Abstract
BackgroundMajor 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.MethodsResting-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.ResultsThe 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.ConclusionsOur 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.
Highlights
Major depressive disorder (MDD) is a common psychiatric disorder characterized by deficits in regulating one's own emotions (Aldao et al, 2010; Anticevic et al, 2015)
In MDD patients, one of the most notable changes revealed by functional magnetic resonance imaging is abnormalities in brain functional connectivity (FC) (Guo et al, 2014; Kaiser et al, 2015; Tao et al, 2013; Zhang et al, 2011a), which have been suggested as a potential mechanism underlying their emotional and cognitive symptoms (Marchetti et al, 2012; Whitfield-Gabrieli and Ford, 2012)
The temporal variability and characteristic temporal path length were found to be significantly correlated with the Hamilton Depression Rating Scale (HAMD) scores in patients (Spearman's rho = 0.111 and −0.101, false discovery rate (FDR)-corrected p = 0.045 and 0.045 for temporal variability and characteristic temporal path length, respectively, Fig. 2D), while no significant correlations were found between any metrics and duration of illness (FDRcorrected p > 0.05)
Summary
Major depressive disorder (MDD) is a common psychiatric disorder characterized by deficits in regulating one's own emotions (Aldao et al, 2010; Anticevic et al, 2015). “dynamic FC” has become a new topic in neuroimaging studies to track fluctuations in brain FC patterns (Preti et al, 2017) Such fluctuations have been demonstrated to be involved in a wide range of cognitive and affective processes such as attention (Shine et al, 2016), learning (Bassett et al, 2011), executive functions (Braun et al, 2015), internally-oriented cognition (Zabelina and Andrews-Hanna, 2016) and mood (Betzel et al, 2017), as well as a number of common psychiatric disorders such as autism (Zhang et al, 2016), bipolar disorder (Nguyen et al, 2017) and schizophrenia (Dong et al, 2019; Guo et al, 2018). 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
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