Maobin Wei1, Jiaolong Qin1, Rui Yan2, Kun Bi1, Chu Liu1, Zhijian Yao2,3, Qing Lu1,4. 1. Key Laboratory of Child Development and Learning Science, Research Center of Learning Science, Southeast University, Nanjing, China. 2. Academic Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China. 3. Medical College of Nanjing University, Nanjing, China. 4. Suzhou Research Institute of Southeast University, Suzhou, China.
Abstract
PURPOSE: To detect the consecutive variations of the internetwork interactions over time, which helps to discover the underlying dysfunction of depressive disorders. Abnormal interactions of resting-state functional networks have been reported in depression. However, little is known regarding the dynamics of how these crucial networks interact and the disease-related dysfunction. MATERIALS AND METHODS: Functional magnetic resonance imaging data at 3.0T in the resting state were acquired from 20 depressed patients and 20 healthy controls. Twelve resting-state networks were extracted by group-independent component analysis, and their interactions were calculated through a sliding windowed Granger causality model analysis. The acquired effective connectivity matrices were used to construct multislice networks with modular structures that were detected via a multislice community detection method. RESULTS: No significant differences were observed in the modularity and total module numbers between the depressed patients and the healthy controls. The P values were 0.133 with a confidence interval (-0.0001 0.0093) and 0.136 with a confidence interval (-0.30 0.90), respectively. However, the depressed patients exhibited decreased flexibility of the salience network (SN) compared with the controls (P = 0.048, corrected, with a confidence interval 0.0068 0.066). CONCLUSION: SN was inclined to participate less in the multiple brain functional modules across the resting time in depression, and infrequently changed its modular allegiance. These findings support the potential importance of the SN in the neuropathological mechanism of depression. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:1135-1143.
PURPOSE: To detect the consecutive variations of the internetwork interactions over time, which helps to discover the underlying dysfunction of depressive disorders. Abnormal interactions of resting-state functional networks have been reported in depression. However, little is known regarding the dynamics of how these crucial networks interact and the disease-related dysfunction. MATERIALS AND METHODS: Functional magnetic resonance imaging data at 3.0T in the resting state were acquired from 20 depressedpatients and 20 healthy controls. Twelve resting-state networks were extracted by group-independent component analysis, and their interactions were calculated through a sliding windowed Granger causality model analysis. The acquired effective connectivity matrices were used to construct multislice networks with modular structures that were detected via a multislice community detection method. RESULTS: No significant differences were observed in the modularity and total module numbers between the depressedpatients and the healthy controls. The P values were 0.133 with a confidence interval (-0.0001 0.0093) and 0.136 with a confidence interval (-0.30 0.90), respectively. However, the depressedpatients exhibited decreased flexibility of the salience network (SN) compared with the controls (P = 0.048, corrected, with a confidence interval 0.0068 0.066). CONCLUSION: SN was inclined to participate less in the multiple brain functional modules across the resting time in depression, and infrequently changed its modular allegiance. These findings support the potential importance of the SN in the neuropathological mechanism of depression. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:1135-1143.
Authors: Richard E Daws; Christopher Timmermann; Bruna Giribaldi; James D Sexton; Matthew B Wall; David Erritzoe; Leor Roseman; David Nutt; Robin Carhart-Harris Journal: Nat Med Date: 2022-04-11 Impact factor: 87.241
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