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.

Hydrocarbon-degrading microbial populations in permanently cold deep-sea sediments in the NW Atlantic - Oyeboade Adebayo; Srijak Bhatnagar; Jamie Webb; Calvin Campbell; Martin Fowler; Natasha M. MacAdam; Adam Macdonald; Carmen Li; Casey R.J. Hubert (Marine Pollution Bulletin, 2024)

Multi-year seabed environmental baseline in deep-sea offshore oil prospective areas established using microbial biodiversity - Deidra K. Ferguson; Carmen Li; Anirban Chakraborty; Daniel A. Gittins; Martin Fowler; Jamie Webb; Calvin Campbell; Natasha Morrison; Adam MacDonald; Casey R.J. Hubert (Marine Pollution Bulletin, 2023)

Elevated bacterial endospores associated with thermogenic hydrocarbon seeps in deep sea sediments - Jayne E. Rattray; Gretta Elizondo; Kathryn Sloan; Natasha Morrison; Martin Fowler; Daniel A. Gittins; Jamie Webb; D. Calvin Campbell; Adam MacDonald; Casey R.J. Hubert (Organic Geochemistry, 2023)

Bacterial anomalies associated with deep sea hydrocarbon seepage along the Scotian Slope - Carmen Li; Oyeboade Adebayo; Deidra K. Ferguson; Scott Wang; Jayne E. Rattray; Martin Fowler; Jamie Webb; Calvin Campbell; Natasha Morrison; Adam MacDonald; Casey R.J. Hubert (Deep Sea Research Part I: Oceanographic Research Papers, 2023)

The Supportive Care Needs of Cancer Patients: a Systematic Review - Madeleine Evans Webb; Elizabeth Murray; Zane William Younger; Henry Goodfellow; Jamie Ross (Journal of Cancer Education, 2021)
Cancer, and the complex nature of treatment, has a profound impact on lives of patients and their families. Subsequently, cancer patients have a wide range of needs. This study aims to identify and synthesise cancer patients’ views about areas where they need support throughout their care. A systematic search of the literature from PsycInfo, Embase and Medline databases was conducted, and a narrative. Synthesis of results was carried out using the Corbin & Strauss “3 lines of work” framework. For each line of work, a group of key common needs were identified. For illness-work, the key needs idenitified were; understanding their illness and treatment options, knowing what to expect, communication with healthcare professionals, and staying well. In regards to everyday work, patients wanted to maintain a sense of normalcy and look after their loved ones. For biographical work, patients commonly struggled with the emotion impact of illness and a lack of control over their lives. Spiritual, sexual and financial problems were less universal. For some types of support, demographic factors influenced the level of need reported. While all patients are unique, there are a clear set of issues that are common to a majority of cancer journeys. To improve care, these needs should be prioritised by healthcare practitioners.

Introduction to the special issue - Jamie Davies; Michael Grinfeld; Steven D. Webb (Theory in Biosciences, 2011)