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.

The Toxicology Investigators Consortium 2023 Annual Report - Adrienne Hughes; Alexandra Amaducci; Sharan L. Campleman; Shao Li; Mari Costantini; Meghan B. Spyres; Hannah Spungen; Jessica Kent; Alyssa Falise; Rachel Culbreth; Paul M. Wax; Jeffery Brent; et al. (Journal of Medical Toxicology, 2024)

Advancing Understanding of Chemical Exposures and Maternal-child Health Through the U.S. Environmental Influences on Child Health Outcomes (ECHO) Program: A Scoping Review - Emily S. Barrett; Jennifer L. Ames; Stephanie M. Eick; Alicia K. Peterson; Zorimar Rivera-Núñez; Anne P. Starling; Jessie P. Buckley; Barbara O‟Brien; Lisa Peterson; Patrick Parsons; Kannan Kurunthacalam; Manish Arora; et al. (Current Environmental Health Reports, 2024)
Purpose of Review Environmental chemical exposures may disrupt child development, with long-lasting health impacts. To date, U.S. studies of early environmental exposures have been limited in size and diversity, hindering power and generalizability. With harmonized data from over 60,000 participants representing 69 pregnancy cohorts, the National Institutes of Health’s Environmental influences on Child Health Outcomes (ECHO) Program is the largest study of U.S. children’s health. Here, we: (1) review ECHO-wide studies of chemical exposures and maternal-child health; and (2) outline opportunities for future research using ECHO data. Recent Findings As of early 2024, in addition to over 200 single-cohort (or award) papers on chemical exposures supported by ECHO, ten collaborative multi-cohort papers have been made possible by ECHO data harmonization and new data collection. Multi-cohort papers have examined prenatal exposure to per- and polyfluoroalkyl substances (PFAS), phthalates, phenols and parabens, organophosphate esters (OPEs), metals, melamine and aromatic amines, and emerging contaminants. They have primarily focused on describing patterns of maternal exposure or examining associations with maternal and infant outcomes; fewer studies have examined later child outcomes (e.g., autism) although follow up of enrolled ECHO children continues. The NICHD’s Data and Specimen Hub (DASH) database houses extensive ECHO data including over 470,000 chemical assay results and complementary data on priority outcome areas (pre, peri-, and postnatal, airway, obesity, neurodevelopment, and positive health), making it a rich resource for future analyses. Summary ECHO’s extensive data repository, including biomarkers of chemical exposures, can be used to advance our understanding of environmental influences on children’s health. Although few published studies have capitalized on these unique harmonized data to date, many analyses are underway with data now widely available.

The Toxicology Investigators Consortium 2022 Annual Report - Alexandra M. Amaducci; Sharan L. Campleman; Shao Li; Dana L. Karshenas; Meghan B. Spyres; Lynn A. Farrugia; A. Min Kang; Rachel E. Culbreth; Paul M. Wax; Jeffrey Brent; Kim Aldy; Jennifer Acciani; et al. (Journal of Medical Toxicology, 2023)

Reconciling bathymetric and stratigraphic expressions of submarine channel geometry - W. Hamish Mitchell; Alexander C. Whittaker; Mike Mayall; Lidia Lonergan (Marine Geology, 2023)

The Toxicology Investigators Consortium Case Registry—the 2021 Annual Report - Jennifer S. Love; Dana L. Karshenas; Meghan B. Spyres; Lynn A. Farrugia; A. Min Kang; HoanVu Nguyen; Sharan L. Campleman; Shao Li; Paul M. Wax; Jeffery Brent; Kim Aldy; Michael Abesamis; et al. (Journal of Medical Toxicology, 2022)

Demonstration of the Feasibility of Predicting the Flow of Pharmaceutically Relevant Powders from Particle and Bulk Physical Properties - Hervé Barjat; Stephen Checkley; Toma Chitu; Neil Dawson; Amin Farshchi; Ana Ferreira; John Gamble; Michael Leane; Andy Mitchell; Chris Morris; Kendal Pitt; Richard Storey; et al. (Journal of Pharmaceutical Innovation, 2021)

The seafood supply chain from a fraudulent perspective - Michaela Fox; Mike Mitchell; Moira Dean; Christopher Elliott; Katrina Campbell (Food Security, 2018)

