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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2016 Edwin Thanarajah, Han, Rotarska-Jagiela, Singer, Deichmann, Maurer, Kaiser and Uhlhaas. The graph theoretical analysis of structural magnetic resonance imaging (MRI) data has received a great deal of interest in recent years to characterize the organizational principles of brain networks and their alterations in psychiatric disorders, such as schizophrenia. However, the characterization of networks in clinical populations can be challenging, since the comparison of connectivity between groups is influenced by several factors, such as the overall number of connections and the structural abnormalities of the seed regions. To overcome these limitations, the current study employed the whole-brain analysis of connectional fingerprints in diffusion tensor imaging data obtained at 3 T of chronic schizophrenia patients (n = 16) and healthy, age-matched control participants (n = 17). Probabilistic tractography was performed to quantify the connectivity of 110 brain areas. The connectional fingerprint of a brain area represents the set of relative connection probabilities to all its target areas and is, hence, less affected by overall white and gray matter changes than absolute connectivity measures. After detecting brain regions with abnormal connectional fingerprints through similarity measures, we tested each of its relative connection probability between groups. We found altered connectional fingerprints in schizophrenia patients consistent with a dysconnectivity syndrome. While the medial frontal gyrus showed only reduced connectivity, the connectional fingerprints of the inferior frontal gyrus and the putamen mainly contained relatively increased connection probabilities to areas in the frontal, limbic, and subcortical areas. These findings are in line with previous studies that reported abnormalities in striatal-frontal circuits in the pathophysiology of schizophrenia, highlighting the potential utility of connectional fingerprints for the analysis of anatomical networks in the disorder.
Author(s): Thanarajah SE, Han CE, Rotarska-Jagiela A, Singer W, Deichmann R, Maurer K, Kaiser M, Uhlhaas PJ
Publication type: Article
Publication status: Published
Journal: Frontiers in Psychiatry
Online publication date: 30/05/2016
Acceptance date: 10/06/2016
Date deposited: 06/04/2017
ISSN (electronic): 1664-0640
Publisher: Frontiers Research Foundation
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