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

SNAPSHOT USA 2024: Year 6 of the Coordinated National Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Daniel J. Herrera; Camilla Price; Phuong‐Thao Nguyen‐Nu; Katrina Adamczyk; Peter D. Alexander; David Allen; Jesse M. Alston; Diego F. Alvarado‐Serrano; et al. (Global Ecology and Biogeography, 2026)
Motivation SNAPSHOT USA is an annual, multi‐contributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2024 contains 377,427 records of camera trap image sequence data and 3127 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in 49 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1 August and 19 December in 2024. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2024 comprises two .csv files. The original data can be found within the SNAPSHOT USA 2024 project on the Wildlife Insights platform.

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.

SNAPSHOT USA 2019–2023: The First Five Years of Data From a Coordinated Camera Trap Survey of the United States - Brigit Rooney; Roland Kays; Michael V. Cove; Alex Jensen; Benjamin R. Goldstein; Christopher Pate; Paula Castiblanco; Maggie E. Abell; Jessie Adley; Briana Agenbroad; Adam A. Ahlers; Peter D. Alexander; et al. (Global Ecology and Biogeography, 2025)
Motivation SNAPSHOT USA is an annual, multicontributor camera trap survey of mammals across the United States. The growing SNAPSHOT USA dataset is intended for tracking the spatial and temporal responses of mammal populations to changes in land use, land cover and climate. These data will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, as well as the impacts of species interactions on daily activity patterns. Main Types of Variables Contained SNAPSHOT USA 2019–2023 contains 987,979 records of camera trap image sequence data and 9694 records of camera trap deployment metadata. Spatial Location and Grain Data were collected across the United States of America in all 50 states, 12 ecoregions and many ecosystems. Time Period and Grain Data were collected between 1st August and 29th December each year from 2019 to 2023. Major Taxa and Level of Measurement The dataset includes a wide range of taxa but is primarily focused on medium to large mammals. Software Format SNAPSHOT USA 2019–2023 comprises two .csv files. The original data can be found within the SNAPSHOT USA Initiative in the Wildlife Insights platform.

Sprint cycling rate of torque development associates with strength measurement in trained cyclists - Shannon Connolly; Peter Peeling; Martyn J. Binnie; Paul S. R. Goods; Christopher Latella; Janet L. Taylor; Anthony J. Blazevich; Wouter P. Timmerman; Chris R. Abbiss (European Journal of Applied Physiology, 2023)
Purpose A cyclist’s rate of force/torque development (RFD/RTD) and peak force/torque can be measured during single-joint or whole-body isometric tests, or during cycling. However, there is limited understanding of the relationship between these measures, and of the mechanisms that contribute to each measure. Therefore, we examined the: (i) relationship between quadriceps central and peripheral neuromuscular function with RFD/RTD in isometric knee extension, isometric mid-thigh pull (IMTP), and sprint cycling; and (ii) relationship among RFD/RTD and peak force/torque between protocols. Methods Eighteen trained cyclists completed two familiarisation and two experimental sessions. Each session involved an isometric knee extension, IMTP, and sprint cycling protocol, where peak force/torque, average and peak RFD/RTD, and early (0–100 ms) and late (0–200 ms) RFD/RTD were measured. Additionally, measures of quadriceps central and peripheral neuromuscular function were assessed during the knee extension. Results Strong relationships were observed between quadriceps early EMG activity (EMG 50 /M) and knee extension RTD ( r or ρ = 0.51–0.65) and IMTP late RFD ( r = 0.51), and between cycling early or late RTD and peak twitch torque ( r or ρ = 0.70–0.75). Strong-to-very strong relationships were observed between knee extension, IMTP, and sprint cycling for peak force/torque, early and late RFD/RTD, and peak RFD/RTD ( r or ρ = 0.59–0.80). Conclusion In trained cyclists, knee extension RTD or IMTP late RFD are related to measures of quadriceps central neuromuscular function, while cycling RTD is related to measures of quadriceps peripheral neuromuscular function. Further, the strong associations among force/torque measures between tasks indicate a level of transferability across tasks.

