Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting‐state fMRI study

Abstract

Children exposed to natural disasters are vulnerable to the development of posttraumatic stress disorder (PTSD). Recent studies of other neuropsychiatric disorders have used graph-based theoretical analysis to investigate the topological properties of the functional brain connectome. However, little is known about this connectome in pediatric PTSD. Twenty-eight pediatric PTSD patients and 26 trauma-exposed non-PTSD patients were recruited from 4,200 screened subjects after the 2008 Sichuan earthquake to undergo a resting-state functional magnetic resonance imaging scan. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was then used to examine the group-specific topological properties of the two functional connectomes. Both the PTSD and non-PTSD control groups exhibited "small-world" brain network topology. However, the functional connectome of the PTSD group showed a significant increase in the clustering coefficient and a normalized characteristic path length and local efficiency, suggesting a shift toward regular networks. Furthermore, the PTSD connectomes showed both enhanced nodal centralities, mainly in the default mode- and salience-related regions, and reduced nodal centralities, mainly in the central-executive network regions. The clustering coefficient and nodal efficiency of the left superior frontal gyrus were positively correlated with the Clinician-Administered PTSD Scale. These disrupted topological properties of the functional connectome help to clarify the pathogenesis of pediatric PTSD and could be potential biomarkers of brain abnormalities.

Typhoon-Related Post-Traumatic Stress Disorder and Trauma Might Lead to Functional Integration Abnormalities in Intra- and Inter-Resting State Networks: a Resting-State Fmri Independent Component Analysis

“…Consistent with our study, Reuven et al reported that resting-state functional connectivity between the left SFG and DMN classical brain region increased as compared with the TEC group [31]. Furthermore, large-scale structural and functional connectivity research indicated that local parameters of the SFG in PTSD patients were higher than those in the HC and TEC groups [32,33]. Bluhm et al used resting-state fMRI to investigate childhood trauma-related PTSD.…”

Altered intrinsic functional brain architecture in female patients with bulimia nervosa

“…38 We found increased clustering and path length in women with bulimia nervosa, suggesting increased local but decreased global efficiency and a shift toward regular network, which has been characterized in several other psychiatric diseases. 39,40 Although the exact mechanism remains unclear, the regularized process has been associated with reduced signal transmission speed and coordination, 41 which may be compensated by higher local efficiency to preserve efficient communication. 42 Further analysis of regional and network measures revealed both increased and decreased patterns of nodal strength and iFC in women with bulimia nervosa.…”

Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency

“…The networks differed in the number of edges (i.e., correlation matrix) (Wen et al, 2011; Shen et al, 2015). Thus, we applied a range of sparsity thresholds, defined as the fraction of the total number of edges remaining in a network, so every graph had the same number of edges (Watts and Strogatz, 1998; Wen et al, 2011; Shen et al, 2015; Suo et al, 2015). The minimum sparsity was set so that the averaged node degree of the network with threshold was 2log( N ), where N was the number of nodes, and the small-worldness scalar of the network was >1.1 (Wen et al, 2011; Shen et al, 2015; Suo et al, 2015).…”

“…Thus, we applied a range of sparsity thresholds, defined as the fraction of the total number of edges remaining in a network, so every graph had the same number of edges (Watts and Strogatz, 1998; Wen et al, 2011; Shen et al, 2015; Suo et al, 2015). The minimum sparsity was set so that the averaged node degree of the network with threshold was 2log( N ), where N was the number of nodes, and the small-worldness scalar of the network was >1.1 (Wen et al, 2011; Shen et al, 2015; Suo et al, 2015). This thresholding strategy produced networks that could be used to estimate small-worldness with sparse properties and the minimum possible number of spurious edges (Wen et al, 2011; Shen et al, 2015; Suo et al, 2015).…”