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.

Plasticity and regional heterothermy of upper thermal tolerance in the ring-necked snake - Christian L. Cox; Kelly Lin Wuthrich; Dareen Aloudeh; Samuel J. Baker; Anna G. Climo; Alexa F. Gross; Christian W. Kizer; Zoe M. Korff; Oscar Melendez Vera; Veronica Y. Silva; Alexis M. Spans; Grace R. Thilenius; et al. (Journal of Thermal Biology, 2026)

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

Changes in plasma concentrations of per- and Polyfluoroalkyl substances after bariatric surgery in adolescents from the Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study - Brittney O. Baumert; Sandrah P. Eckel; Jesse A. Goodrich; Zhenjiang Li; Nikos Stratakis; Douglas I. Walker; Yinqi Zhao; Fabian Christoph Fischer; Scott Bartell; Damaskini Valvi; Xiangping Lin; Zoe Coates Fuentes; et al. (Science of The Total Environment, 2024)

Macroalgae exhibit diverse responses to human disturbances on coral reefs - Sara E. Cannon; Simon D. Donner; Angela Liu; Pedro C. González Espinosa; Andrew H. Baird; Julia K. Baum; Andrew G. Bauman; Maria Beger; Cassandra E. Benkwitt; Matthew J. Birt; Yannick Chancerelle; Joshua E. Cinner; et al. (Global Change Biology, 2023)
Scientists and managers rely on indicator taxa such as coral and macroalgal cover to evaluate the effects of human disturbance on coral reefs, often assuming a universally positive relationship between local human disturbance and macroalgae. Despite evidence that macroalgae respond to local stressors in diverse ways, there have been few efforts to evaluate relationships between specific macroalgae taxa and local human‐driven disturbance. Using genus‐level monitoring data from 1205 sites in the Indian and Pacific Oceans, we assess whether macroalgae percent cover correlates with local human disturbance while accounting for factors that could obscure or confound relationships. Assessing macroalgae at genus level revealed that no genera were positively correlated with all human disturbance metrics. Instead, we found relationships between the division or genera of algae and specific human disturbances that were not detectable when pooling taxa into a single functional category, which is common to many analyses. The convention to use percent cover of macroalgae as an indication of local human disturbance therefore likely obscures signatures of local anthropogenic threats to reefs. Our limited understanding of relationships between human disturbance, macroalgae taxa, and their responses to human disturbances impedes the ability to diagnose and respond appropriately to these threats.

Phylogeography of Blue Corals (Genus Heliopora) Across the Indo-West Pacific - Hiroki Taninaka; Davide Maggioni; Davide Seveso; Danwei Huang; Abram Townsend; Zoe T. Richards; Sen-Lin Tang; Naohisa Wada; Taisei Kikuchi; Hideaki Yuasa; Megumi Kanai; Stéphane De Palmas; et al. (Frontiers in Marine Science, 2021)
Species delimitation of corals is one of the most challenging issues in coral reef ecology and conservation. Morphology can obscure evolutionary relationships, and molecular datasets are consistently revealing greater within-species diversity than currently understood. Most phylogenetic studies, however, have examined narrow geographic areas and phylogeographic expansion is required to obtain more robust interpretations of within- and among- species relationships. In the case of the blue coral Heliopora , there are currently two valid species ( H. coerulea and H. hiberniana ) as evidenced by integrated genetic and morphological analyses in northwestern Australia. There are also two distinct genetic lineages of H. coerulea in the Kuroshio Current region that are morphologically and reproductively different from each other. Sampling from all Heliopora spp. across the Indo-Pacific is essential to obtain a more complete picture of phylogeographic patterns. To examine phylogenetic relationships within the genus Heliopora , we applied Multiplexed inter simple sequence repeat (ISSR) Genotyping by sequencing (MIG-seq) on > 1287 colonies across the Indo-West Pacific. Maximum likelihood phylogenetic trees indicated the examined Heliopora samples comprise three genetically distinct groups: H. coerulea group, H. hiberniana group, and a new undescribed Heliopora sp. group with further subdivisions within each group. Geographic structuring is evident among the three species with H. hiberniana group found in the Indo-Malay Archipelago and biased toward the Indian Ocean whilst Heliopora sp. was only found in the Kuroshio Current region and Singapore, indicating that this taxon is distributed in the western Pacific and the Indo-Malay Archipelago. Heliopora coerulea has a wider distribution, being across the Indian Ocean and western Pacific. This study highlights the effectiveness of phylogenetic analysis using genome-wide markers and the importance of examining populations across their distribution range to understand localized genetic structure and speciation patterns of corals.