Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder.



Suo, X, Lei, D, Chen, F, Wu, M, Li, L, Sun, L, Wei, X, Zhu, H, Li, L, Kemp, GJ ORCID: 0000-0002-8324-9666
et al (show 1 more authors) (2017) Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder. Radiology, 282 (3). 826 - 834.

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Abstract

Purpose To use diffusion-tensor (DT) imaging and graph theory approaches to explore the brain structural connectome in pediatric posttraumatic stress disorder (PTSD). Materials and Methods This study was approved by the relevant research ethics committee, and all participants’ parents or guardians provided informed consent. Twenty-four pediatric patients with PTSD and 23 control subjects exposed to trauma but without PTSD were recruited after the 2008 Sichuan earthquake. The structural connectome was constructed by using DT imaging tractography and thresholding the mean fractional anisotropy of 90 brain regions to yield 90 × 90 partial correlation matrixes. Graph theory analysis was used to examine the group-specific topologic properties, and nonparametric permutation tests were used for group comparisons of topologic metrics. Results Both groups exhibited small-world topology. However, patients with PTSD showed an increase in the characteristic path length (P = .0248) and decreases in local efficiency (P = .0498) and global efficiency (P = .0274). Furthermore, patients with PTSD showed reduced nodal centralities, mainly in the default mode, salience, central executive, and visual regions (P < .05, corrected for false-discovery rate). The Clinician-Administered PTSD Scale score was negatively correlated with the nodal efficiency of the left superior parietal gyrus (r = −0.446, P = .043). Conclusion The structural connectome showed a shift toward “regularization,” providing a structural basis for functional alterations of pediatric PTSD. These abnormalities suggest that PTSD can be understood by examining the dysfunction of large-scale spatially distributed neural networks.

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 13 Dec 2016 10:38
Last Modified: 18 Jan 2021 21:20
DOI: 10.1148/radiol.2016160907
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3004835