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However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study,\u00a0we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution\u00a0vector underlying spatial pathological aging mechanism. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model\u2010agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder\u2010specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.<\/jats:p>","DOI":"10.1002\/hbm.26066","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T11:51:39Z","timestamp":1662983499000},"page":"5017-5031","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity"],"prefix":"10.1002","volume":"43","author":[{"given":"Chen","family":"Ran","sequence":"first","affiliation":[{"name":"Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7547-4580","authenticated-orcid":false,"given":"Yanwu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen  Shenzhen China"},{"name":"Peng Cheng Laboratory  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenfei","family":"Ye","sequence":"additional","affiliation":[{"name":"International Research Institute for Artificial Intelligence Harbin Institute of Technology at Shenzhen  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Lv","sequence":"additional","affiliation":[{"name":"MindsGo Shenzhen Life Science Co. 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