Decoding ruminative states from neurophysiological patterns

Abstract

AbstractIndividuals with depression often engage in iterative “rumination” about challenging situations and potential outcomes. Although the state of rumination has been associated with diverse univariate neurophysiological features, the potential to use multivariate patterns to decode it remains uncertain. In this study, we trained linear support vector machines to differentiate state rumination from distraction using patterns in the alpha, beta, and theta bands, as well as inter-channel connectivity. We used validated tasks to induce rumination or distraction for eight minutes in 24 depressed individuals in six runs over three sessions. During inductions, we recorded 64-channel EEG data and measured self-reported levels of rumination. Participants reported strongly increased rumination, and we decoded state rumination from EEG patterns with significant accuracy. However, the informative features were not consistent across participants, demonstrating that while ruminative states can indeed be decoded from EEG data, these states appear to reflect processes unique to each individual.