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. 2020:26:102163.
doi: 10.1016/j.nicl.2020.102163. Epub 2020 Jan 7.

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

Affiliations

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

Yicheng Long et al. Neuroimage Clin. 2020.

Abstract

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach.

Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels.

Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10-3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients.

Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

Keywords: Connectome; Default-mode; Depression; Dynamic functional connectivity; FMRI; Temporal variability.

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Conflict of interest statement

Declaration of Competing Interest The authors report no biomedical financial interests or potential conflicts of interest related to the present work. Dr. Castellanos reports serving on the advisory board of BOL Pharma and receiving research support (study drug) from Greenwich Biosciences.

Figures

Fig 1
Fig. 1
The procedures for constructing dynamic brain networks and computing dynamic network metrics. (A) The time series for all nodes were divided into a number of continuous time windows. (B) The whole-brain connectivity matrices were calculated within each window to compose a dynamic network, whose temporal variability was then estimated by average dissimilarities between different windows. (C) The dynamic brain networks were further thresholded and binarized with a range of sparsities from 10% to 50%, at which temporal clustering and temporal efficiency were estimated. TR = repetition time.
Fig 2
Fig. 2
(A) Group comparison on temporal variability. (B-C) Group comparisons on the temporal correlation coefficient and characteristic temporal path length, with values at each sparsity level and average values across all sparsities (10% to 50%) both presented. (D) Partial correlations between each metric and the HAMD score, adjusted for age, sex and site effects. The error bars and shadows in (A)–(C) represent 95% confidence intervals, and all reported p values were corrected for multiple tests using the FDR method.
Fig 3
Fig. 3
The nodes showing (A) a higher nodal temporal variability, (B) a lower nodal temporal correlation coefficient, and (C) a shorter nodal temporal path length in MDD patients than HCs (with FDR-corrected p < 0.05). Sizes of plots are weighted by F values. ACC = anterior cingulate cortex; dFC = dorsal frontal cortex; IPS = intraparietal sulcus; L = left hemisphere; R = right hemisphere; vmPFC = ventromedial prefrontal cortex.
Fig 4
Fig. 4
Results of subgroup comparisons among the first-episode, drug-naïve (FEDN) patients, non-FEDN patients and healthy controls (HCs). The error bars represent 95% confidence intervals, and all reported p values were corrected for multiple tests using the FDR method.

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