Elephants never forget, should art museums remember too? Historic ivory collections as ambassadors for conservation education - Caroline Good; Peter Tyrrell; Zhaomin Zhou; David W. Macdonald (Biodiversity and Conservation, 2019)

Gene expression and neurochemical characterization of the rostromedial tegmental nucleus (RMTg) in rats and mice - Rachel J. Smith; Peter J. Vento; Ying S. Chao; Cameron H. Good; Thomas C. Jhou (Brain Structure and Function, 2019)

Tundra Trait Team: A database of plant traits spanning the tundra biome - Anne D. Bjorkman; Isla H. Myers‐Smith; Sarah C. Elmendorf; Signe Normand; Haydn J. D. Thomas; Juha M. Alatalo; Heather Alexander; Alba Anadon‐Rosell; Sandra Angers‐Blondin; Yang Bai; Gaurav Baruah; Mariska te Beest; et al. (Global Ecology and Biogeography, 2018)
Motivation The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database ( www.try-db.org ) to be included in the next version release.

Exploring uncertainty of Amazon dieback in a perturbed parameter Earth system ensemble - Chris A. Boulton; Ben B. B. Booth; Peter Good (Global Change Biology, 2017)
The future of the Amazon rainforest is unknown due to uncertainties in projected climate change and the response of the forest to this change (forest resiliency). Here, we explore the effect of some uncertainties in climate and land surface processes on the future of the forest, using a perturbed physics ensemble of Had CM 3C. This is the first time Amazon forest changes are presented using an ensemble exploring both land vegetation processes and physical climate feedbacks in a fully coupled modelling framework. Under three different emissions scenarios, we measure the change in the forest coverage by the end of the 21st century (the transient response) and make a novel adaptation to a previously used method known as “dry‐season resilience” to predict the long‐term committed response of the forest, should the state of the climate remain constant past 2100. Our analysis of this ensemble suggests that there will be a high chance of greater forest loss on longer timescales than is realized by 2100, especially for mid‐range and low emissions scenarios. In both the transient and predicted committed responses, there is an increasing uncertainty in the outcome of the forest as the strength of the emissions scenarios increases. It is important to note however, that very few of the simulations produce future forest loss of the magnitude previously shown under the standard model configuration. We find that low optimum temperatures for photosynthesis and a high minimum leaf area index needed for the forest to compete for space appear to be precursors for dieback. We then decompose the uncertainty into that associated with future climate change and that associated with forest resiliency, finding that it is important to reduce the uncertainty in both of these if we are to better determine the Amazon's outcome.

Whole exome sequencing of wild-derived inbred strains of mice improves power to link phenotype and genotype - Peter L. Chang; Emily Kopania; Sara Keeble; Brice A. J. Sarver; Erica Larson; Annie Orth; Khalid Belkhir; Pierre Boursot; François Bonhomme; Jeffrey M. Good; Matthew D. Dean (Mammalian Genome, 2017)

Non-linear interactions between $$\hbox {CO}_2$$ CO 2 radiative and physiological effects on Amazonian evapotranspiration in an Earth system model - Kate Halladay; Peter Good (Climate Dynamics, 2017)

