Common and distinct networks underlying reward valence and processing stages: A meta-analysis of functional neuroimaging studies

Abstract

To better understand the reward circuitry in human brain, we conducted activation likelihood estimation (ALE) and parametric voxel-based meta-analyses (PVM) on 142 neuroimaging studies that examined brain activation in reward-related tasks in healthy adults. We observed several core brain areas that participated in reward-related decision making, including the nucleus accumbens (NAcc), caudate, putamen, thalamus, orbitofrontal cortex (OFC), bilateral anterior insula, anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC), as well as cognitive control regions in the inferior parietal lobule and prefrontal cortex (PFC). The NAcc was commonly activated by both positive and negative rewards across various stages of reward processing (e.g., anticipation, outcome, and evaluation). In addition, the medial OFC and PCC preferentially responded to positive rewards, whereas the ACC, bilateral anterior insula, and lateral PFC selectively responded to negative rewards. Reward anticipation activated the ACC, bilateral anterior insula, and brain stem, whereas reward outcome more significantly activated the NAcc, medial OFC, and amygdala. Neurobiological theories of reward-related decision making should therefore take distributed and interrelated representations of reward valuation and valence assessment into account.

Introduction

People face countless reward-related decision making opportunities everyday. Our physical, mental, and socio-economical well-being critically depends on the consequences of the choices we make. It is thus crucial to understand what underlies normal functioning of reward-related decision making. Studying the normal functioning of reward-related decision making also helps us to better understand the various behavioral and mental disorders which arise when such function is disrupted, such as depression (Drevets, 2001), substance abuse (Bechara, 2005, Garavan and Stout, 2005, Volkow et al., 2003), and eating disorders (Kringelbach et al., 2003, Volkow and Wise, 2005).

Functional neuroimaging research on reward has become a rapidly growing field. We have observed a huge surge of neuroimaging research in this domain, with dozens of relevant articles showing up in the PubMed database every month. On the one hand, this is exciting because the mounting results are paramount to formalizing behavioral and neural mechanisms of reward-related decision making (Fellows, 2004, Trepel et al., 2005). On the other hand, the heterogeneity of the results in conjunction with the occasional opposing patterns make it difficult to obtain a clear picture of the reward circuitry in human brain. The mixture of results is partly due to diverse experimental paradigms developed by various research groups that aimed to address different aspects of reward-related decision making, such as the distinction between reward anticipation and outcome (Breiter et al., 2001, Knutson et al., 2001b, McClure et al., 2003, Rogers et al., 2004), valuation of positive and negative rewards (Liu et al., 2007, Nieuwenhuis et al., 2005, O’Doherty et al., 2001, O’Doherty et al., 2003a, Ullsperger and von Cramon, 2003), and assessment of risk (Bach et al., 2009, d’Acremont and Bossaerts, 2008, Hsu et al., 2009, Huettel, 2006).

Therefore, it is crucial to pool existing studies together and examine the core reward networks in human brain, from both data-driven and theory-driven approaches to test the commonality and distinction of different aspects of reward-related decision making. To achieve this goal, we employed and compared two coordinate-based meta-analysis (CBMA) methods (Salimi-Khorshidi et al., 2009), activation likelihood estimation (ALE) (Laird et al., 2005, Turkeltaub et al., 2002) and parametric voxel-based meta-analysis (PVM) (Costafreda et al., 2009), so as to reveal the concordance across a large number of neuroimaging studies on reward-related decision making. We anticipated that the ventral striatum and orbitofrontal cortex (OFC), two major dopaminergic projection areas that have been associated with reward processing, would be consistently activated.

In addition, from a theory-driven perspective, we aimed to elucidate whether there exist distinctions in the brain networks that are responsible for processing positive and negative reward information, and that are preferentially involved in different stages of reward processing such as reward anticipation, outcome monitoring, and decision evaluation. Decision making involves encoding and representation of the alternative options and comparing the values or utilities associated with these options. Across these processes, decision making is usually affiliated with positive or negative valence from either the outcomes or emotional responses toward the choices made. Positive reward valence refers to the positive subjective states we experience (e.g., happiness or satisfaction) when the outcome is positive (e.g., winning a lottery) or better than we anticipate (e.g., losing less value than projected). Negative reward valence refers to the negative feelings we go through (e.g., frustration or regret) when the outcome is negative (e.g., losing a gamble) or worse than what we expect (e.g., stock value increasing lower than projected). Although previous studies have attempted to distinguish reward networks that are sensitive to processing positive or negative information (Kringelbach, 2005, Liu et al., 2007), as well as those that are involved in reward anticipation or outcome (Knutson et al., 2003, Ramnani et al., 2004), empirical results have been mixed. We aimed to extract consistent patterns by pooling over a large number of studies examining these distinctions.