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.

Combination and summary of ATLAS dark matter searches interpreted in a 2HDM with a pseudo-scalar mediator using 139 fb−1 of s = 13 TeV pp collision data - G. Aad; B. Abbott; K. Abeling; N.J. Abicht; S.H. Abidi; A. Aboulhorma; H. Abramowicz; H. Abreu; Y. Abulaiti; A.C. Abusleme Hoffman; B.S. Acharya; C. Adam Bourdarios; et al. (Science Bulletin, 2024)

Mapping of temperate upland habitats using high-resolution satellite imagery and machine learning - Charmaine Cruz; Philip M. Perrin; James R. Martin; Jerome O’Connell; Kevin McGuinness; John Connolly (Environmental Monitoring and Assessment, 2024)
Upland habitats provide vital ecological services, yet they are highly threatened by natural and anthropogenic stressors. Monitoring these vulnerable habitats is fundamental for conservation and involves determining information about their spatial locations and conditions. Remote sensing has evolved as a promising tool to map the distribution of upland habitats in space and time. However, the resolutions of most freely available satellite images (e.g., 10-m resolution for Sentinel-2) may not be sufficient for mapping relatively small features, especially in the heterogeneous landscape—in terms of habitat composition—of uplands. Moreover, the use of traditional remote sensing methods, imposing discrete boundaries between habitats, may not accurately represent upland habitats as they often occur in mosaics and merge with each other. In this context, we used high-resolution (2 m) Pleiades satellite imagery and Random Forest (RF) machine learning to map habitats at two Irish upland sites. Specifically, we investigated the impact of varying spatial resolutions on classification accuracy and proposed a complementary approach to traditional methods for mapping complex upland habitats. Results showed that the accuracy generally improved with finer spatial resolution data, with the highest accuracy values (80.34% and 79.64%) achieved for both sites using the 2-m resolution datasets. The probability maps derived from the RF-based fuzzy classification technique can represent complex mosaics and gradual transitions occurring in upland habitats. The presented approach can potentially enhance our understanding of the spatiotemporal dynamics of habitats over large areas.

Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning - Maryam Rahmani; Donna Dierker; Lauren Yaeger; Andrew Saykin; Patrick H. Luckett; Andrei G. Vlassenko; Christopher Owens; Hussain Jafri; Kyle Womack; Jurgen Fripp; Ying Xia; Duygu Tosun; et al. (Brain Imaging and Behavior, 2024)
This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, we categorized WMH segmentation tools based on their methodologies from January 1, 2000, to November 18, 2022. Inclusion criteria involved articles using openly available techniques with detailed descriptions, focusing on WMH as a primary outcome. Our analysis identified 1007 visual rating scales, 118 pipeline development articles, and 509 implementation articles. These studies predominantly explored aging, dementia, psychiatric disorders, and small vessel disease, with aging and dementia being the most prevalent cohorts. Deep learning emerged as the most frequently developed segmentation technique, indicative of a heightened scrutiny in new technique development over the past two decades. We illustrate observed patterns and discrepancies between published and implemented WMH techniques. Despite increasingly sophisticated quantitative segmentation options, visual rating scales persist, with the SPM technique being the most utilized among quantitative methods and potentially serving as a reference standard for newer techniques. Our findings highlight the need for future standards in WMH segmentation, and we provide recommendations based on these observations.

