Abstract
Major depressive disorder (MDD) is a severe mental disorder and is lacking in biomarkers for clinical diagnosis. Previous studies have demonstrated that functional abnormalities of the unifying triple networks are the underlying basis of the neuropathology of depression. However, whether the functional properties of the triple network are effective biomarkers for the diagnosis of depression remains unclear. In our study, we used independent component analysis to define the triple networks, and resting-state functional connectivities (RSFCs), effective connectivities (EC) measured with dynamic causal modeling (DCM), and dynamic functional connectivity (dFC) measured with the sliding window method were applied to map the functional interactions between subcomponents of triple networks. Two-sample t-tests with p < 0.05 with Bonferroni correction were used to identify the significant differences between healthy controls (HCs) and MDD. Compared with HCs, the MDD showed significantly increased intrinsic FC between the left central executive network (CEN) and salience network (SAL), increased EC from the right CEN to left CEN, decreased EC from the right CEN to the default mode network (DMN), and decreased dFC between the right CEN and SAL, DMN. Moreover, by fusion of the changed RSFC, EC, and dFC as features, support vector classification could effectively distinguish the MDD from HCs. Our results demonstrated that fusion of the multiple functional connectivities measures of the triple networks is an effective way to reveal functional disruptions for MDD, which may facilitate establishing the clinical diagnosis biomarkers for depression.
Highlights
Major depressive disorder (MDD) is a severe mental illness with emotional and cognitive abnormalities, and anhedonia, reduced energy, poor attention, and concentration are core symptoms of MDD (Diener et al, 2012; Belzung et al, 2015)
MDD patients were diagnosed with the Structured Clinical Interview for DSM Disorders (SCID) using DSM-IV criteria, and the severity of depressive symptoms was measured by Hamilton Depression Rating Scale (HAMD)
There are no significant differences in gender (p = 0.66), age (p = 0.63), and education level (p = 0.94) between MDD and healthy controls (HCs)
Summary
Major depressive disorder (MDD) is a severe mental illness with emotional and cognitive abnormalities, and anhedonia, reduced energy, poor attention, and concentration are core symptoms of MDD (Diener et al, 2012; Belzung et al, 2015). The triple network model, consisting of the central executive network (CEN), default mode network (DMN), and Diagnosis Biomarkers for MDD salience network (SAL), was proposed, and dysfunctions of the three networks may underlay the cognitive and affective abnormalities in psychiatric and neurological disorders (Menon, 2011). The functional abnormalities of the three networks have been reported in different studies (Kaiser et al, 2015; Mulders et al, 2015; Brakowski et al, 2017; Wang et al, 2017c), it remains unclear whether/how the intrinsic functional changes and the casual influences between the sub-components of the three networks contribute to the neuropathology of depression. Using intrinsic, effective, and dynamic connectivities to explore the abnormal interactions between the sub-components of the tripe network without any assumption may provide us with new information specific to the neuropathology of MDD. Fusion of the multiple functional connectivity measures may facilitate establishing more effective diagnosis biomarkers than using single connectivity measures
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