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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...
Electronic cigarettes (e-cigs) are major contributors to inflammatory-mediated responses, which are implicated in a vast array of pathophysiological conditions, including cardiovascular disease (CVD). More recently e-cigs have been recognized as a source of thirdhand exposure (THEC); the process by which expelled toxins settle on materials (i.e., carpets, curtains, clothes etc.) and undergo chemical reactions, rendering them m...
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...
Tidal turbines have emerged as a promising alternative to fossil-fuel-based energy generation, with estuarine environments identified as potential sites for their deployment. However, estuaries are sensitive ecosystems, and understanding the impacts of turbine installation on local hydrodynamics and sediment transport is critical. While previous studies have shown the influence of turbines on seabed morphology under steady cur...