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To date, little is known about the associations between features from brain-imaging and individual Alzheimer\u2019s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer\u2019s Disease Assessment Scale\u2013Cognitive Subscale (ADAS\u2013Cog13) based on MRI and FDG\u2013PET brain-imaging data. Analysis of the trained network showed that each key ADAS\u2013Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG\u2013PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG\u2013PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.<\/jats:p>","DOI":"10.1186\/s40708-024-00218-x","type":"journal-article","created":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T11:02:18Z","timestamp":1707044538000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer\u2019s disease sub-scores"],"prefix":"10.1186","volume":"11","author":[{"given":"Kaida","family":"Ning","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascale B.","family":"Cannon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Srinesh","family":"Shenoi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joydeep","family":"Sarkar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"218_CR1","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s00401-011-0826-y","volume":"121","author":"PT Nelson","year":"2011","unstructured":"Nelson PT et al (2011) Alzheimer\u2019s disease is not \u201cbrain aging\u201d: neuropathological, genetic, and epidemiological human studies. 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