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As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3–96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease. •Multi-site harmonization method that pools volumetric data from 18 studies, controlling for nonlinear age effects.•Resulting dataset covers ages 3 to 96 and used to derive age trends of brain structure through the lifespan.•Interactive visualization tool provided for exploring age trends and comparing new data.

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN – a multi-view, weakly-supervised deep clustering method – which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer’s disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes. Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

•Multiscale functional connectivity pattern of the aging brain were learned from a large-scale multisite fMRI datasets.•A machine learning model built on the multiscale functional connectivity measures achieved accurate brain age prediction.•Functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction.•Data harmonization significantly improved the brain age prediction performance. To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

Purpose Hypertension is a risk factor for cognitive impairment; however, the mechanisms leading to cognitive changes remain unclear. In this cross-sectional study, we evaluate the impact of white matter lesion (WML) burden on brain functional connectivity (FC) and cognition in a large cohort of hypertensive patients from the Systolic Blood Pressure Intervention Trial (SPRINT) at baseline. Methods Functional networks were identified from baseline resting state functional MRI scans of 660 SPRINT participants using independent component analysis. WML volumes were calculated from structural MRI. Correlation analyses were carried out between mean FC of each functional network and global WML as well as WML within atlas-defined white matter regions. For networks of interest, voxel-wise-adjusted correlation analyses between FC and regional WML volume were performed. Multiple variable linear regression models were built for cognitive test performance as a function of network FC, followed by mediation analysis. Results Mean FC of the default mode network (DMN) was negatively correlated with global WML volume, and regional WML volume within the precuneus. Voxel-wise correlation analyses revealed that regional WML was negatively correlated with FC of the DMN’s left lateral temporal region. FC in this region of the DMN was positively correlated to performance on the Montreal Cognitive Assessment and demonstrated significant mediation effects. Additional networks also demonstrated global and regional WML correlations; however, they did not demonstrate an association with cognition. Conclusion In hypertensive patients, greater WML volume is associated with lower FC of the DMN, which in turn is related to poorer cognitive test performance. Trial registration NCT01206062

Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread brain atrophy, and the “MTL-AD” (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4 ) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were “druggable genes” for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4 , amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction—driven by genes different from APOE —which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

Background Spread of tau tangles, one of the hallmarks of Alzheimer’s Disease (AD) is complex and heterogenous. While many studies focus on stereotypical patterns of tau, there are many individuals who deviates from Braak staging (ex.atypical tau). Understanding heterogeneity of tau spread and association with other factors has important implications in clinical trials. In this analysis, we derived four distinct patterns of tau using a data driven, deep learning‐based clustering method, Surreal‐GAN. Method We used data from 1445 (Amyloid+:792, Amyloid‐:653) subjects with 18F‐flortaucipir PET from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; N = 803), HABS; N = 195 and A4; N = 447. We used study‐defined cut‐offs for deriving amyloid status for each study separately. In this analysis, we utilized tau regional standardized uptake value ratios(SUVR) calculated for each of the aparc+aseg FreeSurfer labels. We then applied Surreal‐GAN on the regional tau SUVR for Amyloid + subjects with Amyloid ‐ as reference group to determine the Tau spatiotemporal subtypes. We tested the associations between r‐indices and cognitive scores. Result Participants had a mean age of 71.8 (±4.8); 73.5(±6.2); 73.8(±7.6) years for A4, HABS and ADNI respectively. Surreal‐GAN showed optimal agreement indices for four patterns. Pattern r1 (r.occipital) shows the involvement of cuneus, lingual gyrus; pattern r2 (r.frontotemporal) shows frontal and temporal; pattern r3 (r.parietal) shows precuneus and inferior parietal and pattern r4 (r.typical) shows typical AD regions. Pattern r.occiptal was associated with language area of cognitive function(MMSE language domain); We observed significant associations of r.typical with composite memory scores in ADNI. Conclusion Employing a data‐driven deep learning‐based approach, we derived more than one dissociable pattern of tau deposition. These findings could inform identification of subgroups of individuals who may or may not be responsive to particular clinical intervention.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis. Assessing brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies, a generative model identifies five dominant patterns of brain atrophy, with specific associations with biomedical, lifestyle and genetic factors.

Background Hypertension (HTN) is an established risk factor for neurodegeneration and dementia, supported by epidemiological and neuroimaging studies, although there is large variation in individual outcomes. We developed a machine learning (ML)‐based model, termed SPARE‐HTN, to quantify the spatial pattern of HTN‐related neurodegeneration observable in individual structural magnetic resonance images (sMRI). SPARE‐HTN demonstrates superior sensitivity compared to the most widely‐used measure of HTN‐related brain changes, correlates with cognitive performance, and detects early changes in mid‐life years. This study investigated the predictive capacity of SPARE‐HTN for incident HTN and its mediating role in the relationship between HTN and cognition. Methods SPARE‐HTN, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 968 (59% female, mean age 63.0 ± 11 years) individuals with longitudinal clinical data from six studies (Table 1). Baseline SPARE‐HTN values were compared across participants categorized by longitudinal HTN status: persistently normotensive, persistently hypertensive, or incident HTN. The risk of incident HTN was evaluated among baseline normotensive participants using Cox regression model across the baseline SPARE‐HTN quartiles, adjusted for age and sex. The average causal mediated effect (ACME) of SPARE‐HTN and total white matter hyperintensity (WMH) volume on the relationship between hypertension and cognitive test scores were assessed using mediation models with 5000 simulated permutations. Results Table 1 presents the participant characteristics at baseline, stratified by longitudinal HTN status. SPARE‐HTN was significantly elevated (Figure 1A) in participants who were normotensive at baseline but developed HTN within 3‐7 years (+0.44, p = 0.01), but not in those who developed HTN in 8‐12 years (p = 0.65). Normotensive participants with elevated baseline SPARE‐HTN scores exhibited significantly higher Cox proportional hazard ratios for HTN incidence (Figure 1B). Mediation analysis demonstrated that SPARE‐HTN mediated up to 26% of the effect of HTN on cognitive measures, whilst the conventional WMH volumes showed little mediation effect (Figure 2). Conclusion Our ML‐based sMRI marker predicted incident HTN prior to formal clinical diagnosis, suggesting the presence of subclinical cerebrovascular changes possibly associated with blood pressure variations. These markers, particularly relevant in midlife, offer potential for informing dementia prevention trials by enabling individualized risk stratification and potentially more sensitive measurement of therapeutic efficacy.