ELSC Seminar Series

Mood is a complex experience that profoundly influences many of our functions, bodily sensations, and mental health. Computational and interdisciplinary approaches offer new avenues for studying emotional dynamics and their aberrations in mental disorders. In this presentation, I will demonstrate how a closed-loop control strategy, commonly used in engineering, can be a powerful tool for modifying and characterizing mood. Our paradigm involves adjusting Reward-Prediction-Errors in real-time in an individualized and adaptive manner, allowing to generate and computationally model mood transitions. Moreover, this methodology enables the implementation of a system characterization approach to uncover the intricate relationships across the body-brain-mood triad. We hope that this approach would help us reveal neural and physiological markers of mood that are objective and continuously measurable. In addition, identifying the interrelations between neural, physiological, and mood dynamics, could also advance the diagnosis of mood disorders and the development of individualized clinical applications.