Prioritization of knowledge needs for sustainable aquaculture: a national and global perspective - Aisla C Jones; Angela Mead; Michel J Kaiser; Melanie C V Austen; Alex W Adrian; Neil A Auchterlonie; Kenneth D Black; Lucy R Blow; Charlotte Bury; Janet H Brown; Gavin M Burnell; Elaine Connolly; et al. (Fish and Fisheries, 2015)
Aquaculture is currently the fastest expanding global animal food production sector and is a key future contributor to food security. An increase in food security will be dependent upon the development and improvement of sustainable practices. A prioritization exercise was undertaken, focusing on the future knowledge needs to underpin UK sustainable aquaculture (both domestic and imported products) using a ‘task force’ group of 36 ‘practitioners’ and 12 ‘research scientists’ who have an active interest in sustainable aquaculture. A long list of 264 knowledge needs related to sustainable aquaculture was developed in conjunction with the task force. The long list was further refined through a three stage process of voting and scoring, including discussions of each knowledge need. The top 25 knowledge needs are presented, as scored separately by ‘practitioners’ or ‘research scientists’. There was similar agreement in priorities identified by these two groups. The priority knowledge needs will provide guidance to structure ongoing work to make science accessible to practitioners and help to prioritize future science policy needs and funding. The process of knowledge exchange, and the mechanisms by which this can be achieved, effectively emerged as the top priority for sustainable aquaculture. Viable alternatives to wild fish‐based aquaculture feeds, resource constraints that will potentially limit expansion of aquaculture, sustainable offshore aquaculture and the treatment of sea lice also emerged as strong priorities. Although the exercise was focused on UK needs for sustainable aquaculture, many of the emergent issues are considered to have global application.

Artificial watering points are focal points for activity by an invasive herbivore but not native herbivores in conservation reserves in arid Australia - Mike Letnic; Shawn W. Laffan; Aaron C. Greenville; Benjamin G. Russell; Bruce Mitchell; Peter J. S. Fleming (Biodiversity and Conservation, 2015)

Secondary cell wall composition and candidate gene expression in developing willow (Salix purpurea) stems - Yongfang Wan; Cristina Gritsch; Theodora Tryfona; Mike J. Ray; Ambrose Andongabo; Keywan Hassani-Pak; Huw D. Jones; Paul Dupree; Angela Karp; Peter R. Shewry; Rowan A. C. Mitchell (Planta, 2014)

The Business of Forest Enterprise / Urban Ecosystems - Steve Narolski; Richard Bin Mei; Rafael De La Torre; Mike Clutter; John Pait; Maggi Kelly; Frederick Cubbage; Patricio MacDonagh; Gustavo Balmelli; Virginia Morales Olmos; Adriana Bussoni; Rafael Rubilar; et al. (Journal of Forestry, 2011)
22In 2009, five unique methods were used to inspect vegetation-related conditions along Bonneville Power Administration (BPA) rights-of-way (ROW). Some methods were trials that BPA committed to execute as part of a settlement with its regional regulatory organization, the Western Electric Coordination Council (WECC), for violations of reliability standards from vegetation grow-in related outages. A combination of simple, stratified, and 100% sampling were used to compare and contrast each inspection technique. A cost-replacement comparison between all inspection techniques was performed, weighting efficacy of one technique to another in the form of replacement value. Cost-benefit and return-on-investment analyses were also computed. From these analyses, LiDAR proved most effective in identifying vegetation related clearance issues but proved most costly, at least for initial establishment. The average cost of LiDAR trended downward with subsequent flights. The most cost effective method was using helicopters with either Natural Resource Specialists (NRS) or Transmission Line Maintenance (TLM) personnel serving as aerial observers, but this methodology proved the most inaccurate. Furthermore, the ancillary utility of LiDAR for related asset assessments more than justify the initial expense, includes power line sag ratings, asset (structures, insulators, roads, etc.) health, and encroachment identification. It is hypothesized that incorporating LiDAR sampling from 20% of the whole system per year to 40+ % may actually represent a cost-savings when allocating available resources system-wide. This data can also be used for documenting compliance with all federal regulations and requirements, as well as substitute for manual on-the-ground inspections, whether by BPA staff or third-party contractors.

Improved Quality Control Metrics for Cascade Impaction Measurements of Orally Inhaled Drug Products (OIPs) - Terrence P. Tougas; David Christopher; Jolyon P. Mitchell; Helen Strickland; Bruce Wyka; Mike Van Oort; Svetlana Lyapustina (AAPS PharmSciTech, 2009)

Minimizing Variability of Cascade Impaction Measurements in Inhalers and Nebulizers - Matthew Bonam; David Christopher; David Cipolla; Brent Donovan; David Goodwin; Susan Holmes; Svetlana Lyapustina; Jolyon Mitchell; Steve Nichols; Gunilla Pettersson; Chris Quale; Nagaraja Rao; et al. (AAPS PharmSciTech, 2008)

Mouse Y chromosome - Colin Bishop; Mike Mitchell (Mammalian Genome, 1997)