Neurotyping depression using multiple event-related potentials (ERPs): Leveraging task-based variation to predict remission in depression

Abstract

Aims: Depression is a prevalent, burdensome, and difficult mental health disorder to treat. Significant heterogeneity in clinical characteristics and course of depression hinders treatment success. Efforts to identify more homogeneous subgroups of depression could reduce heterogeneity of depression and therefore improve treatment development and randomized clinical trial outcomes. Event-related potentials (ERPs) derived from continuous electroencephalogram (EEG) can be used to identify depression and predict course (i.e., advance precision psychiatry). Methods: In the current study, we demonstrate how multiple ERPs collected from the same individual across different experimental paradigms can provide insight into brain function and individual differences in depression using factor analysis. This approach for neurotyping depression exploits the high within-task and low between-task associations between ERPs to better understand brain function and depression. Results: We observed three neurotypes, two of which differentiated depressed from non-depressed individuals. Only one neurotype – related to affective processing – prospectively predicted full remission. This neurotype predicted remission even when accounting for other clinical and demographic variables related to subsequent remission. The AUC of this neurotype was acceptable (i.e., 0.72) in predicting remission, exceeding previous study's measures within a single task. Conclusion: Leveraging multiple ERPs derived from many tasks is an important yet underutilized approach in precision psychiatry.