Elsevier

NeuroImage

Volume 60, Issue 3, 15 April 2012, Pages 1678-1685
NeuroImage

Full Length Article
The role of beta-gamma oscillations in unexpected rewards processing

https://doi.org/10.1016/j.neuroimage.2012.01.125Get rights and content

Abstract

Reward processing in humans is carried out by an extensive fronto-subcortical network that might be coordinated by fast oscillatory electrical activity. Previous studies have identified an increase in beta-gamma oscillatoryactivity after the processing of positive feedback stimuli but the functional role of this electroencephalographic (EEG) correlate remains unclear. In the present study, we used event-related brain potentials (ERPs) and trial-by-trial wavelet-based time–frequency analysis of the EEG signal to investigate the effects of expectancy and magnitude of positive and negative feedbacks associated with monetary gains and losses in a gambling task. Large increase of beta-gamma oscillatory activity only in unexpected monetary gains was observed,irrespective of its magnitude. Based on recent findings we propose that this increase in beta-gamma oscillatory activity might reflect a general cognitive mechanism in charge of monitoring unexpected positive events based on feedback processing.

Introduction

Reward processing in humans is carried out by an extensive fronto-subcortical network, composed by several brain structures including the striatum, amygdala, orbitofrontal and insular cortex (Camara et al., 2009, Koob and Volkow, 2010). The existence of such extensive network requires for an integration mechanism that allows the coordination of the different areas involved in it. Brain oscillations seem to be an optimal mechanism for such task (Buzsáki and Draguhn, 2004, Varela et al., 2001). Specifically high frequency oscillations (beta and gamma bands) are key candidates to synchronize these different components as they allow the integration of information across distant brain areas (Colgin et al., 2009, Steriade, 2006, Uhlhaas and Singer, 2006).

In this regard, intracranial recordings on animals have found increase in beta and gamma oscillatory activity in striatum and frontal cortex after actions that were carried out to obtain rewards (Berke, 2009, Courtemanche et al., 2003). In humans, non-invasive electroencephalographic recordings have revealed an increase in the EEG beta band power (Hallschmid et al., 2002) as well as an increase in the beta/theta ratio activity (Schutter and Van Honk, 2005, Snyder and Hall, 2006) during reward processing. In addition, beta-gamma band (20–35 Hz) power increase has been observed 200–400 ms after positive feedback informing about monetary gains using EEG (Cohen et al., 2007, Marco-Pallarés et al., 2008, Marco-Pallarés et al., 2009; see for a recent replication using Magnetoencephalography, Doñamayor et al., 2011) which might be modulated by probability (Cohen et al., 2007) and magnitude of rewards (Marco-Pallarés et al., 2008). In addition, Marco-Pallarés et al. (2009) found a modulation of this response with the COMT Val158Met polymorphism, supporting a possible role of dopamine in this gain related response.

All these studies suggest that beta-gamma oscillatory activity might be an important brain signature of reward-related networks but little is known about the nature of this response or its functional properties. Direct recordings from the Ventral Tegmental Area of awaken monkeys have shown an increase in the dopaminergic activity after improbable rewards (Fiorillo et al., 2003, Schultz et al., 1997, Waelti et al., 2001) or after larger than expected rewards (Tobler etal., 2005). Complementarily, studies in humans have described that some brain areas such as the ventral striatum (Nucleus Accumbens), amygdala and anterior cingulate cortex are selectively activated when a mismatch is detected between the real feedback obtained from an action and the expected one (hereafter referred as prediction error term, PE; Hare et al., 2008, Rutledge et al., 2010, Yacubian et al., 2006). In this context, it has not been appropriately studied which aspects (i.e. probability, magnitude, expected value or prediction error) elicit and modulate beta-gamma oscillatory activity in monetary reward processing. The goal of the present study is to determine the functional significance of beta-gamma power increase after rewards using a gambling Event-Related Potential (ERP) paradigm in which probability and magnitude of rewards and punishments were carefully manipulated.

Section snippets

Subjects

Twenty-six right-handed healthy students participated in the experiment (four men, mean age 25.6 ± 4.8 (S.D.)) for monetary compensation. None of the participants had any history of neurological diseases or substance abuse. Subjects were paid 30 Euros for participation plus/minus what they won/lost in the game respectively. Written consent was obtained prior to the experiment. The experiment was approved by the local ethical committee.

Design

Experimental design is shown in Fig. 1. The experiment

Behavioral results

Participants chose the three cards from left to right in 29.6% (left), 44.3% (middle) and 26.1% (right) of trials. ANOVA showed a significant effect of card location (F(2,38) = 12.61, P = 0.001) and post hoc t-tests showed that subjects chose the middle card significantly more than the left and right cards (for left and middle: t(19) =  2.97, p < 0.01, for right and middle: t(19) =  4.55, P < 0.001) while there was no significant difference in choosing left and right cards (t(19) = 1.79, n.s.). The average

Discussion

In the present study we analyzed the involvement of high-frequency brain oscillatory activity in monetary reward processing in humans. In concrete, we tested whether beta-gamma power increase after a positive feedback was modulated by the probability, magnitude or expected value of the monetary outcome. We found a beta-gamma1

Conclusion

The present paper shows that beta-gamma activity might be a brain signature of unexpected gain that might reflect frontostriatal interactions in the reward network. Further studies are needed to delineate the neural network involved in the generation of this response.

Acknowledgments

Supported by the Ramon y Cajal research program awarded to JMP (RYC-2007-01614), Spanish Government grants (MICINN, PSI2008-03901 to ARF and PSI2009-09101 to JMP) and a grant from the Catalan Government (2009 SGR 93).

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