Mood Brain and Development Unit
National Institute of Mental Health
Implementing analytic tools from engineering can bring a great promise to the study of human dynamics. In this talk I will show how a closed-loop control strategy can be a powerful tool for controlling and characterizing mood. I will present the Mood-Machine-Interface, an efficient mood modification algorithm, that can shift mood by adjusting reward values according to individual mood sensitivity. This method ensures that the created positive and negative environments are potent across individuals and across time. The paradigm can be used to generate substantial mood changes in healthy, depressed, adult and adolescent individuals. And moreover, by creating a non-random environment, it enabled us to model how mood changes are affected by previous events. This model shows that early experiences carry a strong influence on mood, which is mediated by neural signals in the ACC, thus providing a neuro-computational account for mood regulation. Overall, this paradigm demonstrates how closing the loop can open the door to understanding mood dynamics and to developing individualized treatments of mood disorders.