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.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

A global analysis of complexity–biodiversity relationships on marine artificial structures - Elisabeth M. A. Strain; Peter D. Steinberg; Maria Vozzo; Emma L. Johnston; Marco Abbiati; Moises A. Aguilera; Laura Airoldi; J. David Aguirre; Gail Ashton; Maritina Bernardi; Paul Brooks; Benny K. K. Chan; et al. (Global Ecology and Biogeography, 2021)
Aim Topographic complexity is widely accepted as a key driver of biodiversity, but at the patch‐scale, complexity–biodiversity relationships may vary spatially and temporally according to the environmental stressors complexity mitigates, and the species richness and identity of potential colonists. Using a manipulative experiment, we assessed spatial variation in patch‐scale effects of complexity on intertidal biodiversity. Location 27 sites within 14 estuaries/bays distributed globally. Time period 2015–2017. Major taxa studied Functional groups of algae, sessile and mobile invertebrates. Methods Concrete tiles of differing complexity (flat; 2.5‐cm or 5‐cm complex) were affixed at low–high intertidal elevation on coastal defence structures, and the richness and abundance of the colonizing taxa were quantified after 12 months. Results The patch‐scale effects of complexity varied spatially and among functional groups. Complexity had neutral to positive effects on total, invertebrate and algal taxa richness, and invertebrate abundances. However, effects on the abundance of algae ranged from positive to negative, depending on location and functional group. The tidal elevation at which tiles were placed accounted for some variation. The total and invertebrate richness were greater at low or mid than at high intertidal elevations. Latitude was also an important source of spatial variation, with the effects of complexity on total richness and mobile mollusc abundance greatest at lower latitudes, whilst the cover of sessile invertebrates and sessile molluscs responded most strongly to complexity at higher latitudes. Conclusions After 12 months, patch‐scale relationships between biodiversity and habitat complexity were not universally positive. Instead, the relationship varied among functional groups and according to local abiotic and biotic conditions. This result challenges the assumption that effects of complexity on biodiversity are universally positive. The variable effect of complexity has ramifications for community and applied ecology, including eco‐engineering and restoration that seek to bolster biodiversity through the addition of complexity.

Differences in acute reinforcement across reduced nicotine content cigarettes - Kenneth A. Perkins; Joshua L. Karelitz (Psychopharmacology, 2020)

Tobacco smoking may delay habituation of reinforcer effectiveness in humans - Joshua L. Karelitz; Kenneth A. Perkins (Psychopharmacology, 2018)

Threshold dose for behavioral discrimination of cigarette nicotine content in menthol vs. non-menthol smokers - Kenneth A. Perkins; Nicole Kunkle; Joshua L. Karelitz (Psychopharmacology, 2017)

Threshold dose for discrimination of nicotine via cigarette smoking - Kenneth A. Perkins; Nicole Kunkle; Joshua L. Karelitz; Valerie C. Michael; Eric C. Donny (Psychopharmacology, 2016)

Association of genetic susceptibility variants for type 2 diabetes with breast cancer risk in women of European ancestry - Zhiguo Zhao; Wanqing Wen; Kyriaki Michailidou; Manjeet K. Bolla; Qin Wang; Ben Zhang; Jirong Long; Xiao-Ou Shu; Marjanka K. Schmidt; Roger L. Milne; Montserrat García-Closas; Jenny Chang-Claude; et al. (Cancer Causes & Control, 2016)

An efficient early phase 2 procedure to screen medications for efficacy in smoking cessation - Kenneth A. Perkins; Caryn Lerman (Psychopharmacology, 2014)

Reinforcement enhancing effects of nicotine via smoking - Kenneth A. Perkins; Joshua L. Karelitz (Psychopharmacology, 2013)

Estimated effects of climate change on flood vulnerability of U.S. bridges - Len Wright; Paul Chinowsky; Kenneth Strzepek; Russell Jones; Richard Streeter; Joel B. Smith; Jean-Marc Mayotte; Anthony Powell; Lesley Jantarasami; William Perkins (Mitigation and Adaptation Strategies for Global Change, 2012)

Varenicline’s effects on acute smoking behavior and reward and their association with subsequent abstinence - Kenneth A. Perkins; Melissa Mercincavage; Carolyn A. Fonte; Caryn Lerman (Psychopharmacology, 2010)

