Understanding how the brain computes value is a basic question in neuroscience. Although individual studies have driven this progress, meta-analyses provide an opportunity to test hypotheses that require large collections of data. We carry out a meta-analysis of a large set of functional magnetic resonance imaging studies of value computation to address several key questions. First, what is the full set of brain areas that reliably correlate with stimulus values when they need to be computed? Second, is this set of areas organized into dissociable functional networks? Third, is a distinct network of regions involved in the computation of stimulus values at decision and outcome? Finally, are different brain areas involved in the computation of stimulus values for different reward modalities? Our results demonstrate the centrality of ventromedial prefrontal cortex (VMPFC), ventral striatum and posterior cingulate cortex (PCC) in the computation of value across tasks, reward modalities and stages of the decision-making process. We also find evidence of distinct subnetworks of co-activation within VMPFC, one involving central VMPFC and dorsal PCC and another involving more anterior VMPFC, left angular gyrus and ventral PCC. Finally, we identify a posterior-to-anterior gradient of value representations corresponding to concrete-to-abstract rewards.

A sizable body of evidence has shown that the brain computes several types of value-related signals to guide decision making, such as stimulus values, outcome values, and prediction errors. A critical question for understanding decision-making mechanisms is whether these value signals are computed using an absolute or a normalized code. Under an absolute code, the neural response used to represent the value of a given stimulus does not depend on what other values might have been encountered. By contrast, under a normalized code, the neural response associated with a given value depends on its relative position in the distribution of values. This review provides a simple framework for thinking about value normalization, and uses it to evaluate the existing experimental evidence.

A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity.

The ventromedial prefrontal cortex (vmPFC) plays a critical role in processing appetitive stimuli. Recent investigations have shown that reward value signals in the vmPFC can be altered by emotion regulation processes; however, to what extent the processing of positive emotion relies on neural regions implicated in reward processing is unclear. Here, we investigated the effects of emotion regulation on the valuation of emotionally evocative images. Two independent experimental samples of human participants performed a cognitive reappraisal task while undergoing fMRI. The experience of positive emotions activated the vmPFC, whereas the regulation of positive emotions led to relative decreases in vmPFC activation. During the experience of positive emotions, vmPFC activation tracked participants' own subjective ratings of the valence of stimuli. Furthermore, vmPFC activation also tracked normative valence ratings of the stimuli when participants were asked to experience their emotions, but not when asked to regulate them. A separate analysis of the predictive power of vmPFC on behavior indicated that even after accounting for normative stimulus ratings and condition, increased signal in the vmPFC was associated with more positive valence ratings. These results suggest that the vmPFC encodes a domain-general value signal that tracks the value of not only external rewards, but also emotional stimuli.

The study of stroke patients with modern lesion-symptom analysis techniques has yielded valuable insights into the representation of spatial attention in the human brain. Here we introduce an approach-multivariate pattern analysis-that no longer assumes independent contributions of brain regions but rather quantifies the joint contribution of multiple brain regions in determining behavior. In a large sample of stroke patients, we found patterns of damage more predictive of spatial neglect than the best-performing single voxel. In addition, modeling multiple brain regions-those that are frequently damaged and, importantly, spared-provided more predictive information than modeling single regions. Interestingly, we also found that the superior temporal gyrus demonstrated a consistent ability to improve classifier performance when added to other regions, implying uniquely predictive information. In sharp contrast, classifier performance for both the angular gyrus and insular cortex was reliably enhanced by the addition of other brain regions, suggesting these regions lack independent predictive information for spatial neglect. Our findings highlight the utility of multivariate pattern analysis in lesion mapping, furnishing neuroscience with a modern approach for using lesion data to study human brain function.brain injury | superior temporal cortex | voxelwise lesion symptom mapping | distributed network O bserving the behavioral consequences of brain injury has driven our understanding of brain function. Although recent brain activation measures have revealed how behavior engages spatially distributed networks, lesion methods remain important because of their clinical significance and level of inference, as a result of their ability to detect if a region is critical for a task rather than merely involved with a task (1, 2). Classically, lesion-behavior relationships were inferred by looking for associations: identification of regions consistently damaged in patients with a given symptom. However, this approach suffers from a major confound; some regions of the brain are more likely to be injured than others, and therefore, these studies identify both regions that are critical to a function as well as regions that are frequently injured (for review, see ref.2). Voxelwise lesion symptom mapping (VLSM) revolutionized this method by looking for statistical dissociations: identifying regions that are consistently damaged in individuals with a deficit but spared in those without a deficit (3, 4).Conventional VLSM methods compute independent analyses for each and every voxel of the brain. Unfortunately, this mass univariate method necessarily limits the statistical power for many common neurological syndromes. For example, if damage to either of two distant brain regions can lead to the same symptoms (e.g., a distributed network), a patient with damage to one location will effectively appear as a counter example for detecting the other location. Indeed, consider the situation where a symptom is observed whenever only a po...