Paper
25 March 2016 Altered effective connectivity within default mode network in major depression disorder
Liang Li, Baojuan Li, Yuanhan Bai, Huaning Wang, Linchuan Zhang, Longbiao Cui, Hongbing Lu
Author Affiliations +
Abstract
Understanding the neural basis of Major Depressive Disorder (MDD) is important for the diagnosis and treatment of this mental disorder. The default mode network (DMN) is considered to be highly involved in the MDD. To find directed interaction between DMN regions associated with the development of MDD, the effective connectivity within the DMN of the MDD patients and matched healthy controls was estimated by using a recently developed spectral dynamic causal modeling. Sixteen patients with MDD and sixteen matched healthy control subjects were included in this study. While the control group underwent the resting state fMRI scan just once, all patients underwent resting state fMRI scans before and after two months’ treatment. The spectral dynamic causal modeling was used to estimate directed connections between four DMN nodes. Statistical analysis on connection strengths indicated that efferent connections from the medial frontal cortex (MFC) to posterior cingulate cortex (PCC) and to right parietal cortex (RPC) were significant higher in pretreatment MDD patients than those of the control group. After two-month treatment, the efferent connections from the MFC decreased significantly, while those from the left parietal cortex (LPC) to MFC, PCC and RPC showed a significant increase. These findings suggest that the MFC may play an important role for inhibitory conditioning of the DMN, which was disrupted in MDD patients. It also indicates that disrupted suppressive function of the MFC could be effectively restored after two-month treatment.
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Liang Li, Baojuan Li, Yuanhan Bai, Huaning Wang, Linchuan Zhang, Longbiao Cui, and Hongbing Lu "Altered effective connectivity within default mode network in major depression disorder", Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890G (25 March 2016); https://doi.org/10.1117/12.2218363
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KEYWORDS
Microsoft Foundation Class Library

Control systems

Functional magnetic resonance imaging

Spectral models

Independent component analysis

Statistical analysis

Magnetic resonance imaging

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