The Structural Network of Major Depressive Disorder

Abstract

Event Abstract Back to Event The Structural Network of Major Depressive Disorder Saeideh Bakhshi1*, David Gutman2 and Constantine Dovrolis1 1 Georgia Tech, United States 2 Emory University, United States Purpose Major Depressive Disorder (MDD) is one of the most common psychiatric disorders with a lifetime prevalence of 15% [Blazer 1994]. It is also the source of significant morbidity and it is one of the top causes of disability among adults under the age of 50 worldwide [worldhealth 2001]. Despite the availability of a large number of behavioral and pharmacological treatment strategies, up to 30% of patients fail to sufficiently respond to any medication or clinical therapy. Based on a number of imaging studies involving both PET and functional MRI, Mayberg et al. [Mayberg 2005] has developed a putative circuit model for depression integrating a number of cortical and subcortical regions. Based on this model and on findings about alterations in the SubCallosal Cingulate (SCC) activity across several studies, it was proposed that direct modulation of the SCC activity may provide another treatment approach for MDD. This ultimately led to the development and testing of Deep Brain Stimulation (DBS) of the SCC as a novel treatment strategy, which has demonstrated favorable efficacy in a number of small clinical trials [Mayberg 2009]. Approach In this work, we investigate the structural connectivity of the depression network. We focus on 18 ROIs that are considered key mediators in the putative depression network. Our objective is is to identify how these 18 ROIs are interconnected, using Diffusion Tensor Imaging (DTI) data and tractography methods, and to reveal the special role (if any) of the SCC region in network-centric terms. We focus on the 18 ROIs in the MDD circuit model proposed by Mayberg [Mayberg 2005]. These regions include mood regulation, mood monitoring, exteroception and interoception areas. The ROIs were drawn on the 2mm standard space using the SPM pickatlas tool [Maldijan 2003]. We perform Diffusion Tensor Imaging (DTI) and probabilistic tractography on diffusion MRI data from 30 healthy subjects using FDT [Smith 2004]. The tractography results allow us to identify the connectivity from each voxel of each source ROI to all target ROIs. We use this data to create both unweighted (binary) as well as weighted versions of the depression network interconnecting these 18 ROIs. We also evaluate the robustness of the inferred networks with respect to variations in tractography parameters as well as network inference parameters. In the resulting (robust) networks, we apply network analysis methods to characterize each ROI in terms of various centrality measures (degree, closeness, betweenness and others) using both unweighted and weighted metrics. Our focus is on the SCC region, given that that ROI is the target of DBS treatment for MDD. The variability of the depression network connectivity across different subjects is also quantified and discussed. Finally, we compare the SCC with other regions that have been recently proposed as potential DBS targets for treating MDD. Keywords: General neuroinformatics Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Bakhshi S, Gutman D and Dovrolis C (2011). The Structural Network of Major Depressive Disorder. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00065 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Saeideh Bakhshi, Georgia Tech, Atlanta, United States, saeideh@gatech.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Saeideh Bakhshi David Gutman Constantine Dovrolis Google Saeideh Bakhshi David Gutman Constantine Dovrolis Google Scholar Saeideh Bakhshi David Gutman Constantine Dovrolis PubMed Saeideh Bakhshi David Gutman Constantine Dovrolis Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.