A decade of decoding reward-related fMRI signals and where we go from here

Highlights

• MVPA has been used in recent years to study reward and decision making.

• Assumptions and models for interpreting these results are rarely discussed.

• This paper reviews selected MVPA studies on reward and what we have learned from them.

• It highlights questions about reward processing for which MVPA is particularly useful.

Abstract

Information about potential rewards in the environment is essential for guiding adaptive behavior, and understanding neural reward processes may provide insights into neuropsychiatric dysfunctions. Over the past 10 years, multivoxel pattern analysis (MVPA) techniques have been used to study brain areas encoding information about expected and experienced outcomes. These studies have identified reward signals throughout the brain, including the striatum, medial prefrontal cortex, orbitofrontal cortex, dorsolateral prefrontal cortex, and parietal cortex. This review article discusses some of the assumptions and models that are used to interpret results from these studies, and how they relate to findings from animal electrophysiology. The article reviews and summarizes some of the key findings from MVPA studies on reward. In particular, it first focuses on studies that, in addition to mapping out the brain areas that process rewards, have provided novel insights into the coding mechanisms of value and reward. Then, it discusses examples of how multivariate imaging approaches are being used more recently to decode features of expected rewards that go beyond value, such as the identity of an expected outcome or the action required to obtain it. The study of such complex and multifaceted reward representations highlights the key advantage of using representational methods, which are uniquely able to reveal these signals and may narrow the gap between animal and human research. Applied in a clinical context, MVPA may advance our understanding of neuropsychiatric disorders and the development of novel treatment strategies.

Introduction

Approaching potential rewards in the environment while avoiding danger is critical for survival. To enable such adaptive behaviors, nervous systems of humans and other animals must process and represent reward-related information about future and experienced outcomes. In turn, disruptions in neural reward processing may result in maladaptive behaviors such as those observed in patients with neurological and psychiatric disorders.

Neuroscience has a rich history of studying reward processing in animals using lesions (Everitt and Stacey, 1987, Gaffan and Murray, 1990, Holland and Gallagher, 1993, Schoenbaum et al., 2003), electrophysiology (Romo and Schultz, 1990, Schoenbaum et al., 1998, Tremblay and Schultz, 1999) and, more recently, optogenetics (Ferenczi et al., 2016, Gremel and Costa, 2013, Steinberg et al., 2013). In contrast, the study of human reward processing has traditionally been limited to evidence from patients with lesions in circumscribed brain areas (Bechara et al., 1994, Tsuchida et al., 2010). This drastically changed with the invention of functional magnetic resonance imaging (fMRI), which has opened a window into the neural mechanisms of reward in humans (Breiter et al., 2001, Knutson et al., 2001, O’Doherty et al., 2001). Although univariate fMRI studies have taught us a great deal about the brain regions involved in reward processing (Bartra et al., 2013, Clithero and Rangel, 2013), they are restricted to brain areas in which neuronal activity is relatively uniformly associated with reward across voxels and individuals. The advent of multivariate approaches to fMRI data analysis (Haxby et al., 2001, Haynes and Rees, 2005, Kamitani and Tong, 2005, Kriegeskorte et al., 2006) mitigated these limitations and enabled us to study brain regions in which neurons represent reward-related information heterogeneously across space and individuals.

Over the past decade, fMRI studies using MVPA techniques have decoded a wide array of reward-related signals from the human brain. Individual studies have examined feedback-related signals predicting value-based choices (Hampton and O’Doherty, 2007), and how reinforcement signals from the outcome of simple games are represented (Vickery et al., 2011). Other studies have focused on decoding the subjective value of offers (Krajbich et al., 2009, Wang et al., 2014), the value of predicted outcomes (Kahnt et al., 2010, Kahnt et al., 2011b), consumer choices made inside the scanner (Grosenick et al., 2008), and differences between types of valuation and reward (Clithero et al., 2009, Clithero et al., 2011). Moreover, several studies used brain activity acquired during passive viewing (Levy et al., 2011), or even outside the focus of attention (Tusche et al., 2010, Tusche et al., 2013), to predict preference-based choices made outside the scanner (Smith et al., 2014). For instance, Tusche et al. (2010) presented a series of images of automobiles in the background while subjects were engaged in a demanding visual fixation task. The acquired fMRI data were then used to predict hypothetical consumer choices made after scanning. Similarly, Levy et al. (2011) showed that preference-related fMRI signals during passive viewing of images of consumer products can be used to predict actual choices at a later time point. Beyond demonstrating the technical feasibility of decoding real-world decisions based on fMRI signals, these studies have illustrated potential applications of reward-based decoding approaches to infer choice and valuation when open behavior is unavailable (Grosenick et al., 2008, Smith et al., 2014).

In general, MVPA studies in the reward domain have confirmed previous results from univariate fMRI experiments and successfully decoded reward-related information in subcortical areas such as the striatum, and in areas of the prefrontal and parietal cortex. Section 2 of this article discusses common models and assumptions that relate MVPA findings to the reward-related firing of single neurons. (Note that no general overview on technical aspects of fMRI decoding methods and multivariate classifiers will be provided, and the interested reader is referred to comprehensive review articles on this topic (Haynes, 2015, Misaki et al., 2010, Mourao-Miranda et al., 2005).) Beyond mapping reward-related brain regions, many MVPA studies have addressed additional questions regarding neural mechanisms of reward that would have been difficult to address with conventional imaging approaches. A selection of such studies is the focus of section 3 wherein key points arising from these experiments are summarized. Section 4 discusses how representational imaging methods are being used more recently to address questions related to reward and goal-directed behavior that go beyond the encoding of value.