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The current study aimed to establish a neurobiological signature of PTSD from the connectivity of large\u2010scale brain networks and clarify the brain network mechanisms of PTSD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We examined fifty\u2010seven unmedicated survivors with chronic PTSD and 59 matched trauma\u2010exposed healthy controls (TEHCs) using resting\u2010state functional magnetic resonance imaging (rs\u2010fMRI). We extracted the node\u2010to\u2010network connectivity and obtained a feature vector with a dimensionality of 864 (108 nodes\u00a0\u00d7\u00a08 networks) to represent each subject\u2019s functional connectivity (FC) profile. Multivariate pattern analysis with a relevance vector machine was then used to distinguish PTSD patients from TEHCs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We achieved a promising diagnostic accuracy of 89.2% in distinguishing PTSD patients from TEHCs. The most heavily weighted connections for PTSD classification were among the default mode network (DMN), visual network (VIS), somatomotor network, limbic network, and dorsal attention network (DAN). The strength of the anticorrelation of FC between the ventral medial prefrontal cortex (vMPFC) in DMN and the VIS and DAN was associated with the severity of PTSD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study achieved relatively high accuracy in classifying PTSD patients vs. TEHCs at the individual level. This performance demonstrates that rs\u2010fMRI\u2010derived multivariate classification based on large\u2010scale brain networks can provide potential signatures both to facilitate clinical diagnosis and to clarify the underlying brain network mechanisms of PTSD caused by natural disasters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/acps.13150","type":"journal-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T02:13:51Z","timestamp":1580350431000},"page":"285-298","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Multivariate classification of earthquake survivors with post\u2010traumatic stress disorder based on large\u2010scale brain networks"],"prefix":"10.1111","volume":"141","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8420-581X","authenticated-orcid":false,"given":"H.","family":"Zhu","sequence":"first","affiliation":[{"name":"Mental Health Center and Psychiatric 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