{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T02:10:03Z","timestamp":1792203003238,"version":"4.4.0"},"reference-count":73,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2021,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Resting\u2010state fMRI (rs\u2010fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs\u2010fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on \u201cnodes\u201d and \u201cedges\u201d together with structural MRI\u2010based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)\/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML\u2010based image interpretation of rs\u2010fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC\/MCI to AD.<\/jats:p>","DOI":"10.1002\/hbm.25369","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T03:51:44Z","timestamp":1620100304000},"page":"2941-2968","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":255,"title":["Diagnostic power of resting\u2010state\n                    <scp>fMRI<\/scp>\n                    for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review"],"prefix":"10.1002","volume":"42","author":[{"given":"Buhari","family":"Ibrahim","sequence":"first","affiliation":[{"name":"Department of Radiology, Faculty of Medicine and Health Sciences Universiti Putra Malaysia  Serdang Selangor Malaysia"},{"name":"Department of Physiology, Faculty of Basic Medical Sciences Bauchi State University Gadau  Gadau Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2495-6408","authenticated-orcid":false,"given":"Subapriya","family":"Suppiah","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine and Health Sciences Universiti Putra Malaysia  Serdang Selangor Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Normala","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Faculty of Medicine and Health Sciences Universiti Putra Malaysia  Serdang Selangor Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mazlyfarina","family":"Mohamad","sequence":"additional","affiliation":[{"name":"Centre for Diagnostic and Applied Health Sciences, Faculty of Health Sciences Universiti Kebangsaan Malaysia  Kuala Lumpur Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hasyma Abu","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine and Health Sciences Universiti Putra Malaysia  Serdang Selangor Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nisha Syed","family":"Nasser","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Medicine and Health Sciences Universiti Putra Malaysia  Serdang Selangor Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M Iqbal","family":"Saripan","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication System Engineering Universiti Putra Malaysia  Serdang Selangor Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2011.03.008"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dadm.2017.03.007"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2008.10.017"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pscychresns.2013.10.010"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2377-12-33"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194479"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.5698-11.2012"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.5062-08.2009"},{"issue":"3","key":"e_1_2_10_11_1","first-page":"154","article-title":"Diagnostic test accuracy: Methods for systematic review and meta\u2010analysis","volume":"13","author":"Campbell J. 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