Search

The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

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.

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.

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.

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

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) 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 WM 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 AD: the AD 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 regression 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 (CGH) and uncinate fasciculus (UNC) 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.

PurposeIn 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.MethodsWe 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.ResultsAcross 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.ConclusionThe 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.

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.

BackgroundThere are contradicting reports on the associations between Apolipoprotein E4 (ApoE ε4) and brain outcomes in HIV with some evidence that relationships may be greatest in older age groups.MethodsWe 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.ResultsThe median age of the participants was 64 years (range: 60-84) and the median estimated duration of HIV infection was 22 years. Apo ε4 carriers (n = 19) were similar to noncarriers (n = 57) in sex (95% vs. 96% male), and education (16.0 vs. 16.2 years) ApoE ε4 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 ε4 carriers compared with ε4 noncarriers.ConclusionsIn this sample of older HIV-infected individuals, having at least 1 ApoE ε4 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.

BackgroundPerinatal 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.MethodsWe 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.ResultsMean (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.ConclusionsNo 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.

Our understanding of network breakdown in Alzheimer's disease (AD) is likely to be enhanced through advanced mathematical descriptors. Here, we applied spectral graph theory to provide novel metrics of structural connectivity based on 3-Tesla diffusion weighted images in 42 AD patients and 50 healthy controls. We reconstructed connectivity networks using whole-brain tractography and examined, for the first time here, cortical disconnection based on the graph energy and spectrum. We further assessed supporting metrics - link density and nodal strength - to better interpret our results. Metrics were analyzed in relation to the well-known APOE-4 genetic risk factor for late-onset AD. The number of disconnected cortical regions increased with the number of copies of the APOE-4 risk gene in people with AD. Each additional copy of the APOE-4 risk gene may lead to more dysfunctional networks with weakened or abnormal connections, providing evidence for the previously hypothesized "disconnection syndrome".

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.

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.

Alzheimer's disease (AD) is characterized by cortical atrophy and disrupted anatomic connectivity, and leads to abnormal interactions between neural systems. Diffusion-weighted imaging (DWI) and graph theory can be used to evaluate major brain networks and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard magnetic resonance imaging (MRI), we assessed baseline white-matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI, mean age 71.8 ± 7.5 years, 18 males and 12 females) from the Alzheimer's Disease Neuroimaging Initiative. 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, including mean clustering coefficient and characteristic path length. We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3-dimensional Jacobian "expansion factor maps" between baseline and 6-month follow-up anatomic scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.

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.

We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer's disease (AD) patients from controls. We studied diffusion-weighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. 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 3-dimensional lattice. Based on these 2 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, 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 mild cognitive impairment or AD.

Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease. We report significant associations between higher serum cholesterol (CHOL) and high-density lipoprotein levels and higher fractional anisotropy in 403 young adults (23.8 ± 2.4 years) scanned with diffusion imaging and anatomic magnetic resonance imaging at 4 Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related, single-nucleotide polymorphisms implicated in Alzheimer's disease risk predicted fractional anisotropy. We focused on the single-nucleotide polymorphism with the largest individual effects, CETP (rs5882), and found that increased G-allele dosage was associated with higher fractional anisotropy and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected white matter associations with rs5882 in the opposite direction in 78 older individuals (74.3 ± 7.3 years). Cholesterol levels may influence white matter integrity, and cholesterol-related genes may exert age-dependent effects on the brain.

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.

Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone--the so-called "k-core"--of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.

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.

Antiretroviral therapies have become widely available, and as a result, individuals infected with the human immunodeficiency virus (HIV) are living longer, and becoming integrated into the geriatric population. Around half of the HIV+ population shows some degree of cognitive impairment, but it is unknown how their neural networks and brain connectivity compare to those of noninfected people. Here we combined magnetic resonance imaging-based cortical parcellations with high angular resolution diffusion tensor imaging tractography in 55 HIV-seropositive patients and 30 age-matched controls, to map white matter connections between cortical regions. We set out to determine selective virus-associated disruptions in the brain's structural network. All individuals in this study were aged 60-80, with full access to antiretroviral therapy. Frontal and motor connections were compromised in HIV+ individuals. HIV+ people who carried the apolipoprotein E4 allele (ApoE4) genotype-which puts them at even greater risk for neurodegeneration-showed additional network structure deficits in temporal and parietal connections. The ApoE4 genotype interacted with duration of illness. Carriers showed greater brain network inefficiencies the longer they were infected. Neural network deficiencies in HIV+ populations exceed those typical of normal aging, and are worse in those genetically predisposed to brain degeneration. This work isolates neuropathological alterations in HIV+ elders, even when treated with antiretroviral therapy. Network impairments may contribute to the neuropsychological abnormalities in elderly HIV patients, who will soon account for around half of all HIV+ adults.