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

SNAPSHOT USA 2024: Year 6 of the Coordinated National Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Daniel J. Herrera; Camilla Price; Phuong‐Thao Nguyen‐Nu; Katrina Adamczyk; Peter D. Alexander; David Allen; Jesse M. Alston; Diego F. Alvarado‐Serrano; et al. (Global Ecology and Biogeography, 2026)
Motivation SNAPSHOT USA is an annual, multi‐contributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2024 comprises two .csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

A Conceptual Framework and Methods for Studying the Connectivity of Fishes - Jordanna N. Bergman; Jessica A. Robichaud; Jasper McCutcheon; Michael T. Booth; Brendan Campbell; Grace A. Casselberry; Cienna R. Cooper; Bronwyn M. Gillanders; Lucas P. Griffin; Edward Hale; Luc LaRochelle; Karen J. Murchie; et al. (Fish and Fisheries, 2026)
Connectivity is a multifaceted concept that has important implications for the management and conservation of marine and freshwater fishes. We developed a conceptual framework that encompasses multiple, interrelated categories of connectedness, including landscape (e.g., structural, functional) connectivity and ecological (e.g., trophic, genetic, demographic) connectivity, that together shape the flow of organisms, energy and information across ecosystems. We also synthesised six key methods that can be used to study connectivity of fishes: (1) telemetry, including satellite, acoustic, radio and passive integrated transponders (PIT), (2) mark‐recapture, (3) environmental tracers, including stable isotopes and otolith‐microchemistry, (4) genetics, (5) community structure analysis and (6) emerging technologies and tools (e.g., remote sensing and artificial intelligence). For each method, we describe the categories of connectivity it can assess and provide real‐world examples where they have been effectively used. We also identify limitations of each method. This article highlights the diverse and evolving toolbox of methods used to assess fish connectivity, underscoring the need for continued collaboration, innovation and integration of new approaches to refine our understanding and address remaining challenges in this critical area of aquatic ecology and fisheries management.

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.

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.

The impact of gastrointestinal parasitism on the behaviour and welfare of weaned housed lambs - Michelle C. Reeves; Naomi Booth; Naomi J. Fox; Jo Donbavand; Mhairi Jack; Fiona Kenyon; Jessica E. Martin; Emma M. Baxter; Cathy M. Dwyer (Applied Animal Behaviour Science, 2024)

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)

A pathogenic deletion in Forkhead Box L1 (FOXL1) identifies the first otosclerosis (OTSC) gene - Nelly Abdelfatah; Ahmed A. Mostafa; Curtis R. French; Lance P. Doucette; Cindy Penney; Matthew B. Lucas; Anne Griffin; Valerie Booth; Christopher Rowley; Jessica E. Besaw; Lisbeth Tranebjærg; Nanna Dahl Rendtorff; et al. (Human Genetics, 2022)
Otosclerosis is a bone disorder of the otic capsule and common form of late-onset hearing impairment. Considered a complex disease, little is known about its pathogenesis. Over the past 20 years, ten autosomal dominant loci ( OTSC1-10 ) have been mapped but no genes identified. Herein, we map a new OTSC locus to a 9.96 Mb region within the FOX gene cluster on 16q24.1 and identify a 15 bp coding deletion in Forkhead Box L1 co-segregating with otosclerosis in a Caucasian family. Pre-operative phenotype ranges from moderate to severe hearing loss to profound sensorineural loss requiring a cochlear implant. Mutant FOXL1 is both transcribed and translated and correctly locates to the cell nucleus. However, the deletion of 5 residues in the C-terminus of mutant FOXL1 causes a complete loss of transcriptional activity due to loss of secondary (alpha helix) structure. FOXL1 (rs764026385) was identified in a second unrelated case on a shared background. We conclude that FOXL1 (rs764026385) is pathogenic and causes autosomal dominant otosclerosis and propose a key inhibitory role for wildtype Foxl1 in bone remodelling in the otic capsule. New insights into the molecular pathology of otosclerosis from this study provide molecular targets for non-invasive therapeutic interventions.

Validation of an in vitro model of erbB2+ cancer cell redirection - Jang Pyo Park; Walker M. Blanding; Jessica A. Feltracco; Brian W. Booth (In Vitro Cellular & Developmental Biology - Animal, 2015)

Lithium carbonate in the management of cannabis withdrawal: a randomized placebo-controlled trial in an inpatient setting - Jennifer Johnston; Nicholas Lintzeris; David J. Allsop; Anastasia Suraev; Jessica Booth; Dean S. Carson; David Helliwell; Adam Winstock; Iain S. McGregor (Psychopharmacology, 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)