{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T17:02:51Z","timestamp":1792515771091,"version":"4.5.0"},"reference-count":124,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":196,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Alzheimer\u2019s disease (AD), a widely studied neurodegenerative disorder, poses significant research challenges due to its high prevalence and complex etiology. Age, a critical risk factor for AD, is typically assessed by comparing physiological and estimated brain ages. This study utilizes mouse models expressing human alleles of APOE and human nitric oxide synthase 2 (hNOS2), replicating genetic risks for AD alongside a human-like immune response. We developed a multivariate model that incorporates brain structural connectomes, APOE genotypes, demographic traits (age and sex), environmental factors such as diet, and behavioral data to estimate brain age. Our methodology employs a Feature Attention Graph Neural Network (FAGNN) to integrate these diverse datasets. Behavioral data are processed using a 2D convolutional neural network (CNN), demographic traits via a 1D CNN, and brain connectomes through a graph neural network equipped with a quadrant attention module that accentuates critical neural connections. The FAGNN model demonstrated a mean absolute error in age prediction of 31.85 days and a root mean squared error of 41.84 days, significantly outperforming simpler models. Our analysis further focused on the brain age delta, which assesses accelerated or delayed aging by comparing brain age, predicted by FAGNN, to the chronological age. A high-fat diet and the presence of the human NOS2 gene were identified as significant accelerators of brain aging in the old age group. Key neural connections identified by FAGNN, such as those between the cingulum, corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex, were found to be significant in the aging process. Validation using diffusion MRI-based metrics, including fractional anisotropy and return-to-origin probability measures across these connections, revealed significant age-related differences. These findings suggest that white matter degradation in the connections highlighted by FAGNN plays a key role in aging. Our findings suggest that the complex interplay of APOE genotype with sex, immunity, and environmental factors modulates brain aging and enhance our understanding of AD risk in mouse models of aging.<\/jats:p>","DOI":"10.1162\/imag_a_00245","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T16:14:31Z","timestamp":1721060071000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":11,"title":["Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer\u2019s disease"],"prefix":"10.1162","volume":"2","author":[{"given":"Hae Sol","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, Durham, NC, United States"},{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Mahzarnia","sequence":"additional","affiliation":[{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacques","family":"Stout","sequence":"additional","affiliation":[{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert J.","family":"Anderson","sequence":"additional","affiliation":[{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zay Yar","family":"Han","sequence":"additional","affiliation":[{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica T.","family":"Tremblay","sequence":"additional","affiliation":[{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian T.","family":"Badea","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, Durham, NC, United States"},{"name":"Quantitative Imaging and Analysis Laboratory, Department of Radiology, Duke University School of Medicine, Durham, NC, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Badea","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Duke University, Durham, NC, United 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