Differences in negative mood-induced smoking reinforcement due to distress tolerance, anxiety sensitivity, and depression history - Kenneth A. Perkins; Joshua L. Karelitz; Grace E. Giedgowd; Cynthia A. Conklin; Michael A. Sayette (Psychopharmacology, 2010)

Lack of reinforcement enhancing effects of nicotine in non-dependent smokers - Kenneth A. Perkins; Amy Grottenthaler; Annette S. Wilson (Psychopharmacology, 2009)

Initial nicotine sensitivity in humans as a function of impulsivity - Kenneth A. Perkins; Caryn Lerman; Sarah B. Coddington; Christopher Jetton; Joshua L. Karelitz; John A. Scott; Annette S. Wilson (Psychopharmacology, 2008)

Guidelines on nicotine dose selection for in vivo research - Shannon G. Matta; David J. Balfour; Neal L. Benowitz; R. Thomas Boyd; Jerry J. Buccafusco; Anthony R. Caggiula; Caroline R. Craig; Allan C. Collins; M. Imad Damaj; Eric C. Donny; Phillip S. Gardiner; Sharon R. Grady; et al. (Psychopharmacology, 2007)

Medication screening for smoking cessation: a proposal for new methodologies - Kenneth A. Perkins; Maxine Stitzer; Caryn Lerman (Psychopharmacology, 2006)

Sex differences in the influence of nicotine dose instructions on the reinforcing and self-reported rewarding effects of smoking - Kenneth A. Perkins; Todd Doyle; Melinda Ciccocioppo; Cynthia Conklin; Michael Sayette; Anthony Caggiula (Psychopharmacology, 2006)

Reduced nicotine reward in obesity: cross-comparison in human and mouse - Julie A. Blendy; Andrew Strasser; Carrie L. Walters; Kenneth A. Perkins; Freda Patterson; Robert Berkowitz; Caryn Lerman (Psychopharmacology, 2005)

Sex differences in the contribution of nicotine and nonpharmacological stimuli to nicotine self-administration in rats - Nadia Chaudhri; Anthony R. Caggiula; Eric C. Donny; Sheri Booth; Maysa A. Gharib; Laure A. Craven; Shannon S. Allen; Alan F. Sved; Kenneth A. Perkins (Psychopharmacology, 2005)

Naltrexone reduces the relative reinforcing value of nicotine in a cigarette smoking choice paradigm - Margaret Rukstalis; Christopher Jepson; Andrew Strasser; Kevin G. Lynch; Kenneth Perkins; Freda Patterson; Caryn Lerman (Psychopharmacology, 2005)

Subjective effects of an initial dose of nicotine nasal spray predict treatment outcome - Margaret Rukstalis; Kenneth Perkins; Janet Audrain-McGovern; Caryn Lerman; Christopher Jepson; Vyga Kaufmann (Psychopharmacology, 2004)

Analysis of costimulation by 4-1BBL, CD40L, and B7 in graft rejection by gene expression profiles - Rachel DeFina; Kenneth Christopher; Hongzhen He; Didier Mandelbrot; Yongping Gu; Patricia Finn; David L. Perkins (Journal of Molecular Medicine, 2003)

Sex differences in the subjective and reinforcing effects of cigarette nicotine dose - Kenneth Perkins; Lynette Jacobs; Mark Sanders; Anthony Caggiula (Psychopharmacology, 2002)

Environmental stimuli promote the acquisition of nicotine self-administration in rats - Anthony Caggiula; Eric Donny; Anthony White; Nadia Chaudhri; Sheri Booth; Maysa Gharib; Alycia Hoffman; Kenneth Perkins; Alan Sved (Psychopharmacology, 2002)

Quitting cigarette smoking produces minimal loss of chronic tolerance to nicotine - Kenneth Perkins; Debra Gerlach; Michelle Broge; Mark Sanders; James Grobe; Carolyn Fonte; Christine Cherry; Annette Wilson; Rolf Jacob (Psychopharmacology, 2001)

Threshold doses for nicotine discrimination in smokers and non-smokers - Kenneth A. Perkins; Carolyn Fonte; Mark Sanders; Jennifer Meeker; Annette Wilson (Psychopharmacology, 2001)

Effects of central and peripheral nicotinic blockade on human nicotine discrimination - Kenneth A. Perkins; Mark Sanders; Carolyn Fonte; Annette S. Wilson; Wendy White; Richard Stiller; Dennis McNamara (Psychopharmacology, 1999)