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.

A simple and robust approach to Bayesian modelling of overdispersed data - David Fletcher; Peter W. Dillingham; Matthew Parry (Environmental and Ecological Statistics, 2023)

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)

Multi‐scale integration of tree recruitment and range dynamics in a changing climate - Paige E. Copenhaver‐Parry; Charles J. W. Carroll; Patrick H. Martin; Matthew V. Talluto (Global Ecology and Biogeography, 2020)
Aim The rate and magnitude of climate‐induced tree range shifts may be influenced by range‐wide variation in recruitment, which acts as a bottleneck in tree range dynamics. Here, we compare range predictions made using standard species distribution models (SDMs) and an integrated metamodelling approach that assimilates data on adult occurrence, seedling recruitment dynamics, and seedling survival under both current and future climate, and evaluate the degree to which information provided by seedling data can improve predictions of range dynamics. Location The interior west region of the United States. Time period 1990–2015. Major taxa studied Five widespread conifer tree species. Methods We used a previously published metamodelling framework to combine information from SDMs of adult tree occurrence and sub‐models describing seedling recruitment dynamics and seedling survival into a single set of predictions for the probability of occurrence for each species. The integrated framework links sub‐models to a SDM to generate cohesive predictions that consider information and uncertainty contained in all datasets. We then compared predictions from the integrated model to SDM predictions. Results Integration of seedling information served primarily to improve characterization of model uncertainty, particularly in regions where recruitment may be limited by temperatures that exceed seedling tolerance. Integration constrained response curves very slightly across most climate gradients, particularly across temperature gradients. These differences were primarily attributable to the isolated effects of temperature on seedling survival and not to recruitment dynamics. Main conclusions Our results indicate that range‐wide variation in recruitment both now and in the future is most uncertain along the edges of occupied regions, which increases uncertainty in projections of future species occurrence along range margins. Overall, the broad‐scale climatic dependence of the regeneration niche appears weaker than that of the adult climatic niche, and this enhances uncertainty in predicting range‐wide responses of these species to climate change.

Design, Fabrication, Installation, and Population of a Novel Fiberglass Reinforced Plastic Coral Nursery Structure Off the South Shore of O’ahu, Hawaii - Bardia Konh; Matthew Parry (Frontiers in Marine Science, 2019)

Ecosystem state shifts during long‐term development of an Amazonian peatland - Graeme T. Swindles; Paul J. Morris; Bronwen Whitney; Jennifer M. Galloway; Mariusz Gałka; Angela Gallego‐Sala; Andrew L. Macumber; Donal Mullan; Mark W. Smith; Matthew J. Amesbury; Thomas P. Roland; Hamed Sanei; et al. (Global Change Biology, 2018)
The most carbon (C)‐dense ecosystems of Amazonia are areas characterized by the presence of peatlands. However, Amazonian peatland ecosystems are poorly understood and are threatened by human activities. Here, we present an investigation into long‐term ecohydrological controls on C accumulation in an Amazonian peat dome. This site is the oldest peatland yet discovered in Amazonia (peat initiation ca. 8.9 ka BP ), and developed in three stages: (i) peat initiated in an abandoned river channel with open water and aquatic plants; (ii) inundated forest swamp; and (iii) raised peat dome (since ca. 3.9 ka BP ). Local burning occurred at least three times in the past 4,500 years. Two phases of particularly rapid C accumulation (ca. 6.6–6.1 and ca. 4.9–3.9 ka BP ), potentially resulting from increased net primary productivity, were seemingly driven by drier conditions associated with widespread drought events. The association of drought phases with major ecosystem state shifts (open water wetland–forest swamp–peat dome) suggests a potential climatic control on the developmental trajectory of this tropical peatland. A third drought phase centred on ca. 1.8–1.1 ka BP led to markedly reduced C accumulation and potentially a hiatus during the peat dome stage. Our results suggest that future droughts may lead to phases of rapid C accumulation in some inundated tropical peat swamps, although this can lead ultimately to a shift to ombrotrophy and a subsequent return to slower C accumulation. Conversely, in ombrotrophic peat domes, droughts may lead to reduced C accumulation or even net loss of peat. Increased surface wetness at our site in recent decades may reflect a shift towards a wetter climate in western Amazonia. Amazonian peatlands represent important carbon stores and habitats, and are important archives of past climatic and ecological information. They should form key foci for conservation efforts.

Consumption of wild-harvested meat from New Zealand feral animals provides a unique opportunity to study the health effects of lead exposure in hunters - Eric J. Buenz; Gareth J. Parry; Matthew Peacey (Ambio, 2016)

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.

Examining the effects of initial smoking abstinence on response to smoking-related stimuli and response inhibition in a human laboratory model - Matthew P. Bradstreet; Stephen T. Higgins; F. Joseph McClernon; Rachel V. Kozink; Joan M. Skelly; Yukiko Washio; Alexa A. Lopez; Marie A. Parry (Psychopharmacology, 2014)

Concomitant Antibiotic and Mercury Resistance Among Gastrointestinal Microflora of Feral Brook Trout, Salvelinus fontinalis - Matthew M. Meredith; Erin M. Parry; Justin A. Guay; Nicholas O. Markham; G. Russell Danner; Keith A. Johnson; Tamar Barkay; Frank A. Fekete (Current Microbiology, 2012)

Trophic variation with length in two ommastrephid squids, Ommastrephes bartramii and Sthenoteuthis oualaniensis - Matthew Parry (Marine Biology, 2008)

Effects of elevated plasma tryptophan on brain activation associated with the Stroop task - Rhian M. Morgan; Allyson M. M. Parry; Ricardo M. Arida; Paul M. Matthews; Bruce Davies; Linda M. Castell (Psychopharmacology, 2007)