{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T19:55:35Z","timestamp":1792266935732,"version":"4.4.0"},"reference-count":112,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T00:00:00Z","timestamp":1733702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropathology-based, data-driven, multi-label deep-learning framework to identify and quantify in vivo biomarkers for Alzheimer\u2019s disease (AD), vascular dementia (VD) and Lewy body dementia (LBD) using antemortem T1-weighted MRI scans of 423 demented and 361 control participants from National Alzheimer\u2019s Coordinating Center and Alzheimer\u2019s Disease Neuroimaging Initiative datasets. Based on the best-performing deep-learning model, explainable heat maps were extracted to visualize disease patterns, and the novel Deep Signature of Pathology Atrophy REcognition (DeepSPARE) indices were developed, where a higher DeepSPARE score indicates more brain alterations associated with that specific pathology.<\/jats:p>\n                  <jats:p>A substantial discrepancy in clinical and neuropathological diagnosis was observed in the demented patients: 71% had more than one pathology, but 67% were diagnosed clinically as AD only. Based on these neuropathological diagnoses and leveraging cross-validation principles, the deep-learning model achieved the best performance, with a balanced accuracy of 0.844, 0.839 and 0.623 for AD, VD and LBD, respectively, and was used to generate the explainable deep-learning heat maps and DeepSPARE indices.<\/jats:p>\n                  <jats:p>The explainable deep-learning heat maps revealed distinct neuroimaging brain alteration patterns for each pathology: (i) the AD heat map highlighted bilateral hippocampal regions; (ii) the VD heat map emphasized white matter regions; and (iii) the LBD heat map exposed occipital alterations. The DeepSPARE indices were validated by examining their associations with cognitive testing and neuropathological and neuroimaging measures using linear mixed-effects models. The DeepSPARE-AD index was associated with Mini-Mental State Examination, the Trail Making Test B, memory, hippocampal volume, Braak stages, Consortium to Establish a Registry for Alzheimer's Disease (CERAD) scores and Thal phases [false-discovery rate (FDR)-adjusted P &amp;lt; 0.05]. The DeepSPARE-VD index was associated with white matter hyperintensity volume and cerebral amyloid angiopathy (FDR-adjusted P &amp;lt; 0.001), and the DeepSPARE-LBD index was associated with Lewy body stages (FDR-adjusted P &amp;lt; 0.05).<\/jats:p>\n                  <jats:p>The findings were replicated in an out-of-sample Alzheimer\u2019s Disease Neuroimaging Initiative dataset by testing associations with cognitive, imaging, plasma and CSF measures. CSF and plasma tau phosphorylated at threonine-181 (pTau181) were significantly associated with DeepSPARE-AD in the AD and mild cognitive impairment amyloid-\u03b2 positive (AD\/MCI\u0391\u03b2+) group (FDR-adjusted P &amp;lt; 0.001), and CSF \u03b1-synuclein was associated solely with DeepSPARE-LBD (FDR-adjusted P = 0.036).<\/jats:p>\n                  <jats:p>Overall, these findings demonstrate the advantages of our innovative deep-learning framework in detecting antemortem neuroimaging signatures linked to different pathologies. The newly deep-learning-derived DeepSPARE indices are precise, pathology-sensitive and single-valued non-invasive neuroimaging metrics, bridging the traditional widely available in vivo T1 imaging with histopathology.<\/jats:p>","DOI":"10.1093\/brain\/awae388","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T15:25:34Z","timestamp":1732548334000},"page":"1963-1977","source":"Crossref","is-referenced-by-count":25,"title":["Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer\u2019s, vascular and Lewy body dementias"],"prefix":"10.1093","volume":"148","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0460-1463","authenticated-orcid":false,"given":"Di","family":"Wang","sequence":"first","affiliation":[{"name":"Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio , San Antonio, TX 78229 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University of Texas Health Science Center at San Antonio , San Antonio, TX 78229 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6578-393X","authenticated-orcid":false,"given":"Sokratis","family":"Charisis","sequence":"additional","affiliation":[{"name":"Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio , San Antonio, TX 78229 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanweer","family":"Rashid","sequence":"additional","affiliation":[{"name":"Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio , San Antonio, TX 78229 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anoop","family":"Benet 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