Modulatory interactions of resting-state brain functional connectivity in major depressive disorder
Authors Tu Z, Jia YY, Wang T, Qu H, Pan JX, Jie J, Xu XY, Wang HY, Xie P
Received 12 February 2018
Accepted for publication 3 July 2018
Published 28 September 2018 Volume 2018:14 Pages 2461—2472
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Prof. Dr. Roumen Kirov
Peer reviewer comments 2
Editor who approved publication: Professor Wai Kwong Tang
Zhe Tu,1–4,* Yuan Yuan Jia,2,5,* Tao Wang,2,3,* Hang Qu,2,3 Jun Xi Pan,2,3 Jie Jie,2,3 Xiao Yan Xu,2,3 Hai Yang Wang,2,3 Peng Xie1–3
1Department of Neurology, Yongchuan Hospital, Chongqing Medical University, Chongqing, China; 2Department of Neurology, the First Affiliated Hospital, Chongqing Medical University, Chongqing, China; 3Chongqing Key Laboratory of Neurobiology, Chongqing Medical University, Chongqing, China; 4Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; 5The College of Medical Informatics, Chongqing Medical University, Chongqing, China
*These authors contributed equally to this work
Background: Major depressive disorder (MDD) is mediated by chronic dysregulation of complex neural circuits, particularly the specific neurotransmitters or other neural substrates. Recently, both increases and decreases in resting-state functional connectivity have been observed in patients with MDD. However, previous research has only assessed the functional connectivity within a specific network or some regions of interests, without considering the modulatory effects of the entire brain regions. To fill in the research gap, this study employed PPI (physiophysiological interaction) to investigate the functional connectivity in the entire brain regions. Apart from the traditional PPI used for cognitive research, current PPI analysis is more suitable for exploring the neural mechanism in MDD patients. Besides, this PPI method does not require a new cognitive estimation task and can assess the modulatory effects on different part of brain without prior setting of regions of interest.
Methods: First, we recruited 76 outpatients with major depressive disorder, and conducted MRI scan to acquire structural and functional images. As referred to the previous study of resting-state networks, we identified eight well-defined intrinsic resting-state networks by using independent component analysis. Subsequently, we explored the regions that exhibited synchronous modulatory interactions within the network by executing PPI analysis.
Results: Our findings indicated that the modulatory effects between healthy crowed and patient are different. By using PPI analysis in neuroimaging can help us to understand the mechanisms of neural disruptions in MDD patients. In addition, this study provides new insight into the complicated relationships between three or more regions of brain, as well as different brain networks functions in external and internal.
Conclusion: Furthermore, the functional connectivity may deepen our knowledge regarding the complex brain functions in MDD patients and suggest a new multimodality treatment for MDD including targeted therapy and transcranial magnetic stimulation.
Keywords: major depressive disorder, physiophysiological, modulatory effects, resting-state networks
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