Neurocomputational studies of mood-related momentum dynamics linking reward learning, valuation and responsivity

PUBLIC HEALTH RELEVANCE:

Many people with mental disorders suffer from drastic mood swings, which can escalate to the point of despair and even attempting suicide. This work will help understand the brain and behavior processes that underlie mood swings; if successful, this study will help clinicians predict mood swings using phone apps and wearable sensors.

PROJECT SUMMARY:

The RDoC Positive Valence Systems (PVS) encompass motivational processes underlying normal reward- guided behavior and its alterations in many mental disorders. Yet, the theoretical links between the PVS constructs of Reward Responsiveness, Learning, and Valuation remain under-specified. Hence, our goal is to unify them under a new model of computational reinforcement learning with momentum dynamics wherein momentum reflects whether recent outcomes have generally exceeded or fallen short of our expectations, signaling an improving or worsening reward rate. Momentum is closely linked with mood and our model offers new insights into the interplay of mood and reward learning. Thus, we are seeking to provide a mechanistic account of transdiagnostic mood dynamics and affective instability (AI), a dimension of psychopathology seen in depression, anxiety, eating and personality disorders, and suicidal behavior. While ecological momentary assessment (EMA) studies of AI have shown how mood changes over time in mental illness, to date we have no formal model that can explain why it changes thus. On the other hand, lab-based experimental studies have used tools from cognitive neuroscience to explore potential neural mechanisms of affective instability. Though promising, lab studies are too brief to capture the temporal dynamics of AI in psychopathology, which typically unfold over hours or days. Here, we overcome the limitations of EMA and laboratory studies to date by bringing together key elements of both within a framework grounded in reinforcement learning and dynamical systems theory. To this end, we will combine mood tracking with learning experiments carried out in daily life over 4 weeks, concurrently recording neurophysiological signals via wearable heart rate and electroencephalography sensors. We have shown that this platform captures the behavioral and physiological effects of positive and negative outcomes, and that physiological learning signals predict day-to-day changes in subjects’ mood. We will use this platform to examine PVS constructs and AI in individuals sampled from the community (Sample 1, n = 300) and in a clinical sample of individuals with borderline personality (Sample 2, n = 150) recruited from two ongoing studies in Pittsburgh and State College, PA. In our earlier study, mood induction that impacted reward valuation also impacted striatal reward responsiveness. Here, we will investigate the cortico-striatal substrates of momentum dynamics in relation to real-life mood fluctuations by combining mobile longitudinal assessment with model-based fMRI. During the scan, subjects will choose between experimental stimuli they previously encountered in different moods. This will allow us to examine how mood impacts neural valuation and learning signals and how learning signals shape future mood. Our interdisciplinary team has expertise in computational modeling of mood and its integration with EMA, physiology and imaging (Eldar), computational model-augmented functional imaging and EMA in clinical populations (Dombrovski, Hallquist), and neuroimaging methods (Hallquist).