title: Depression, neuroimaging and connectomics: a selective overview.
abstract: Depression is a multifactorial disorder with clinically heterogeneous features involving disturbances of mood and cognitive function. Noninvasive neuroimaging studies have provided rich evidence that these behavioral deficits in depression are associated with structural and functional abnormalities in specific regions and connections. Recent advances in brain connectomics through the use of graph theory highlight disrupted topological organization of large-scale functional and structural brain networks in depression, involving global topology (e.g., local clustering, shortest-path lengths, and global and local efficiencies), modular structure, and network hubs. These system-level disruptions show important correlates with genetic and environmental factors, which provide an integrative perspective on mood and cognitive deficits in depressive syndrome. Moreover, research suggests that the pathologic networks associated with depression represent potentially valuable biomarkers for early detection of this disorder and they are likely to be regulated and recalibrated by using pharmacologic, psychological, and brain stimulation therapies. These connectome-based imaging studies present new opportunities to reconceptualize the pathogenesis of depression, improve our knowledge of the biological mechanisms of therapeutic effects, and identify appropriate stimulation targets to optimize the clinical response in depression treatment. Here, we summarize the current findings and historical understanding of structural and functional connectomes in depression, focusing on graph analyses of depressive brain networks. We also consider methodological factors such as sample heterogeneity and poor test-retest reliability of recordings due to physiological, head motion, and imaging artifacts to discuss result inconsistencies among studies. We conclude with suggestions for future research directions on the emerging field of imaging connectomics in depression.
authors.affiliation: Huaxi Magnetic Resonance Research Center, Department of Radiology, Center for Medical Imaging, West China Hospital of Sichuan University, Chengdu, China; Department of Psychiatry , Yale University School of Medicine, New Haven, Connecticut; Department of Psychiatry, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu.
authors.forename: Qiyong
authors.initials: Q
authors.lastname: Gong
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning and International Digital Group/McGovern Institute for Brain Research; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.. Electronic address: yong.he@bnu.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D000818:Animals; D001921:Brain / Q000503:physiopathology*; D063132:Connectome / Q000379:methods*; D003866:Depressive Disorder / Q000175:diagnosis / Q000235:genetics / Q000503:physiopathology*; D006801:Humans; D009434:Neural Pathways / Q000503:physiopathology; D059906:Neuroimaging / Q000379:methods*
keywords: Connectivity; Connectome; Graph theory; Hub; Mood disorder; Network; Rich club