Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder
Fengmei Lu1, Qian Cui2, Xinju Huang3
1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, PR China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 610054, PR China.
Objective:
Major depressive disorder (MDD) is a ubiquitous mental illness with heterogeneous symptoms, however, the pathophysiology mechanisms are still not fully understood. Clinical and preclinical studies suggested that depression could cause disturbances in sensory perception systems, disruptions in auditory and visual functions may serve as an essential clinical features underlying MDD.
Methods:
The current study investigated the abnormal intrinsic connectivity within and between visual and auditory networks in 95 MDD patients and 97 age-, gender-, education level-matched healthy controls (HCs) by using resting-state functional magnetic resonance imaging (fMRI). One auditory network (AN) and three visual components including visual component 1 (VC1), VC2, and VC3 were identified by using independent component analysis method based on the fMRI networks during the resting state with the largest spatial correlations, combining with brain regions and specific network templates.
Results:
We found that MDD could be characterized by the following disrupted network model relative to HCs: (i) reduced within-network connectivity in the AN, VC2, and VC3; (ii) reduced between-network connectivity between the AN and the VC3. Furthermore, aberrant functional connectivity (FC) within the visual network was linked to the clinical symptoms.
Conclusions:
Overall, our results demonstrated that abnormalities of FC in perception systems including intrinsic visual and auditory networks may explain neurobiological mechanisms underlying MDD and could serve as a potential effective biomarker.
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