{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T06:28:54Z","timestamp":1793687334307,"version":"4.5.1"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81973154"],"award-info":[{"award-number":["81973154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020771","name":"Natural Science Foundation for Young Scientists of Shanxi Province","doi-asserted-by":"publisher","award":["201901D211330"],"award-info":[{"award-number":["201901D211330"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020771","name":"Natural Science Foundation for Young Scientists of Shanxi Province","doi-asserted-by":"publisher","award":["202103021223242"],"award-info":[{"award-number":["202103021223242"]}],"id":[{"id":"10.13039\/501100020771","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:p>Due to the clinical continuum of Alzheimer\u2019s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.<\/jats:p>","DOI":"10.3389\/fnagi.2022.935055","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T06:34:18Z","timestamp":1660113258000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Hierarchical multi-class Alzheimer\u2019s disease diagnostic framework using imaging and clinical features"],"prefix":"10.3389","volume":"14","author":[{"given":"Yao","family":"Qin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyan","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuling","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanhong","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.jalz.2010.03.006","article-title":"Clinical core of the Alzheimer\u2019s disease neuroimaging initiative: Progress and plans.","volume":"6","author":"Aisen","year":"2010","journal-title":"Alzheimers Dement."},{"key":"B2","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1016\/j.jalz.2015.05.005","article-title":"Alzheimer\u2019s disease neuroimaging initiative 2 clinical core: Progress and plans.","volume":"11","author":"Aisen","year":"2015","journal-title":"Alzheimers Dement."},{"key":"B3","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.bspc.2018.02.019","article-title":"Multi-class Alzheimer\u2019s disease classification using image and clinical features.","volume":"43","author":"Altaf","year":"2018","journal-title":"Biomed. 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