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.

Environmental factors influencing ground-nesting bee communities in an urban landscape: implications for conservation - Indré Cepukaitè; Anders Björkén; Lina Ahlbäck Widenfalk; Mats Jonsell; Barbara Locke (Urban Ecosystems, 2025)
Habitat loss and degradation of nesting habitats due to agricultural and urban development is considered one of the key factors causing pollinator decline worldwide. Nesting habitat requirements and availability is a strong determinant to which wild bee species are prevailing in urban spaces and knowledge of habitat requirements is therefore fundamental for successful conservation management. Although urban environments can host relatively diverse wild bee communities, ground-nesting bees and associated cleptoparasitic bees are disadvantaged, primarily due to a scarcity of suitable nesting resources. Further, conservation efforts for wild bee habitat (re)creation are largely focused on aerial nesters, despite the proportionally greater diversity and higher vulnerability of ground-nesting bees. The aim of this study was to investigate how environmental factors, including the age and size of sand patches, sand grain size, soil temperature, and the proportion of ground litter-cover vs. bare ground, have shaped ground-nesting bee community structure in an urban landscape. This was done by evaluating wild bee diversity and abundance across various sites with a range of qualities in Uppsala, Sweden. This study revealed a drastic 85% loss of suitable sandy habitat over a 75-year period due to urbanization. Based on the results of this study, we recommend a conservation approach for urban developers to lessen the negative impacts of urbanization on wild bee diversity and abundance. This includes the establishment of numerous bare ground sandy sites with medium size sand grain, and periodical management of vegetation to sustain bare sandy soil patches.

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.

Sequence‐Based Multi Ancestry Association Study Reveals the Polygenic Architecture of Varroa destructor Resistance in the Honeybee Apis mellifera - Sonia E. Eynard; Fanny Mondet; Benjamin Basso; Olivier Bouchez; Yves Le Conte; Benjamin Dainat; Axel Decourtye; Lucie Genestout; Matthieu Guichard; François Guillaume; Emmanuelle Labarthe; Barbara Locke; et al. (Molecular Ecology, 2025)
Honeybees, Apis mellifera , have experienced the full impacts of globalisation, including the recent invasion by the parasitic mite Varroa destructor , now one of the main causes of colony losses worldwide. The strong selection pressure it exerts has led some colonies to develop defence strategies conferring some degree of resistance to the parasite. Assuming these traits are partly heritable, selective breeding of naturally resistant bees could be a sustainable strategy for fighting infestations. To characterise the genetic determinism of varroa resistance, we conducted the largest genome wide association study performed to date on whole genome sequencing of more than 1500 colonies on multiple phenotypes linked to varroa resistance of honeybees. To take into account some genetic diversity of honeybees, colonies belonging to different ancestries representing the main honeybee subspecies in Western Europe were included and analysed both as separate populations and combined in a meta‐analysis. The results show that varroa resistance is substantially heritable and polygenic: while 60 significant associations were identified, none explain a substantial part of the trait genetic variance. Overall our study highlights that genomic selection for varroa resistance is promising but that it will not be based on managing a few strong effect mutations and rather use approaches that leverage the genome wide diversity of honeybee populations. From a broader perspective, these results point the way towards understanding the genetic adaptation of eusocial insects to parasite load.

Bacterial Diseases in Honeybees - Eva Forsgren; Barbara Locke; Fabrice Sircoulomb; Marc Oliver Schäfer (Current Clinical Microbiology Reports, 2018)