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.

Land use regression model to predict nitrogen dioxide in the greater Philadelphia area - Bryan Terry; Peleg Kremer; Steven T. Goldsmith; Kabindra M. Shakya (Atmospheric Pollution Research, 2025)

Air quality impacts from the development of unconventional oil and gas well pads: Air toxics and other volatile organic compounds - I-Ting Ku; Yong Zhou; Arsineh Hecobian; Katherine Benedict; Brent Buck; Emily Lachenmayer; Bryan Terry; Morgan Frazier; Jie Zhang; Da Pan; Lena Low; Amy Sullivan; et al. (Atmospheric Environment, 2024)

The offshore renewables industry may be better served by new bespoke design guidelines than by automatic adoption of recommended practices developed for oil and gas infrastructure: A recommendation illustrated by subsea cable design - Terry Griffiths; Scott Draper; Liang Cheng; Hongwei An; Marie-Lise Schläppy; Antonino Fogliani; David White; Stuart Noble; Daniel Coles; Fraser Johnson; Bryan Thurstan; Yunfei Teng (Frontiers in Marine Science, 2023)
Introduction There is an emerging need for the offshore renewable industry to have their own bespoke design guidelines because the associated projects and offshore facilities differ in fundamental ways to oil and gas facilities. Offshore renewable energy (ORE) facilities have already surpassed the numbers of installed facilities in the oil and gas industry by an order of magnitude and demand is forecast to continue growing exponentially. In addition ORE facilities often have different response characteristics and limit states or failure modes as well as profoundly different risk and consequence profiles given they are generally uncrewed and do not contain explosive hydrocarbon fluids which might be released into the environment. Therefore, the purpose of this paper is to advocate for licensing bodies and regulators (such as the various national PEL 114 committees) to challenge the process of automatic adoption of oil and gas design processes, while pushing for offshore renewables to be treated differently, when appropriate, with more relevant and applicable guidance. Methods To support this argument we present new bespoke design guidance developed for subsea cables based on specific modes of cable behaviour, which often differ from pipelines. We also show worked examples from recent project experience. The results from on-bottom stability analyses of a set of cables are compared between conventional oil and gas guidance following DNV-RP-F109 versus the stability using cable-optimised approaches. Results The outcomes from the ‘conventional’ oil and gas results are not simply biased compared to cable-optimised design methods, with a trend of being either conservative or unconservative. Instead, the results of the two methods are very poorly correlated. This shows that the oil and gas approach isn't simply biased when applied to cables, but is instead unreliable because it doesn't capture the underlying failure conditions. These analytical comparisons are supported by field observation - the ocean doesn't lie, and makes short work of any anthropogenic structures which are designed with inadequate appreciation of the real world conditions. Discussion To support the rapid growth of ORE, we should therefore actively pursue opportunities to rewrite the design rules and standards, so that they better support the specific requirements of ORE infrastructure, rather than legacy oil and gas structures. With more appropriate design practices, we can accelerate the roll out of ORE to meet net zero, and mitigate the climate crisis.

Evaluation of Indoor and Outdoor Aquaculture Systems as Alternatives to Harvesting Hemolymph From Random Wild Capture of Horseshoe Crabs - Rachel Tinker-Kulberg; Anthony L. Dellinger; Lisa C. Gentit; Bryan A. Fluech; Cole A. Wilder; Ivy L. Spratling; David J. Stasek; Christopher L. Kepley; Lee Robertson; Melinda K. Goddard; Terry E. Brady; Lance Tōland; et al. (Frontiers in Marine Science, 2020)

Ginkgo biloba Extract Prevents Female Mice from Ischemic Brain Damage and the Mechanism Is Independent of the HO1/Wnt Pathway - Jatin Tulsulkar; Bryan Glueck; Terry D. Hinds; Zahoor A. Shah (Translational Stroke Research, 2016)

Indoor air quality in schools: exposure to fungal allergens - Estelle Levetin; Richard Shaughnessy; Eugene Fisher; Bryan Ligman; Jed Harrison; Terry Brennan (Aerobiologia, 1995)