Shui Tian1, Mohammad Ridwan Chattun2, Siqi Zhang1, Kun Bi1, Hao Tang2, Rui Yan2, Qiang Wang3, Zhijian Yao4, Qing Lu5. 1. School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China. 2. Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China. 3. Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China. 4. Department of Psychiatry, The Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China. Electronic address: zjyao@njmu.edu.cn. 5. School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China. Electronic address: luq@seu.edu.cn.
Abstract
BACKGROUND: Major Depressive Disorder (MDD), characterized by depressed mood or anhedonia, is associated with altered functional connectivity (FC) within and between large scale networks such as the Default Mode Network (DMN), the Central Executive Network (CEN) and the Salience Network (SN). Since aberrant FC exhibits temporal variability and could give rise to distorted reconfiguration of functional brain networks, an in-depth analysis of the community structure could provide further insight into the synchrony of networks. We hypothesized that alterations in dynamic network community structure in MDD could be temporally accompanied by disrupted conscious states of these three networks. METHODS: 26 MDD patients and 25 healthy controls were scanned using a whole-head resting-state Magnetoencephalography (MEG) machine. A novel multilayer modularity framework explored the functional modulation of these networks. Recruitment (R) and integration (I) provided the strength of interaction within networks or across networks, respectively. RESULTS: The brain regions in the DMN, CEN and SN were transiently integrated and segmented in both patients and controls. R of CEN and I of SN were significantly greater in MDD compared to controls. CONCLUSION: Intrinsic resting-state networks dynamically interact and reorganize into distinct functional modules in both patients and controls. However, the CEN "hyper-intertwines" with itself and SN "hyper-integrates" among the network of interest in depressed patients compared to controls. Network-level alterations in R and I revealed a more generalized system-level effect rather than a focal-wise effect from a neural dynamic perspective. This could potentially highlight an abnormal network-based mechanism in depression.
BACKGROUND: Major Depressive Disorder (MDD), characterized by depressed mood or anhedonia, is associated with altered functional connectivity (FC) within and between large scale networks such as the Default Mode Network (DMN), the Central Executive Network (CEN) and the Salience Network (SN). Since aberrant FC exhibits temporal variability and could give rise to distorted reconfiguration of functional brain networks, an in-depth analysis of the community structure could provide further insight into the synchrony of networks. We hypothesized that alterations in dynamic network community structure in MDD could be temporally accompanied by disrupted conscious states of these three networks. METHODS: 26 MDDpatients and 25 healthy controls were scanned using a whole-head resting-state Magnetoencephalography (MEG) machine. A novel multilayer modularity framework explored the functional modulation of these networks. Recruitment (R) and integration (I) provided the strength of interaction within networks or across networks, respectively. RESULTS: The brain regions in the DMN, CEN and SN were transiently integrated and segmented in both patients and controls. R of CEN and I of SN were significantly greater in MDD compared to controls. CONCLUSION: Intrinsic resting-state networks dynamically interact and reorganize into distinct functional modules in both patients and controls. However, the CEN "hyper-intertwines" with itself and SN "hyper-integrates" among the network of interest in depressedpatients compared to controls. Network-level alterations in R and I revealed a more generalized system-level effect rather than a focal-wise effect from a neural dynamic perspective. This could potentially highlight an abnormal network-based mechanism in depression.
Authors: Zhen Yang; Qawi K Telesford; Alexandre R Franco; Ryan Lim; Shi Gu; Ting Xu; Lei Ai; Francisco X Castellanos; Chao-Gan Yan; Stan Colcombe; Michael P Milham Journal: Neuroimage Date: 2020-10-24 Impact factor: 6.556
Authors: Allison C Nugent; Elizabeth D Ballard; Jessica R Gilbert; Prejaas K Tewarie; Matthew J Brookes; Carlos A Zarate Journal: Neuroimage Clin Date: 2020-08-08 Impact factor: 4.881