

Neurodegenerative disorders are characterized by a progressive loss of brain function. Improved precision in mapping the altered brain pathways can provide a deep understanding of the trajectory of decline. We propose a tractometry workflow for conducting group statistical analyses of point-wise microstructural measures along white matter fasciculi to identify patterns of abnormalities associated with disease. We combined state-of-the-art tools including fiber registration, tract simplification and fiber matching for accurate point-wise statistical analyses across populations. We test the utility of this method by identifying group differences between Parkinson’s disease (PD) patients and healthy controls. We find statistically significant group differences in diffusion MRI derived measures along the anterior thalamic radiations (ATR), corticospinal tract (CST) and regions of the corpus callosum (CC). These pathways are essential for motor control systems within cortico-cortical and cortico-subcortical brain networks. Moreover, the reported pathological changes were not widespread but rather localized along several tracts. Point-wise tract analyses may therefore offer an advantage in anatomical specificity over traditional methods that assess mean microstructural measures across large regions of interest.

Purpose In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. Methods We compared the ability of standard FADTI and TDF-derived FA (FATDF), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume. Results Across angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI, particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies. Conclusion The TDF “corrected” form of FA may be a more sensitive and accurate alternative to the commonly used FADTI, even in clinical quality dMRI data. Magn Reson Med 78:2322–2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Susceptibility genes for psychiatric and neurological disorders - including APOE, BDNF, CLU,CNTNAP2, COMT, DISC1, DTNBP1, ErbB4, HFE, NRG1, NTKR3, and ZNF804A - have been reported to affect white matter (WM) microstructure in the healthy human brain, as assessed through diffusion tensor imaging (DTI). However, effects of single nucleotide polymorphisms (SNPs) in these genes explain only a small fraction of the overall variance and are challenging to detect reliably in single cohort studies. To date, few studies have evaluated the reproducibility of these results. As part of the ENIGMA-DTI consortium, we pooled regional fractional anisotropy (FA) measures for 6,165 subjects (CEU ancestry N=4,458) from 11 cohorts worldwide to evaluate effects of 15 candidate SNPs by examining their associations with WM microstructure. Additive association tests were conducted for each SNP. We used several meta-analytic and mega-analytic designs, and we evaluated regions of interest at multiple granularity levels. The ENIGMA-DTI protocol was able to detect single-cohort findings as originally reported. Even so, in this very large sample, no significant associations remained after multiple-testing correction for the 15 SNPs investigated. Suggestive associations (1.3×10-4 < p < 0.05, uncorrected) were found for BDNF, COMT, and ZNF804A in specific tracts. Meta-and mega-analyses revealed similar findings. Regardless of the approach, the previously reported candidate SNPs did not show significant associations with WM microstructure in this largest genetic study of DTI to date; the negative findings are likely not due to insufficient power. Genome-wide studies, involving large-scale meta-analyses, may help to discover SNPs robustly influencing WM microstructure.

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.

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. Models such as the tensor distribution function (TDF), which represents the diffusion profile as a probabilistic mixture of tensors, have been proposed to reconstruct multiple underlying fibers. Although complex HARDI acquisition protocols are rare in clinical studies, the TDF and TDF-derived scalar FA metric (FATDF) have been shown to be advantageous even for data with modest angular resolution. However, further evaluation and validation of the metric are necessary. Here we compared the test-retest reliability of FATDF and FADTI in clinical quality data by computing the intra-class correlation (ICC) between dMRI scans collected 3 months apart. When FATDF and FADTI were calculated at various angular resolutions, FATDF ICC in both the corpus callosum and in a full axial slice were consistently more stable across scans, as compared to FADTI.

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.

People with HIV are living longer as combination antiretroviral therapy (cART) becomes more widely available. However, even when plasma viral load is reduced to untraceable levels, chronic HIV infection is associated with neurological deficits and brain atrophy beyond that of normal aging. HIV is often marked by cortical and subcortical atrophy, but the integrity of the brain's white matter (WM) pathways also progressively declines. Few studies focus on older cohorts where normal aging may be compounded with HIV infection to influence deficit patterns. In this relatively large diffusion tensor imaging (DTI) study, we investigated abnormalities in WM fiber integrity in 56 HIV+ adults with access to cART (mean age: 63.9 ± 3.7 years), compared to 31 matched healthy controls (65.4 ± 2.2 years). Statistical 3D maps revealed the independent effects of HIV diagnosis and age on fractional anisotropy (FA) and diffusivity, but we did not find any evidence for an age by diagnosis interaction in our current sample. Compared to healthy controls, HIV patients showed pervasive FA decreases and diffusivity increases throughout WM. We also assessed neuropsychological (NP) summary z-score associations. In both patients and controls, fiber integrity measures were associated with NP summary scores. The greatest differences were detected in the corpus callosum and in the projection fibers of the corona radiata. These deficits are consistent with published NP deficits and cortical atrophy patterns in elderly people with HIV.

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.

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.