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      Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder

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          Abstract

          Bipolar disorder (BD), particularly BD II, is frequently misdiagnosed as unipolar depression (UD), leading to inappropriate treatment and poor clinical outcomes. Although depressive symptoms may be expressed similarly in UD and BD, the similarities and differences in the architecture of brain functional networks between the two disorders are still unknown. In this study, we hypothesized that UD and BD II patients would show convergent and divergent patterns of disrupted topological organization of the functional connectome, especially in the default mode network (DMN) and the limbic network. Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 32 UD-unmedicated patients, 31 unmedicated BD II patients (current episode depressed) and 43 healthy subjects. Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the following three levels: whole brain, modularity and node. First, both the UD and BD II patients showed increased characteristic path length and decreased global efficiency compared with the controls. Second, both the UD and BD II patients showed disrupted intramodular connectivity within the DMN and limbic system network. Third, decreased nodal characteristics (nodal strength and nodal efficiency) were found predominantly in brain regions in the DMN, limbic network and cerebellum of both the UD and BD II patients, whereas differences between the UD and BD II patients in the nodal characteristics were also observed in the precuneus and temporal pole. Convergent deficits in the topological organization of the whole brain, DMN and limbic networks may reflect overlapping pathophysiological processes in unipolar and bipolar depression. Our discovery of divergent regional connectivity that supports emotion processing could help to identify biomarkers that will aid in differentiating these disorders.

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          Most cited references63

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            Bipolar disorder.

            Bipolar disorder is a recurrent chronic disorder characterised by fluctuations in mood state and energy. It affects more than 1% of the world's population irrespective of nationality, ethnic origin, or socioeconomic status. Bipolar disorder is one of the main causes of disability among young people, leading to cognitive and functional impairment and raised mortality, particularly death by suicide. A high prevalence of psychiatric and medical comorbidities is typical in affected individuals. Accurate diagnosis of bipolar disorder is difficult in clinical practice because onset is most commonly a depressive episode and looks similar to unipolar depression. Moreover, there are currently no valid biomarkers for the disorder. Therefore, the role of clinical assessment remains key. Detection of hypomanic periods and longitudinal assessment are crucial to differentiate bipolar disorder from other conditions. Current knowledge of the evolving pharmacological and psychological strategies in bipolar disorder is of utmost importance.
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              Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder.

              Neuroimaging studies have shown that major depressive disorder (MDD) is accompanied by structural and functional abnormalities in specific brain regions and connections; yet, little is known about alterations of the topological organization of whole-brain networks in MDD patients. Thirty drug-naive, first-episode MDD patients and 63 healthy control subjects underwent a resting-state functional magnetic resonance imaging scan. The whole-brain functional networks were constructed by thresholding partial correlation matrices of 90 brain regions, and their topological properties (e.g., small-world, efficiency, and nodal centrality) were analyzed using graph theory-based approaches. Nonparametric permutation tests were further used for group comparisons of topological metrics. Both the MDD and control groups showed small-world architecture in brain functional networks, suggesting a balance between functional segregation and integration. However, compared with control subjects, the MDD patients showed altered quantitative values in the global properties, characterized by lower path length and higher global efficiency, implying a shift toward randomization in their brain networks. The MDD patients exhibited increased nodal centralities, predominately in the caudate nucleus and default-mode regions, including the hippocampus, inferior parietal, medial frontal, and parietal regions, and reduced nodal centralities in the occipital, frontal (orbital part), and temporal regions. The altered nodal centralities in the left hippocampus and the left caudate nucleus were correlated with disease duration and severity. These results suggest that depressive disorder is associated with disruptions in the topological organization of functional brain networks and that this disruption may contribute to disturbances in mood and cognition in MDD patients. Copyright © 2011 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group
                2158-3188
                July 2017
                04 July 2017
                1 July 2017
                : 7
                : 7
                : e1165
                Affiliations
                [1 ]Medical Imaging Center, First Affiliated Hospital of Jinan University , Guangzhou, China
                [2 ]Institute of Molecular and Functional Imaging, Jinan University , Guangzhou, China
                [3 ]Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute of Brain Research and Rehabilitation, South China Normal University , Guangzhou, China
                [4 ]Department of Psychiatry, First Affiliated Hospital of Jinan University , Guangzhou, China
                Author notes
                [* ]Medical Imaging Center, First Affiliated Hospital of Jinan University , Guangzhou 510630, China, E-mail: cjr.huangli@ 123456vip.163.com
                [* ]Center for the Study of Applied Psychology and MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institue of Brain Research and Rehabilitation, South China Normal University , Guangzhou 510631, China. ruiwang.huang@ 123456gmail.com
                [5]

                These authors contributed equally to this work.

                Article
                tp2017117
                10.1038/tp.2017.117
                5538109
                28675389
                24b7d46d-1a77-4665-973a-e40fda26ee41
                Copyright © 2017 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                : 12 May 2016
                : 06 April 2017
                : 25 April 2017
                Categories
                Original Article

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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