{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T23:24:09Z","timestamp":1783812249913,"version":"3.55.0"},"reference-count":60,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Early detection of post\u2010traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting\u2010state functional magnetic resonance imaging (rs\u2010fMRI). The rs\u2010fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs\u2010fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K\u2010nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs\u2010fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.<\/jats:p>","DOI":"10.1002\/jemt.24065","type":"journal-article","created":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T03:59:40Z","timestamp":1643342380000},"page":"2083-2094","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Machine learning for <scp>post\u2010traumatic<\/scp> stress disorder identification utilizing <scp>resting\u2010state<\/scp> functional magnetic resonance imaging"],"prefix":"10.1002","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6718-3866","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"first","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab (AIDA), CCIS Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3817-2655","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab (AIDA), CCIS Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirza Naveed","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Gujrat  Gujrat Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rabia","family":"Latif","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab (AIDA), CCIS Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed Ali","family":"Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department College of Business Administration Prince Sattam bin Abdulaziz University  Alkharj 11942 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaber","family":"Alyami","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Faculty of Applied Medical Science King Abdulaziz University  Jeddah 21589 Saudi Arabia"},{"name":"Imaging Unit, King Fahd Medical Research Center King Abdulaziz University  Jeddah 21589 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,1,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2474-6"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-7324-y"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-019-01337-2"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/pubmed\/fdz026"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.35940\/ijitee.C8170.019320"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2018.00516"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psychres.2021.113712"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11920-020-1140-y"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-80776-2"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1192\/bjp.bp.114.148486"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.2147\/NDT.S202418"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1475-6773.2005.00404.x"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291720001336"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22998"},{"issue":"11","key":"e_1_2_10_16_1","first-page":"485","article-title":"Functional neuroimaging studies in posttraumatic stress disorder: Review of current methods and findings","volume":"4","author":"Francati V.","year":"2015","journal-title":"International Journal of Current Advanced Research"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1063736"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2018.11.003"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1586\/ern.10.198"},{"issue":"8","key":"e_1_2_10_20_1","first-page":"3451","article-title":"An evolution based hybrid approach for heart diseases classification and associated risk factors identification","volume":"28","author":"Iftikhar S.","year":"2017","journal-title":"Biomedical Research"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23281"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194526"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.22867"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1111\/psyp.13357"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0168315"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0032766"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6743489"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1515\/jisys-2013-0010"},{"key":"e_1_2_10_29_1","doi-asserted-by":"crossref","unstructured":"Nazir M. Khan M. A. Saba T. &Rehman A.(2019 April).Brain tumor detection from MRI images using multi\u2010level wavelets. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1\u20135). IEEE.","DOI":"10.1109\/ICCISci.2019.8716413"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291718002866"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brat.2021.103920"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2006.07.005"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107298"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp2008017"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23429"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23597"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiph.2020.06.033"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.2174\/1573405613666170912164546"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.09.015"},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.1002\/jts.22384"},{"key":"e_1_2_10_41_1","doi-asserted-by":"crossref","unstructured":"Sen B. Bernstein G. A. Xu T. Mueller B. A. Schreiner M. W. Cullen K. R. &Parhi K. K.(2016 August).Classification of obsessive\u2010compulsive disorder from resting\u2010state fMRI. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 3606\u20133609). IEEE.","DOI":"10.1109\/EMBC.2016.7591508"},{"key":"e_1_2_10_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3098453"},{"key":"e_1_2_10_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118242"},{"key":"e_1_2_10_44_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-021-01324-8"},{"key":"e_1_2_10_45_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.00588"},{"key":"e_1_2_10_46_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-016-0957-8"},{"key":"e_1_2_10_47_1","doi-asserted-by":"crossref","unstructured":"Sun X. &Park J.(2015 October).fMRI classification based on analysis of variance combined with support vector machine. In 2015 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 545\u2013547). IEEE.","DOI":"10.1109\/ICTC.2015.7354606"},{"key":"e_1_2_10_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2017.04.042"},{"key":"e_1_2_10_49_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24242"},{"key":"e_1_2_10_50_1","doi-asserted-by":"publisher","DOI":"10.31782\/IJCRR.2021.SP173"},{"key":"e_1_2_10_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bandc.2012.10.002"},{"key":"e_1_2_10_52_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00743"},{"key":"e_1_2_10_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2013.04.032"},{"key":"e_1_2_10_54_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7151475"},{"key":"e_1_2_10_55_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2018.00532"},{"key":"e_1_2_10_56_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-00879-2"},{"key":"e_1_2_10_57_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40535-016-0025-y"},{"key":"e_1_2_10_58_1","doi-asserted-by":"publisher","DOI":"10.2147\/JPR.S177502"},{"key":"e_1_2_10_59_1","doi-asserted-by":"publisher","DOI":"10.1177\/0284185114537927"},{"key":"e_1_2_10_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2018.02.918"},{"key":"e_1_2_10_61_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-019-0663-7"}],"container-title":["Microscopy Research and Technique"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jemt.24065","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jemt.24065","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jemt.24065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T18:42:50Z","timestamp":1760640170000},"score":1,"resource":{"primary":{"URL":"https:\/\/analyticalsciencejournals.onlinelibrary.wiley.com\/doi\/10.1002\/jemt.24065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,28]]},"references-count":60,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["10.1002\/jemt.24065"],"URL":"https:\/\/doi.org\/10.1002\/jemt.24065","archive":["Portico"],"relation":{},"ISSN":["1059-910X","1097-0029"],"issn-type":[{"value":"1059-910X","type":"print"},{"value":"1097-0029","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,28]]},"assertion":[{"value":"2020-12-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}