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.

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.

Operationalizing crop model data assimilation for improved on-farm situational awareness - Matthew J. Knowling; Jeremy T. White; Dylan Grigg; Cassandra Collins; Seth Westra; Rob R. Walker; Anne Pellegrino; Bertram Ostendorf; Bree Bennett; Ayman Alzraiee (Agricultural and Forest Meteorology, 2023)

Applying the dark diversity concept to nature conservation - Rob J. Lewis; Francesco de Bello; Jonathan A. Bennett; Pavel Fibich; Genevieve E. Finerty; Lars Götzenberger; Inga Hiiesalu; Liis Kasari; Jan Lepš; Maria Májeková; Ondřej Mudrák; Kersti Riibak; et al. (Conservation Biology, 2017)
Linking diversity to biological processes is central for developing informed and effective conservation decisions. Unfortunately, observable patterns provide only a proportion of the information necessary for fully understanding the mechanisms and processes acting on a particular population or community. We suggest conservation managers use the often overlooked information relative to species absences and pay particular attention to dark diversity (i.e., a set of species that are absent from a site but that could disperse to and establish there, in other words, the absent portion of a habitat‐specific species pool). Together with existing ecological metrics, concepts, and conservation tools, dark diversity can be used to complement and further develop conservation prioritization and management decisions through an understanding of biodiversity relativized by its potential (i.e., its species pool). Furthermore, through a detailed understanding of the population, community, and functional dark diversity, the restoration potential of degraded habitats can be more rigorously assessed and so to the likelihood of successful species invasions. We suggest the application of the dark diversity concept is currently an underappreciated source of information that is valuable for conservation applications ranging from macroscale conservation prioritization to more locally scaled restoration ecology and the management of invasive species.

Testing the generality of above‐ground biomass allometry across plant functional types at the continent scale - Keryn I. Paul; Stephen H. Roxburgh; Jerome Chave; Jacqueline R. England; Ayalsew Zerihun; Alison Specht; Tom Lewis; Lauren T. Bennett; Thomas G. Baker; Mark A. Adams; Dan Huxtable; Kelvin D. Montagu; et al. (Global Change Biology, 2016)
Accurate ground‐based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost‐effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above‐ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power‐law models explained 84–95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand‐based biomass from allometric models of varying levels of generalization (species‐specific, plant functional type) were validated using whole‐plot harvest data from 17 contrasting stands (range: 9–356 Mg ha −1 ). Losses in efficiency of prediction were stand ‐level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost‐effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species‐specific models is only warranted when gains in accuracy of stand‐based predictions are relatively high (e.g. high‐value monocultures).

Estimating Occupancy in Large Landscapes: Evaluation of Amphibian Monitoring in the Greater Yellowstone Ecosystem - William R. Gould; Debra A. Patla; Rob Daley; Paul Stephen Corn; Blake R. Hossack; Robert Bennetts; Charles R. Peterson (Wetlands, 2012)

Neuroscience data and tool sharing - Peter Eckersley; Gary F. Egan; Erik De Schutter; Tang Yiyuan; Mirko Novak; Vaclav Sebesta; Line Matthiessen; Irio P. Jaaskelainen; Ulla Ruotsalainen; Andreas V. M. Herz; Klaus-Peter Hoffmann; Raphael Ritz; et al. (Neuroinformatics, 2003)