Functional connectivity within the default mode network can predict the sleep disturbance scores of the patients with depression
Objective:To explore whether the functional connectivity (FC) of the default mode network (DMN) can predict the sleep disturbance scores of the patients with major depressive disorder (MDD). Materials and Methods:The resting functional magnetic resonance imaging data of 326 patients with MDD from the REST-meta-MDD project were included after undergoing rigorous selection based on the experimental criteria. The entire brain was defined into 256 regions based on the Power template,followed by separate extraction of the FC of the intra-and inter-DMN. Connectome-based predictive modeling was employed to regress individual sleep disturbance score using both types of FC feature,and the experimental findings would be subsequently validated on an external independent validation dataset. Results:The predictive model based on the intra-FC of the DMN demonstrated significant prediction capability for sleep disturbance scores in individuals with depression,not only in the discovery dataset (r=0.244,P<0.001),but also in the external validation dataset (r=0.345,P=0.046). However,models based on the inter-FC of the DMN exhibited limited prediction ability and can only predict the scores in the discovery dataset (r=0.238,P<0.001),failing to generalize to the external validation dataset (r=0.256,P=0.143). Conclusions:The intra-FC of DMN demonstrates predictive capability for the sleep disturbance scores in patients with MDD in some extent.
depressionsleep disturbancedefault mode networkresting-state functional magnetic resonance imagingmagnetic resonance imaging
国家科技期刊平台
NSTL
万方数据
