Open AccessStatic and dynamic connectomics differentiate between depressed patients with and without suicidal ideation
Author(s) -
Liao Wei,
Li Jiao,
Duan Xujun,
Cui Qian,
Chen Heng,
Chen Huafu
Publication year - 2018
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24235
Subject(s) - connectomics , connectome , neuroscience , magnetic resonance imaging , functional magnetic resonance imaging , suicidal ideation , medicine , psychology , functional connectivity , poison control , radiology , injury prevention , environmental health
Neural circuit dysfunction underlies the biological mechanisms of suicidal ideation (SI). However, little is known about how the brain's “dynome” differentiate between depressed patients with and without SI. This study included depressed patients ( n = 48) with SI, without SI (NSI), and healthy controls (HC, n = 30). All participants underwent resting‐state functional magnetic resonance imaging. We constructed dynamic and static connectomics on 200 nodes using a sliding window and full‐length time–series correlations, respectively. Specifically, the temporal variability of dynamic connectomic was quantified using the variance of topological properties across sliding window. The overall topological properties of both static and dynamic connectomics further differentiated between SI and NSI, and also predicted the severity of SI. The SI showed decreased overall topological properties of static connectomic relative to the HC. The SI exhibited increases in overall topological properties with regard to the dynamic connectomic when compared with the HC and the NSI. Importantly, combining the overall topological properties of dynamic and static connectomics yielded mean 75% accuracy (all p < .001) with mean 71% sensitivity and mean 75% specificity in differentiating between SI and NSI. Moreover, these features may predict the severity of SI (mean r = .55, all p < .05). The findings revealed that combining static and dynamic connectomics could differentiate between SI and NSI, offering new insight into the physiopathological mechanisms underlying SI. Furthermore, combining the brain's connectome and dynome may be considered a neuromarker for diagnostic and predictive models in the study of SI.