{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:19:20Z","timestamp":1785284360561,"version":"3.55.0"},"reference-count":40,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2024,1,28]],"date-time":"2024-01-28T00:00:00Z","timestamp":1706400000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82001898"],"award-info":[{"award-number":["82001898"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82001896"],"award-info":[{"award-number":["82001896"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J of Neuroscience"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Early diagnosis is crucial to slowing the progression of Alzheimer's disease (AD), so it is urgent to find an effective diagnostic method for AD. This study intended to investigate whether the transfer learning approach of deep Q\u2010network (DQN) could effectively distinguish AD patients using local metrics of resting\u2010state functional magnetic resonance imaging (rs\u2010fMRI) as features. This study included 1310 subjects from the Consortium for Reliability and Reproducibility (CoRR) and 50 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) GO\/2. The amplitude of low\u2010frequency fluctuation (ALFF), fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) were extracted as features using the Power 264 atlas. Based on gender bias in AD, we searched for transferable similar parts between the CoRR feature matrix and the ADNI feature matrix, resulting in the CoRR similar feature matrix served as the source domain and the ADNI similar feature matrix served as the target domain. A DQN classifier was pre\u2010trained in the source domain and transferred to the target domain. Finally, the transferred DQN classifier was used to classify AD and healthy controls (HC). A permutation test was performed. The DQN transfer learning achieved a classification accuracy of 86.66% (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.01), recall of 83.33% and precision of 83.33%. The findings suggested that the transfer learning approach using DQN could be an effective way to distinguish AD from HC. It also revealed the potential value of local brain activity in AD clinical diagnosis.\n                  <\/jats:p>","DOI":"10.1111\/ejn.16261","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:29:16Z","timestamp":1706488156000},"page":"2118-2127","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Classification of Alzheimer's disease: application of a transfer learning deep Q\u2010network method"],"prefix":"10.1111","volume":"59","author":[{"given":"Huibin","family":"Ma","sequence":"first","affiliation":[{"name":"School of Information and Electronics Technology Jiamusi University  Jiamusi China"},{"name":"Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province  Jiamusi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yadan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics Technology Jiamusi University  Jiamusi China"},{"name":"Key Laboratory of Autonomous Intelligence and Information Processing in Heilongjiang Province  Jiamusi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeqi","family":"Hao","sequence":"additional","affiliation":[{"name":"School of Psychology Zhejiang Normal University  Jinhua China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Psychiatry the second affiliated hospital of Zhejiang University school of Medicine  Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4698-8723","authenticated-orcid":false,"given":"Xize","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Radiology, Changshu No. 2 People's Hospital The Affiliated Changshu Hospital of Xuzhou Medical University  Changshu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengting","family":"Li","sequence":"additional","affiliation":[{"name":"School of Psychology Zhejiang Normal University  Jinhua China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lanfen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Medical Imaging Weifang Medical University  Weifang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,28]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.02.019"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2019.00976"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2018.01646"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2973324"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1017\/S135561771700100X"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules25245789"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2022.03.079"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.025"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23147"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910350312"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhab394"},{"key":"e_1_2_11_13_1","doi-asserted-by":"crossref","unstructured":"Gautheron L. Redko I. &Lartizien C.(2019).Feature selection for unsupervised domain adaptation using optimal transport. Paper presented at the Machine Learning and Knowledge Discovery in Databases: European Conference ECML PKDD 2018 Dublin Ireland September 10\u201314 2018 Proceedings Part II 18.","DOI":"10.1007\/978-3-030-10928-8_45"},{"key":"e_1_2_11_14_1","doi-asserted-by":"crossref","unstructured":"Golland P. &Fischl B.(2003).Permutation tests for classification: Towards statistical significance in image\u2010based studies. 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