Mood And Reward Dynamics In Human Adolescent Brain Electrophysiology

Abstract Despite the frequency of mood disorders in the population our understanding of neuronal markers of mood remains elusive which stalls the development of targeted brain-based treatments for these problems. Computational models can help identifying likely parameters affecting self-reported mood during mood induction tasks. Here we test if our previously proposed computational model dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end we shifted mood in healthy (N=24) and depressed (N=30) adolescents by delivering individually tailored reward prediction errors whilst recording magnetoencephalography (MEG) data. Following a pre-registered analysis we hypothesize that expectation (defined by previous reward outcomes) would be predictive of beta-gamma oscillatory power (25-40Hz), a frequency shown to modulate to reward feedback. We also hypothesize that trial variations in the evoked response to the presentation of gambling options and in source localized responses to reward feedback. Through our multilevel statistical analysis we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with possible localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule and expectation to have an effect on the cerebellum after presentation of gambling options. To our knowledge, this is the first study to relate fluctuations in mood on a minute time-scale to variations in neural oscillations with non-invasive electrophysiology. Significance Statement Brain mechanisms underlying mood and its relationship with changes in reward contingencies in the environment are still elusive but could have a strong impact on our understanding and treatment of debilitating mood disorders. Building on a previously proposed computational mood model we use multilevel statistical models to find relationship between trial-by-trial variations in model components of mood and neural responses to rewards measured with non-invasive electrophysiology (MEG). Through confirmatory analysis we show that it is possible to observe relationships between trial variations in neural responses and computational parameters describing mood dynamics. Identifying the dynamics of mood and the neural processes it affects could pave the way for more effective neuromodulation treatments.