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Altered global brain-network properties as trait marker in restrictive eating disorders

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Geisler, D., Boehm, I., Borchardt, V., King, J., Tam, F., Roessner, V., et al. (2018). Altered global brain-network properties as trait marker in restrictive eating disorders. Poster presented at 24th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2018), Singapore.

Cite as: http://hdl.handle.net/21.11116/0000-0001-7D98-8
Introduction: Resting state fMRI (rs-fMRI) studies have identified functional connectivity patterns associated with obesity (Kullmann et al., 2012; Garcia-Garcia et al., 2013), but few have investigated in individuals with restrictive eating disorders (RED). Moreover, the majority of previous studies have employed traditional analysis procedures which fail to appreciate the complex nature of brain network organization. One previous rs-fMRI study in underweight patient employing graph-theoretic metrics revealed changes in global and intermediate brain network architecture possibly driven by local degradations in a thalamo-insular network (Geisler et al., 2016). Here we study individuals with restrictive eating disorders (RED) who present with a normal weight. Methods: The study includes fMRI resting state data of 30 individuals with RED and 30 healthy controls (HC). FMRI data were preprocessed within the nipype framework (Gorgolewski et al., 2011) using SPM8 including the artifact detection (ART), DARTEL toolbox for generating a group template and spatial normalization. Then by using the DPARSFA toolbox (Chao-Gan et al., 2010) we parcellated the volumes were into 160 spherical regions of interest (ROIs) as defined by Dosenbach (Dosenbach et al., 2010). The extracted time courses of these ROIs were used to create symmetric correlation matrices with pair-wise Pearson correlation coefficients. Based on these matrices, we constructed weighted, undirected graph networks with 160 nodes on individual subject level. We computed well-established global metrics (clustering coefficient, characteristic pathlength, small-worldness index, efficiency, degree correlation) and local graph metrics (degree, strength, average pathlength, betweenness centrality, participation index, local efficiency, normalized local efficiency) across a range of network densities (Sporns et al., 2004; Rubinov et al., 2010). Results: Indicative of an altered global network structure, RED individuals showed an increased degree correlation between linked pairs of nodes and reduced global clustering as well as small-worldness compared to HC, while no group differences at an intermediate or local network level were evident. Conclusions: This pattern of results suggests that normal-weight RED individuals have a global brain network configuration characterized by an affinity for nodes of a similar degree to connect preferably. Moreover, the network topology of RED was characterized by a reduced presence of highly interconnected groups of nodes.