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There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill\u2010posed results that are sometimes inconsistent with the essential neurological principles.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction\u2010diffusion model, where we consider non\u2010linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long\u2010term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our stratification achieves superior performance in both inter\u2010cluster heterogeneity and intra\u2010cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed PSSN (i) reduces neuroimage data to low\u2010dimensional feature vectors, (ii) combines AT[N]\u2010Net based on real pathological pathways, (iii) predicts long\u2010term biomarker trajectories, and (iv) stratifies subjects into fine\u2010grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre\u2010symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16655","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T08:55:10Z","timestamp":1690793710000},"page":"1190-1202","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Pathology steered stratification network for subtype identification in Alzheimer's disease"],"prefix":"10.1002","volume":"51","author":[{"given":"Enze","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Computer Science Wake Forest University Winston\u2010Salem North Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science Wake Forest University 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