Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Do associations of neighbourhood attributes with cognitive function vary by socio-economic status? A 12-year follow-up study of older Australians - Ester Cerin; Maria V. Soloveva; Miguel A. Molina; Ralf-Dieter Schroers; Yu-Tzu Wu; Perminder S. Sachdev; Luke D. Knibbs; Nicolas Cherbuin; Bin Jalaludin; Suzanne Mavoa; Kaarin J. Anstey (Science of The Total Environment, 2025)

Brain structural covariances in the ageing brain in the UK Biobank - Chao Dong; Anbupalam Thalamuthu; Jiyang Jiang; Karen A. Mather; Perminder S. Sachdev; Wei Wen (Brain Structure and Function, 2024)
The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank ( N = 42,075, aged 45–83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations.

High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile - Mary Revelas; Anbupalam Thalamuthu; Anna Zettergren; Christopher Oldmeadow; Jenna Najar; Nazib M. Seidu; Nicola J. Armstrong; Carlos Riveros; John B. Kwok; Peter R. Schofield; Julian N. Trollor; Margda Waern; et al. (GeroScience, 2023)
Healthy metabolic measures in humans are associated with longevity. Dysregulation leads to metabolic syndrome (MetS) and negative health outcomes. Recent exceptional longevity (EL) genome wide association studies have facilitated estimation of an individual’s polygenic risk score (PRS) for EL. We tested the hypothesis that individuals with high ELPRS have a low prevalence of MetS. Participants were from five cohorts of middle-aged to older adults. The primary analyses were performed in the UK Biobank (UKBB) ( n = 407,800, 40–69 years). Replication analyses were undertaken using three Australian studies: Hunter Community Study ( n = 2122, 55–85 years), Older Australian Twins Study ( n = 539, 65–90 years) and Sydney Memory and Ageing Study ( n = 925, 70–90 years), as well as the Swedish Gothenburg H70 Birth Cohort Studies ( n = 2273, 70–93 years). MetS was defined using established criteria. Regressions and meta-analyses were performed with the ELPRS and MetS and its components. Generally, MetS prevalence (22–30%) was higher in the older cohorts. In the UKBB, high EL polygenic risk was associated with lower MetS prevalence (OR = 0.94, p = 1.84 × 10 –42 ) and its components ( p < 2.30 × 10 – 8 ). Meta-analyses of the replication cohorts showed nominal associations with MetS ( p = 0.028) and 3 MetS components ( p < 0.05). This work suggests individuals with a high polygenic risk for EL have a healthy metabolic profile promoting longevity.

Cognitive decline is associated with frequency-specific resting state functional changes in normal aging - Dongqiong Fan; Tao Liu; Jiyang Jiang; Nicole A. Kochan; Yilong Wang; Henry Brodaty; Perminder S. Sachdev; Wei Wen (Brain Imaging and Behavior, 2022)

Prediction of disability-free survival in healthy older people - Johannes Tobias Neumann; Le T. P. Thao; Anne M. Murray; Emily Callander; Prudence R. Carr; Mark R. Nelson; Rory Wolfe; Robyn L. Woods; Christopher M. Reid; Raj C. Shah; Anne B. Newman; Jeff D. Williamson; et al. (GeroScience, 2022)
Prolonging survival in good health is a fundamental societal goal. However, the leading determinants of disability-free survival in healthy older people have not been well established. Data from ASPREE, a bi-national placebo-controlled trial of aspirin with 4.7 years median follow-up, was analysed. At enrolment, participants were healthy and without prior cardiovascular events, dementia or persistent physical disability. Disability-free survival outcome was defined as absence of dementia, persistent disability or death. Selection of potential predictors from amongst 25 biomedical, psychosocial and lifestyle variables including recognized geriatric risk factors, utilizing a machine-learning approach. Separate models were developed for men and women. The selected predictors were evaluated in a multivariable Cox proportional hazards model and validated internally by bootstrapping. We included 19,114 Australian and US participants aged ≥65 years (median 74 years, IQR 71.6–77.7). Common predictors of a worse prognosis in both sexes included higher age, lower Modified Mini-Mental State Examination score, lower gait speed, lower grip strength and abnormal (low or elevated) body mass index. Additional risk factors for men included current smoking, and abnormal eGFR. In women, diabetes and depression were additional predictors. The biased-corrected areas under the receiver operating characteristic curves for the final prognostic models at 5 years were 0.72 for men and 0.75 for women. Final models showed good calibration between the observed and predicted risks. We developed a prediction model in which age, cognitive function and gait speed were the strongest predictors of disability-free survival in healthy older people. Trial registration Clinicaltrials.gov (NCT01038583)

The relationship of cerebral microbleeds to cognition and incident dementia in non-demented older individuals - Matt Paradise; Adam Seruga; John D. Crawford; Joga Chaganti; Anbupalam Thalamuthu; Nicole A. Kochan; Henry Brodaty; Wei Wen; Perminder S. Sachdev (Brain Imaging and Behavior, 2019)

The relationship between voxel-based metrics of resting state functional connectivity and cognitive performance in cognitively healthy elderly adults - Haobo Zhang; Perminder S. Sachdev; Anbupalam Thalamuthu; Yong He; Mingrui Xia; Nicole A. Kochan; John D. Crawford; Julian N. Trollor; Henry Brodaty; Wei Wen (Brain Imaging and Behavior, 2018)

Deep white matter hyperintensities, microstructural integrity and dual task walking in older people - Tabassom Ghanavati; Myriam Sillevis Smitt; Stephen R. Lord; Perminder Sachdev; Wei Wen; Nicole A. Kochan; Henry Brodaty; Kim Delbaere (Brain Imaging and Behavior, 2018)

Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex - Tulio Guadalupe; Samuel R. Mathias; Theo G. M. vanErp; Christopher D. Whelan; Marcel P. Zwiers; Yoshinari Abe; Lucija Abramovic; Ingrid Agartz; Ole A. Andreassen; Alejandro Arias-Vásquez; Benjamin S. Aribisala; Nicola J. Armstrong; et al. (Brain Imaging and Behavior, 2017)

Mitochondrial membrane potential (MMP) regulates sperm motility - Saurabh Kumar Agnihotri; Ankit Kumar Agrawal; Bilal Ahmad Hakim; A. L. Vishwakarma; T. Narender; Rekha Sachan; Monika Sachdev (In Vitro Cellular & Developmental Biology - Animal, 2016)

Characterization and Fungal Inhibition Activity of Siderophore from Wheat Rhizosphere Associated Acinetobacter calcoaceticus Strain HIRFA32 - D. V. Maindad; V. M. Kasture; H. Chaudhari; D. D. Dhavale; B. A. Chopade; D. P. Sachdev (Indian Journal of Microbiology, 2014)

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data - Paul M. Thompson; Jason L. Stein; Sarah E. Medland; Derrek P. Hibar; Alejandro Arias Vasquez; Miguel E. Renteria; Roberto Toro; Neda Jahanshad; Gunter Schumann; Barbara Franke; Margaret J. Wright; Nicholas G. Martin; et al. (Brain Imaging and Behavior, 2014)