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.

Vaccination against Hendra virus of previously unvaccinated mares and their foals demonstrates the effectiveness of the current prime and boost vaccination schedule for foals - Kimberley J. Carey; Ina Smith; Carol A. Hartley; Kirsten E. Bailey; Kath McIntosh; Christopher C. Broder; James R. Gilkerson (Veterinary Microbiology, 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.

Glass geochemistry and tephrostratigraphy of key tephra layers in and around Lake Van, Eastern Anatolian Volcanic Province (EAVP) - Rebecca J. Kearney; Jeremy Goff; Victoria Smith; Markus J. Schwab; Yavuz Özdemir; Özgür Karaoǧlu; Matthew Thirlwall; Dan N. Barfod; Oona Appelt; Christina Günter; Jan Fietzke; Nadine Pickarski; et al. (Quaternary Science Reviews, 2025)

Foals of mares vaccinated for Hendra virus have a suboptimal response to HeV vaccination - Kimberley J. Carey; Ina Smith; Jennifer Barr; Sarah Caruso; Gough G. Au; Carol A. Hartley; Kirsten E. Bailey; Wendy Perriam; Christopher C. Broder; James R. Gilkerson (Veterinary Microbiology, 2024)

Tropical root responses to global changes: A synthesis - Daniela Yaffar; Laynara F. Lugli; Michelle Y. Wong; Richard J. Norby; Shalom D. Addo‐Danso; Marie Arnaud; Amanda L. Cordeiro; Lee H. Dietterich; Milton H. Diaz‐Toribio; Ming Y. Lee; Om Prakash Ghimire; Chris M. Smith‐Martin; et al. (Global Change Biology, 2024)
Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO 2 , and fires. Based on tree species‐ and community‐level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO 2 by increasing root biomass in species‐scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.

Learning Together for and with the Martuwarra Fitzroy River - Rosemary Hill; Pia Harkness; Nat Raisbeck-Brown; Ilisapeci Lyons; Jorge G. Álvarez-Romero; Milena Kiatkoski Kim; Dennis Chungalla; Heather Wungundin; Mary Aiken; Jean Malay; Bernadette Williams; Rachel Buissereth; et al. (Sustainability Science, 2022)
Co-production across scientific and Indigenous knowledge systems has become a cornerstone of research to enhance knowledge, practice, ethics, and foster sustainability transformations. However, the profound differences in world views and the complex and contested histories of nation-state colonisation on Indigenous territories, highlight both opportunities and risks for Indigenous people when engaging with knowledge co-production. This paper investigates the conditions under which knowledge co-production can lead to improved Indigenous adaptive environmental planning and management among remote land-attached Indigenous peoples through a case study with ten Traditional Owner groups in the Martuwarra (Fitzroy River) Catchment in Western Australia’s Kimberley region. The research team built a 3D map of the river and used it, together with an interactive table-top projector, to bring together both scientific and Indigenous spatial knowledge. Participatory influence mapping, aligned with Traditional Owner priorities to achieve cultural governance and management planning goals set out in the Fitzroy River Declaration, investigated power relations. An analytical framework, examining underlying mechanisms of social learning, knowledge promotion and enhancing influence, based on different theories of change, was applied to unpack the immediate outcomes from these activities. The analysis identified that knowledge co-production activities improved the accessibility of the knowledge, the experiences of the knowledge users, strengthened collective identity and partnerships, and strengthened Indigenous-led institutions. The focus on cultural governance and management planning goals in the Fitzroy River Declaration enabled the activities to directly affect key drivers of Indigenous adaptive environmental planning and management—the Indigenous-led institutions. The nation-state arrangements also gave some support to local learning and decision-making through a key Indigenous institution, Martuwarra Fitzroy River Council. Knowledge co-production with remote land-attached Indigenous peoples can improve adaptive environmental planning and management where it fosters learning together, is grounded in the Indigenous-led institutions and addresses their priorities.

Global root traits (GRooT) database - Nathaly R. Guerrero‐Ramírez; Liesje Mommer; Grégoire T. Freschet; Colleen M. Iversen; M. Luke McCormack; Jens Kattge; Hendrik Poorter; Fons van der Plas; Joana Bergmann; Thom W. Kuyper; Larry M. York; Helge Bruelheide; et al. (Global Ecology and Biogeography, 2021)
Motivation Trait data are fundamental to the quantitative description of plant form and function. Although root traits capture key dimensions related to plant responses to changing environmental conditions and effects on ecosystem processes, they have rarely been included in large‐scale comparative studies and global models. For instance, root traits remain absent from nearly all studies that define the global spectrum of plant form and function. Thus, to overcome conceptual and methodological roadblocks preventing a widespread integration of root trait data into large‐scale analyses we created the Global Root Trait (GRooT) Database. GRooT provides ready‐to‐use data by combining the expertise of root ecologists with data mobilization and curation. Specifically, we (a) determined a set of core root traits relevant to the description of plant form and function based on an assessment by experts, (b) maximized species coverage through data standardization within and among traits, and (c) implemented data quality checks. Main types of variables contained GRooT contains 114,222 trait records on 38 continuous root traits. Spatial location and grain Global coverage with data from arid, continental, polar, temperate and tropical biomes. Data on root traits were derived from experimental studies and field studies. Time period and grain Data were recorded between 1911 and 2019. Major taxa and level of measurement GRooT includes root trait data for which taxonomic information is available. Trait records vary in their taxonomic resolution, with subspecies or varieties being the highest and genera the lowest taxonomic resolution available. It contains information for 184 subspecies or varieties, 6,214 species, 1,967 genera and 254 families. Owing to variation in data sources, trait records in the database include both individual observations and mean values. Software format GRooT includes two csv files. A GitHub repository contains the csv files and a script in R to query the database.