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.

Correction: Land use policies, land use land cover change, and climate: a review with examples from the United States - Pedro Vertino De Queiroz; Rezaul Mahmood; Lilyan Fulginiti; Richard Perrin (Environment, Development and Sustainability, 2025)

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.

Land use policies, land use land cover change, and climate: a review with examples from the United States - Pedro Vertino de Queiroz; Rezaul Mahmood; Lilyan Fulginiti; Richard Perrin (Environment, Development and Sustainability, 2025)

Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning - Maryam Rahmani; Donna Dierker; Lauren Yaeger; Andrew Saykin; Patrick H. Luckett; Andrei G. Vlassenko; Christopher Owens; Hussain Jafri; Kyle Womack; Jurgen Fripp; Ying Xia; Duygu Tosun; et al. (Brain Imaging and Behavior, 2024)
This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.

Habitat fragmentation matters more than habitat loss: The case of host–parasite interactions - Antoine Perrin; Aurélie Khimoun; Anthony Ollivier; Yves Richard; Antón Pérez‐Rodríguez; Bruno Faivre; Stéphane Garnier (Molecular Ecology, 2023)
While ecologists agree that habitat loss has a substantial negative effect on biodiversity it is still very much a matter of debate whether habitat fragmentation has a lesser effect and whether this effect is positive or negative for biodiversity. Here, we assess the relative influence of tropical forest loss and fragmentation on the prevalence of vector‐borne blood parasites of the genera Plasmodium and Haemoproteus in six forest bird species. We also determine whether habitat loss and fragmentation are associated with a rise or fall in prevalence. We sample more than 4000 individual birds from 58 forest sites in Guadeloupe and Martinique. Considering 34 host–parasite combinations independently and a fine characterization of the amount and spatial configuration of habitat, we use partial least square regressions to disentangle the relative effects of forest loss, forest fragmentation, landscape heterogeneity, and local weather conditions on spatial variability of parasite prevalence. Then we test for the magnitude and the sign of the effect of each environmental descriptor. Strikingly, we show that forest fragmentation explains twice as much of the variance in prevalence as habitat loss or landscape heterogeneity. In addition, habitat fragmentation leads to an overall rise in prevalence in Guadeloupe, but its effect is variable in Martinique. Both habitat loss and landscape heterogeneity exhibit taxon‐specific effects. Our results suggest that habitat loss and fragmentation may have contrasting effects between tropical and temperate regions and that inter‐specific interactions may not respond in the same way as more commonly used biodiversity metrics such as abundance and diversity.

α-Synuclein Over-Expression Induces Increased Iron Accumulation and Redistribution in Iron-Exposed Neurons - Richard Ortega; Asuncion Carmona; Stéphane Roudeau; Laura Perrin; Tanja Dučić; Eleonora Carboni; Sylvain Bohic; Peter Cloetens; Paul Lingor (Molecular Neurobiology, 2016)

The Curvature Induction of Surface-Bound Antimicrobial Peptides Piscidin 1 and Piscidin 3 Varies with Lipid Chain Length - B. Scott Perrin; Alexander J. Sodt; Myriam L. Cotten; Richard W. Pastor (The Journal of Membrane Biology, 2015)

Tumefactive demyelination in a patient with human immunodeficiency virus - Isaac H. Solomon; Richard J. Perrin; David B. Clifford; Beau M. Ances (Journal of NeuroVirology, 2013)

Farm-Scale Production Cost of Switchgrass for Biomass - Richard Perrin; Kenneth Vogel; Marty Schmer; Rob Mitchell (BioEnergy Research, 2008)

GENETIC ANALYSIS OF KILLER WHALE ( ORCINUS ORCA ) HISTORICAL BONE AND TOOTH SAMPLES TO IDENTIFY WESTERN U.S. ECOTYPES - Phillip A. Morin; Richard G. LeDuc; Kelly M. Robertson; Nicole M. Hedrick; William F. Perrin; Michael Etnier; Paul Wade; Barbara L. Taylor (Marine Mammal Science, 2006)
Little is known about the historical range of killer whale ecotypes in the eastern North Pacific (ENP). It is possible that ranges have shifted in the last few decades because of changes in availability of food. In particular, the southern resident ecotype, currently found primarily in the inland waters of Washington State, is known to prey extensively on salmon, which have declined in recent decades along the outer coasts of Washington, Oregon, and California. To investigate historical distributions of this and the other ENP ecotypes, samples of teeth and bones were obtained from NMFS and museum collections. We amplified a short section of the mitochondrial DNA control region that contains four diagnostic sites that differentiate between haplotypes found in ecotypes of ENP killer whales. Results did not show any southern resident haplotypes in samples from south of the Washington State inland waterways. One whale genetically identified as a northern resident extends the known southernmost distribution of the population from Oregon to California. Items of diet identified from stomach contents of six of the whales genetically identified to ecotype conformed with what is known of the feeding habits of the various ecotypes.

STENELLA ATTENUATA FROM CURAÇAO MISIDENTIFIED AS S. COERULEOALBA - RICHARD G. LEDUC; WILLIAM F. PERRIN; ADOLPHE O. De BROT; NÉLIO B. BARROS; PETER J. H. VAN BREE (Marine Mammal Science, 1997)