AUTHOR=Li Yanling , Dai Xin , Wu Huawang , Wang Lijie 

TITLE=Establishment of Effective Biomarkers for Depression Diagnosis With Fusion of Multiple Resting-State Connectivity Measures

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.729958

DOI=10.3389/fnins.2021.729958

ISSN=1662-453X

ABSTRACT=Major depressive disorder (MDD) is a severe mental disorders and is lack of biomarkers for clinical diagnosis. A lot of previous studies have demonstrated functional abnormalities of the unifying triple networks are the underlying basis of neuropathology of depression. However, whether the functional properties of the triple network are effective biomarkers for diagnosis of depression remain unclear. In our study, we used independent component analysis to define the triple networks and resting-state functional connectivities (RSFC), effective connectivities (EC) measured with dynamic casual modeling and dynamic functional connectivity (dFC) measured with 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 and MDD. Compared with healthy controls, the MDD showed significantly increased intrinsic FC between left central executive network (CEN) and salience network (SAL), increased EC from right CEN to left CEN, decreased EC from right CEN to default mode network (DMN), and decreased dFC between right CEN and SAL, DMN. Moreover, by fusion the changed RSFC, EC, and dFC as features, support vector classification could effectively distinguish MDD from healthy controls. 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.