Resting-State Functional Connectome Predicts Individual Differences in Depression During COVID-19 Pandemic

Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM's ability for capturing the depression-related patterns with individuals' resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations. Public Significance Statement The primary aim of this study is to exploit and validate individualized neural markers to facilitate the prediction of depressive emotions during COVID-19. The predictive model worked well in the prediction of depression and was successfully validated in an independent sample. The results indicated two main functional connectivity patterns in the prediction of depression, including an emotion-regulation circuit and an interoceptive circuit. We believe these findings can be informative for mental health practitioners to identify and help potential populations at risk for emotional disorder during COVID-19.