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We propose a unified statistical method, called Sparse Multi\u2010task Adaptive Regularization Truncation (SMART), to simultaneously address three critical challenges: (1)\n                    <jats:italic>High dimensionality<\/jats:italic>\n                    is resolved through an \u2010penalty () that selects sparse, stage\u2010distinct functional connections; (2)\n                    <jats:italic>Disease\u2010stage heterogeneity<\/jats:italic>\n                    is accommodated via an \u2010penalty () that maintains stable activation patterns across tasks; (3)\n                    <jats:italic>Connection collinearity<\/jats:italic>\n                    is mitigated using a truncated  penalty (TLP; ) that adaptively groups edges with similar cross\u2010task profiles without pre\u2010specified structure. SMART offers key advantages over existing methods: Its joint regularization naturally handles smooth activation patterns across stages, while the TLP's dual parameters ( for adaptive grouping threshold,  for sparsity control) provide a principled trade\u2010off between biological fidelity and model complexity. Computationally, we develop a DC\u2010ADMM algorithm that transforms the optimization into tractable subproblems, establishing finite\u2010step convergence to KKT points. Comprehensive simulation studies and real\u2010data analysis of AD neuroimaging data demonstrate SMART's superior accuracy in connectivity biomarker identification, enhanced stability in feature selection, and improved interpretability for AD cohort studies. An accompanying R package, SMART, is available on GitHub (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/LDstat\/SMART\">https:\/\/github.com\/LDstat\/SMART<\/jats:ext-link>\n                    ).\n                  <\/jats:p>","DOI":"10.1002\/sim.70526","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T11:12:11Z","timestamp":1776165131000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised Sparse Multi\u2010Task Learning With Application to Alzheimer's Disease"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8739-289X","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Cheeloo College of Medicine Shandong University  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3562-8861","authenticated-orcid":false,"given":"Jiadong","family":"Ji","sequence":"additional","affiliation":[{"name":"Institute for Financial Studies Shandong University  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1001-9433","authenticated-orcid":false,"given":"Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Statistics &amp; Data Science National University of Singapore  Singapore Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bofeng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Mathematics Shandong University  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011211"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.09.015"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx105"},{"key":"e_1_2_12_5_1","unstructured":"J.Liu S.Ji andJ.Ye \u201cMulti\u2010Task Feature Learning via Efficient l2 1\u2010Norm Minimization \u201darXiv 2009."},{"issue":"1","key":"e_1_2_12_6_1","first-page":"2979","article-title":"Multi\u2010Stage Multi\u2010Task Feature Learning","volume":"14","author":"Gong P.","year":"2013","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_12_7_1","first-page":"595","volume-title":"Proceedings of the 29th International Conference on Machine Learning","author":"Lozano A. 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