Are strong fire–vegetation feedbacks needed to explain the spatial distribution of tropical tree cover? - Peter Good; Anna Harper; Antoon Meesters; Eddy Robertson; Richard Betts (Global Ecology and Biogeography, 2016)
Aim The spatial pattern of tropical fire‐induced tree mortality is partly determined by climate, but feedbacks of tree cover on fire are also important. We re‐examine some recent observations proposed as evidence for very strong tree‐cover feedbacks on fire, sufficient to allow savanna and forest to be alternative stable states over large areas of the tropics. Two pieces of previously reported observational evidence are examined: (1) the trimodal statistical distribution of tropical tree fraction, and (2) the fact that different tree fractional cover is found at different locations in the tropics with similar rainfall. Location Global tropics. Methods For point (1) above we analyse the statistical distribution of tree fraction predicted by the logistic equation of tree growth and self‐competition, with spatially varying mortality rates. For (2), the relationship between mean annual rainfall and mean net primary productivity (NPP) in a climate model is examined. Results (1) A trimodal distribution of tree cover does not necessarily require tree‐cover feedback on fire. It can arise from a combination of two factors: nonlinearities in vegetation dynamics and climate‐driven spatial variation in mortality (the intermediate fire–productivity hypothesis). (2) Different locations in the tropics can have identical rainfall but significantly different NPP, even with no feedback of tree cover on fire. Main conclusions Our results show that strong tree‐cover feedback on fire is not necessary to explain observations (1) and (2). However, it is still possible that strong fire–vegetation feedback is the primary explanation – our results do not rule this out. We simply demonstrate the possibility of an alternative hypothesis (of strong climate control). In reality, it is likely that both tree‐cover feedback and climate contribute. It is challenging to separate these two effects cleanly. More work is needed to quantify their separate effects. We show that plots of mortality versus productivity are useful tools for understanding spatial variations in tree cover.

Seasonality and facilitation drive tree establishment in a semi-arid floodplain savanna - Megan K. Good; Peter J. Clarke; Jodi N. Price; Nick Reid (Oecologia, 2014)

Early warning signals of simulated Amazon rainforest dieback - Chris A. Boulton; Peter Good; Timothy M. Lenton (Theoretical Ecology, 2013)

Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections - Peter Good; Jonathan M. Gregory; Jason A. Lowe; Timothy Andrews (Climate Dynamics, 2013)

Asymmetries in tropical rainfall and circulation patterns in idealised CO2 removal experiments - Robin Chadwick; Peili Wu; Peter Good; Timothy Andrews (Climate Dynamics, 2013)

A step-response approach for predicting and understanding non-linear precipitation changes - Peter Good; William Ingram; F. Hugo Lambert; Jason A. Lowe; Jonathan M. Gregory; Mark J. Webb; Mark A. Ringer; Peili Wu (Climate Dynamics, 2012)

Does Fever Relieve Autistic Behavior by Improving Brain Blood Flow? - Peter Good (Neuropsychology Review, 2011)

Understanding uncertainty in future projections for the tropical Atlantic: relationships with the unforced climate - Peter Good; Jason A. Lowe; David P. Rowell (Climate Dynamics, 2009)

Synthesis and Immunological Evaluation of M Protein Targeted Tetra-Valent and Tri-Valent Group A Streptococcal Vaccine Candidates Based on the Lipid-Core Peptide System - Peter Michael Moyle; Colleen Olive; Levente Karpati; Nadia Barozzi; Mei-Fong Ho; Joanne Dyer; Hsien Kuo Sun; Michael Good; Istvan Toth (International Journal of Peptide Research and Therapeutics, 2006)

Insect herbivory stimulates allelopathic exudation by an invasive plant and the suppression of natives - Giles C. Thelen; Jorge M. Vivanco; Beth Newingham; William Good; Harsh P. Bais; Peter Landres; Anthony Caesar; Ragan M. Callaway (Ecology Letters, 2005)
Exotic invasive plants are often subjected to attack from imported insects as a method of biological control. A fundamental, but rarely explicitly tested, assumption of biological control is that damaged plants are less fit and compete poorly. In contrast, we find that one of the most destructive invasive plants in North America, Centaurea maculosa , exudes far higher amounts of (±)‐catechin, an allelopathic chemical known to have deleterious effects on native plants, when attacked by larvae of two different root boring biocontrol insects and a parasitic fungus. We also demonstrate that C. maculosa plants experimentally attacked by one of these biocontrols exhibit more intense negative effects on natives.