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Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here we test if computationally modelled 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 timescale to variations in neural oscillations with noninvasive electrophysiology.Significance StatementBrain 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.

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinize those findings and the theoretical claims that underlie them. To achieve this, we apply the framework provided by classical work on causality as well as contemporary approaches to prediction. We identify a number of conceptual, practical, and analytical challenges to this line of research, and use a pre-registered meta-analysis to quantify the longitudinal associations between reward processing aberrations and depression. We also investigate the impact of measurement error on reported data. We find that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the evidence thus far does not exclude their possible causal role in depression.

Humans refer to their own mood state regularly in day-to-day as well as in clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here we use a computational approach to quantitatively answer this question and show that early events exert a stronger influence on the reported mood compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations, and replicate this result across random, consistent or dynamic structures of reward environments, age groups and both healthy and depressed participants. Moreover, we find evidence for neural encoding of the Primacy, but not the Recency, model in frontal brain regions related to mood regulation. These findings hold implications for the timing of events in experimental or clinical settings and suggest new directions for individualized mood interventions.SignificanceHow we rate our own mood at any given moment is shaped by our experiences; but are the most recent experiences the most influential, as assumed by current theories? Using several sources of experimental data and mathematical modeling, we show that earlier experiences within a context are more influential than recent events, and replicate this finding across task environments, age groups, and in healthy and depressed participants. Additionally, we present neural evidence supporting this primacy model. Our findings show that delineating a temporal structure is crucial in modeling mood and this has key implications for its measurement and definition in both clinical and everyday settings.