Climate change vulnerability of Arctic char across Scandinavia - Clint C. Muhlfeld; Timothy J. Cline; Anders G. Finstad; Dag O. Hessen; Sam Perrin; Jens Thaulow; Diane Whited; Leif Asbjørn Vøllestad (Global Change Biology, 2024)
Climate change is anticipated to cause species to shift their ranges upward and poleward, yet space for tracking suitable habitat conditions may be limited for range‐restricted species at the highest elevations and latitudes of the globe. Consequently, range‐restricted species inhabiting Arctic freshwater ecosystems, where global warming is most pronounced, face the challenge of coping with changing abiotic and biotic conditions or risk extinction. Here, we use an extensive fish community and environmental dataset for 1762 lakes sampled across Scandinavia (mid‐1990s) to evaluate the climate vulnerability of Arctic char ( Salvelinus alpinus ), the world's most cold‐adapted and northernly distributed freshwater fish. Machine learning models show that abiotic and biotic factors strongly predict the occurrence of Arctic char across the region with an overall accuracy of 89 percent. Arctic char is less likely to occur in lakes with warm summer temperatures, high dissolved organic carbon levels (i.e., browning), and presence of northern pike ( Esox lucius ). Importantly, climate warming impacts are moderated by habitat (i.e., lake area) and amplified by the presence of competitors and/or predators (i.e., northern pike). Climate warming projections under the RCP8.5 emission scenario indicate that 81% of extant populations are at high risk of extirpation by 2080. Highly vulnerable populations occur across their range, particularly near the southern range limit and at lower elevations, with potential refugia found in some mountainous and coastal regions. Our findings highlight that range shifts may give way to range contractions for this cold‐water specialist, indicating the need for pro‐active conservation and mitigation efforts to avoid the loss of Arctic freshwater biodiversity.

Embedded software of the KM3NeT central logic board - S. Aiello; A. Albert; S. Alves Garre; Z. Aly; A. Ambrosone; F. Ameli; M. Andre; E. Androutsou; M. Anghinolfi; M. Anguita; L. Aphecetche; M. Ardid; et al. (Computer Physics Communications, 2024)

Improving the mapping of coastal invasive species using UAV imagery and deep learning - Charmaine Cruz; Kevin McGuinness; Philip M. Perrin; Jerome O’Connell; James R. Martin; John Connolly (International Journal of Remote Sensing, 2023)

Twenty-three unsolved problems in hydrology (UPH) – a community perspective - Günter Blöschl; Marc F.P. Bierkens; Antonio Chambel; Christophe Cudennec; Georgia Destouni; Aldo Fiori; James W. Kirchner; Jeffrey J. McDonnell; Hubert H.G. Savenije; Murugesu Sivapalan; Christine Stumpp; Elena Toth; et al. (Hydrological Sciences Journal, 2019)

Determining the vertical evolution of hydrodynamic parameters in weathered and fractured south Indian crystalline-rock aquifers: insights from a study on an instrumented site - A. Boisson; N. Guihéneuf; J. Perrin; O. Bour; B. Dewandel; A. Dausse; M. Viossanges; S. Ahmed; J. C. Maréchal (Hydrogeology Journal, 2015)

A Role for the Endothelial Glycocalyx in Regulating Microvascular Permeability in Diabetes Mellitus - Rachel M. Perrin; Steven J. Harper; David O. Bates (Cell Biochemistry and Biophysics, 2007)

Release of secondary free radicals during post-ischaemic reperfusion is not influenced by extracellular calcium levels in isolated rat hearts - C. Perrin-Sarrado; O. Bouchot; C. Vergely; L. Rochette (Molecular and Cellular Biochemistry, 2007)

A discussion of statistical methods used to estimate extreme wind speeds - O. Perrin; H. Rootzén; R. Taesler (Theoretical and Applied Climatology, 2006)

Renal handling of salt and water in humans during exercise with or without hydration - J-P. Mallié; Z. Ait-Djafer; C. Saunders; A. Pierrat; M. Caira; O. Courroy; V. Panescu; P. Perrin (European Journal of Applied Physiology, 2002)

Improvement of walking speed prediction by accelerometry and altimetry, validated by satellite positioning - O. Perrin; P. Terrier; Q. Ladetto; B. Merminod; Y. Schutz (Medical & Biological Engineering & Computing, 2000)

STENELLA ATTENUATA FROM CURAÇAO MISIDENTIFIED AS S. COERULEOALBA - RICHARD G. LEDUC; WILLIAM F. PERRIN; ADOLPHE O. De BROT; NÉLIO B. BARROS; PETER J. H. VAN BREE (Marine Mammal Science, 1997)