Self-dependent neural variability predicts recovery from depressive symptoms

Abstract

Abstract Researchers have increasingly paid attention to the neural dynamics of depression. This study examined whether self-dependent neural variability predicts recovery from depressive symptoms. Sixty adults with depressive symptoms who were not officially diagnosed with major depressive disorder participated in this study. Participants completed functional magnetic resonance imaging (fMRI) scanning, including a resting-state and a self-reflection task. The fMRI data were used to estimate neural variability, which refers to the temporal variability in regional functional connectivity patterns. Participants then completed the Self-Construal Scale and the Beck Depression Inventory (BDI). The change in BDI scores over 3 months indicated the degree of recovery from depressive symptoms. Self-construal moderated the effects of general neural variability on predicting recovery from depressive symptoms. Interdependent individuals became less depressive with higher general neural variability, but the relationship was not significant in independent individuals. The differences in neural variability between self-related and other-related conditions also predicted recovery from depressive symptoms. The regions contributing to the prediction were mainly distributed in the default-mode network. Based on these results, the harmony between individuals’ neural dynamics and self-concept is important for recovery from depressive symptoms, which might be a foundation for individualized treatment and counseling.