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.

Vulnerability of marine megafauna to global at‐sea anthropogenic threats - Michelle VanCompernolle; Juliet Morris; Hannah J. Calich; Jorge P. Rodríguez; Sarah A. Marley; Jessica R. Pearce; Briana Abrahms; Katya Abrantes; André S. Afonso; Alex Aguilar; Andrews Agyekumhene; Tomonari Akamatsu; et al. (Conservation Biology, 2026)
Marine megafauna species are affected by a wide range of anthropogenic threats. To evaluate the risk of such threats, species’ vulnerability to each threat must first be determined. We build on the existing threats classification scheme and ranking system of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species by assessing the vulnerability of 256 marine megafauna species to 23 at‐sea threats. The threats we considered included individual fishing gear types, climate‐change‐related subthreats not previously assessed, and threats associated with coastal impacts and maritime disturbances. Our ratings resulted in 70 species having high vulnerability ( v > 0.778 out of 1) to at least 1 threat, primarily drifting longlines, temperature extremes, or fixed gear. These 3 threats were also considered to have the most severe effects (i.e., steepest population declines). Overall, temperature extremes and plastics and other solid waste were rated as affecting the largest proportion of populations. Penguins, pinnipeds, and polar bears had the highest vulnerability to temperature extremes. Bony fishes had the highest vulnerability to drifting longlines and plastics and other solid waste; pelagic cetaceans to 4 maritime disturbance threats; elasmobranchs to 5 fishing threats; and flying birds to drifting longlines and 2 maritime disturbance threats. Sirenians and turtles had the highest vulnerability to at least one threat from all 4 categories. Despite not necessarily having severe effects for most taxonomic groups, temperature extremes were rated among the top threats for all taxa except bony fishes. The vulnerability scores we provide are an important first step in estimating the risk of threats to marine megafauna. Importantly, they help differentiate scope from severity, which is key to identifying threats that should be prioritized for mitigation.

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.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Individual specialization in a migratory grazer reflects long-term diet selectivity on a foraging ground: implications for isotope-based tracking - Jordan A. Thomson; Elizabeth R. Whitman; Maria I. Garcia-Rojas; Alecia Bellgrove; Merrick Ekins; Graeme C. Hays; Michael R. Heithaus (Oecologia, 2018)

Environmental evidence for a pygmy blue whale aggregation area in the Subtropical Convergence Zone south of Australia - Maria I. Garcia‐Rojas; K. Curt S. Jenner; Peter C. Gill; Micheline‐Nicole M. Jenner; Alicia L. Sutton; Robert D. McCauley (Marine Mammal Science, 2018)
The importance of the Subtropical Convergence Zone south of Australia as a feeding area to the pygmy blue whale of the eastern Indian Ocean was investigated to better understand migration patterns and habitat use. A combination of satellite tagging studies during 2003–2005 and acoustic and visual surveys during the austral summer of 2013 confirmed the presence of pygmy blue whales in the Subtropical Convergence Zone and provided evidence for feeding throughout the region. Of the 67 pygmy blue whales acoustically detected, many were localized in proximity to sea surface temperature fronts and high chlorophyll concentrations, which could be a behavioral strategy to attract conspecifics to good feeding sites. Aggregations of up to four pygmy blue whales, along with decreased call rates during the day, may suggest feeding despite no direct observations of pygmy blue whales consuming euphausiids. The oceanographic features present throughout the Subtropical Convergence Zone continue to create an environment productive enough to support the world's largest living mammals during their migratory foraging movements between the Southern Ocean and Indonesia.