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Multivariate classification of earthquake survivors with post‐traumatic stress disorder based on large‐scale brain networks
Author(s) -
Zhu H.,
Yuan M.,
Qiu C.,
Ren Z.,
Li Y.,
Wang J.,
Huang X.,
Lui S.,
Gong Q.,
Zhang W.,
Zhang Y.
Publication year - 2020
Publication title -
acta psychiatrica scandinavica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.849
H-Index - 146
eISSN - 1600-0447
pISSN - 0001-690X
DOI - 10.1111/acps.13150
Subject(s) - default mode network , psychology , functional magnetic resonance imaging , multivariate statistics , task positive network , multivariate analysis , neuroscience , psychiatry , medicine , computer science , machine learning
Objective The identification of post‐traumatic stress disorder (PTSD) among natural disaster survivors is remarkably challenging, and there are no reliable objective signatures that can be used to assist clinical diagnosis and optimize treatment. The current study aimed to establish a neurobiological signature of PTSD from the connectivity of large‐scale brain networks and clarify the brain network mechanisms of PTSD. Methods We examined fifty‐seven unmedicated survivors with chronic PTSD and 59 matched trauma‐exposed healthy controls (TEHCs) using resting‐state functional magnetic resonance imaging (rs‐fMRI). We extracted the node‐to‐network connectivity and obtained a feature vector with a dimensionality of 864 (108 nodes × 8 networks) to represent each subject’s functional connectivity (FC) profile. Multivariate pattern analysis with a relevance vector machine was then used to distinguish PTSD patients from TEHCs. Results 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. Conclusions This study achieved relatively high accuracy in classifying PTSD patients vs. TEHCs at the individual level. This performance demonstrates that rs‐fMRI‐derived multivariate classification based on large‐scale 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.