{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T16:04:52Z","timestamp":1779811492883,"version":"3.53.1"},"posted":{"date-parts":[[2026,5,23]]},"group-title":"Neuroscience","reference-count":149,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Accurate in vivo prediction of neuropathology is critical for advancing diagnosis and treatment of Alzheimer\u2019s disease and related dementias (ADRDs). As many individuals with ADRDs have mixed pathologies (\u03b2-amyloid, pathologic tau, cerebrovascular disease, vascular brain injury, pathologic TDP-43, hippocampal sclerosis, Lewy bodies), there is interest in determining how accurately we can infer these pathologic changes from clinical data, biofluid assays (e.g., CSF), and neuroimaging. Here we evaluated automated machine learning models trained on data curated by the AD Sequencing Project Phenotype Harmonization Consortium (N=7,894 individuals), to predict 26 autopsy-confirmed neuropathological outcomes. Predictors included in vivo clinical and cognitive composite scores, brain measures from 3D structural MRI and diffusion tensor imaging, image-derived measures of white matter hyperintensities (WMH), and CSF biomarkers. Predictive models were trained using ensemble learning with stratified cross-validation. We assessed performance using Spearman\u2019s rank correlation and Matthews correlation coefficient, to accommodate co-occurring pathologic changes. The added value of neuroimaging and CSF versus clinical features alone was quantified. Braak stage was among the most consistently predicted outcomes. CSF biomarkers best predicted \u03b2-amyloid and tau pathology, but diffusion MRI metrics best captured vascular brain injury and white matter injury, and outperformed clinical and cognitive measures and anatomical MRI in predicting Lewy body disease. Anatomical measures from structural MRI outperformed standard clinical assessments in assessing neurodegeneration and hippocampal sclerosis, and WMH complemented cognitive measures in predicting TDP-43 pathology. These results establish a baseline for comparing modalities for inferring neuropathology.<\/jats:p>","DOI":"10.64898\/2026.05.20.726332","type":"posted-content","created":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T18:45:14Z","timestamp":1779561914000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3674-9788","authenticated-orcid":false,"given":"Christopher","family":"Patterson","sequence":"first","affiliation":[{"name":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamoghna","family":"Chattopadhyay","sequence":"additional","affiliation":[{"name":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0046-4070","authenticated-orcid":false,"given":"Sophia I.","family":"Thomopoulos","sequence":"additional","affiliation":[{"name":"Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1376-8532","authenticated-orcid":false,"given":"Andrew J.","family":"Saykin","sequence":"additional","affiliation":[{"name":"Indiana Alzheimer\u2019s Disease Research Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, 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University of California, San Francisco, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7061-6332","authenticated-orcid":false,"given":"Gary W.","family":"Beecham","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7501-7731","authenticated-orcid":false,"given":"Sarah A.","family":"Biber","sequence":"additional","affiliation":[{"name":"National Alzheimer\u2019s Coordinating Center, University of Washington, Seattle, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8761-9014","authenticated-orcid":false,"given":"Walter A.","family":"Kukull","sequence":"additional","affiliation":[{"name":"Department of Neurology, Washington University, St Louis, 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