Prediction of mood state change based on repeated functional brain imaging and mathematical modeling in premenstrual syndrome – Dayoung Yoon

Abstract:

Accurately predicting mood fluctuations in mood disorders is critical for early intervention and personalized treatment. This study developed a neurophysiologically grounded mood prediction model by integrating behavioral modeling, electroencephalography, functional magnetic resonance imaging (fMRI), and physiological data from wearable devices in premenstrual syndrome (PMS). First, applying the active inference framework to a risk-taking behavioral task revealed that PMS is characterized by a significant reduction in policy precision during decision-making during the luteal phase. Rather than a failure in learning trajectories, this reduction reflects impulsivity at the behavioral execution stage and closely correlates with a diminished amplitude of the contingent negative variation (CNV)—an event-related potential indicating pre-decision neural preparation. Second, neural features extracted by applying cortical surface-based geometric eigenmodes to fMRI data successfully differentiated mood states in PMS. We confirmed that these neural features can be accounted for by the control energy required to maintain eigenmodes based on structural connectivity. Furthermore, to overcome the cost and accessibility constraints of fMRI, we constructed an encoder model that approximates fMRI-based latent brain states and predicts mood using only four circadian rhythm markers continuously collected from wearable devices. Finally, the significant correlation between policy precision and the reduction in centro-parietal CNV amplitude was also significantly explained by the control energy of the eigenmodes. In conclusion, this study presents a real-time, personalized mood monitoring framework that is firmly grounded in neurobiological mechanisms yet practically applicable to daily life.