Title: Combining mHealth with Neuroscience to Better Understand Emotion and Well-Being
Affective disorders are among the most prevalent and debilitating in society. My work aims to determine the indicators and mechanisms that give rise to momentary affective reactions, and how specific affective dynamics promote risk for, and resilience to, mood and anxiety disorders. We first utilize personally meaningful, naturalistic events to build computational models of affective, neural, and behavioral dynamics to better understand the parameters driving real-world emotional reactions generally. We then use these paradigms to determine which parameters are linked to individual differences in the risk for, and resilience to affective disorders. Utilizing a combination of Ecological Momentary Assessment (EMA), GPS, and functional neuroimaging, I will present data demonstrating that deviations from expectation (or “prediction errors”), across multiple real-world domains is linked to variation in affect. Furthermore, individual differences in the dynamics of affective and neural reactions during such deviations from expectation are linked to psychopathology risk and psychological well-being. By combining quasi-experimental and experimental methods to take advantage of real-world, personally meaningful events we can build computational models of affect and behavior to better understand the indicators linked to individual differences in psychopathology and well-being.