Lisa Gallagher results 32
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Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegenerationNARA Subscribed
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 fi...
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 te...
Public stigma is a society’s negative attitude toward a person or groups of people, self-stigma relates to the internalisation of these negative attitudes, and affiliate stigma is experienced by those associated with a stigmatised group. Both autism and genetic testing results can be stigmatised. This scoping review explored relationships between genetic testing and stigma in autism, to understand any perceived positive or neg...
Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learningNARA Subscribed
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, th...
Contrasting intra-individual variation in size-based trophic and habitat shifts for two coastal Arctic fish speciesNARA Subscribed
Within and among species variation in trophic and habitat shifts with body size can indicate the potential adaptive capacity of species to ecosystem change. In Arctic coastal ecosystems, which experience dramatic seasonal shifts and are undergoing rapid change, quantifying the trophic flexibility of coastal fishes with different migratory tactics has received limited attention. We examined the relationships among body length a...
Evidence for three morphotypes among anadromous Arctic char ( Salvelinus alpinus ) sampled in the marine environmentNARA Subscribed
Variable resource use and responses to environmental conditions can lead to phenotypic diversity and distinct morphotypes within salmonids, including Arctic char ( Salvelinus alpinus ). Despite the cultural and economic importance of Arctic char in the Inuvialuit Settlement Region (ISR), limited data exist on the extent and presence of morphological diversity in this region. This is of concern for management given climate chan...
Given climate change threats to ecosystems, it is critical to understand the responses of species to warming. This is especially important in the case of apex predators since they exhibit relatively high extinction risk, and changes to their distribution could impact predator–prey interactions that can initiate trophic cascades. Here we used a combined analysis of animal tracking, remotely sensed environmental data, habitat mo...
The ocean plays a crucial role in the functioning of the Earth System and in the provision of vital goods and services. The United Nations (UN) declared 2021–2030 as the UN Decade of Ocean Science for Sustainable Development. The Roadmap for the Ocean Decade aims to achieve six critical societal outcomes (SOs) by 2030, through the pursuit of four objectives (Os). It specifically recognizes the scarcity of biological data for d...