John A. Clithero

Informatic parcellation of the network involved in the computation of subjective value

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.

Decoding the anatomical network of spatial attention

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.

Contributions of frontopolar cortex to judgments about self, others and relations

Activation in frontopolar cortex (FPC; BA 10) has been associated both with attending to mental states and with integrating multiple mental relations. However, few previous studies have manipulated both of these cognitive processes, precluding a clear functional distinction among regions within FPC. To address this issue, we developed an fMRI task that combined mentalizing and relational integration processes. Participants saw blocks of single words and performed one of three judgments: how pleasant or unpleasant they found each word (Self condition), how a specific friend would evaluate the pleasantness of the word (Other condition), or the difference between their own pleasantness judgment and that of their friend (Relational condition). We found that medial FPC was modulated by Other relative to Self judgments, consistent with a role in mentalizing. Lateral FPC was significantly activated during Relational compared to Self judgements, suggesting that this region is particularly involved in relational integration. The results point to a strong functional dissociation between medial and lateral FPC. In addition, the data demonstrate a role for lateral FPC in the social domain, provided that the task requires the integration of ones preferences with those of others.

Value normalization in decision making: theory and evidence

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.

Within- and cross-participant classifiers reveal different neural coding of information

Analyzing distributed patterns of brain activation using multivariate pattern analysis (MVPA) has become a popular approach for using functional magnetic resonance imaging (fMRI) data to predict mental states. While the majority of studies currently build separate classifiers for each participant in the sample, in principle a single classifier can be derived from and tested on data from all participants. These two approaches, within- and cross-participant classification, rely on potentially different sources of variability and thus may provide distinct information about brain function. Here, we used both approaches to identify brain regions that contain information about passively received monetary rewards (i.e., images of currency that influenced participant payment) and social rewards (i.e., images of human faces). Our within-participant analyses implicated regions in the ventral visual processing stream-including fusiform gyrus and primary visual cortex-and ventromedial prefrontal cortex (VMPFC). Two key results indicate these regions may contain statistically discriminable patterns that contain different informational representations. First, cross-participant analyses implicated additional brain regions, including striatum and anterior insula. The cross-participant analyses also revealed systematic changes in predictive power across brain regions, with the pattern of change consistent with the functional properties of regions. Second, individual differences in classifier performance in VMPFC were related to individual differences in preferences between our two reward modalities. We interpret these results as reflecting a distinction between patterns showing participant-specific functional organization and those indicating aspects of brain organization that generalize across individuals.

Nucleus Accumbens Mediates Relative Motivation for Rewards in the Absence of Choice

To dissociate a choice from its antecedent neural states, motivation associated with the expected outcome must be captured in the absence of choice. Yet, the neural mechanisms that mediate behavioral idiosyncrasies in motivation, particularly with regard to complex economic preferences, are rarely examined in situations without overt decisions. We employed functional magnetic resonance imaging in a large sample of participants while they anticipated earning rewards from two different modalities: monetary and candy rewards. An index for relative motivation toward different reward types was constructed using reaction times to the target for earning rewards. Activation in the nucleus accumbens (NAcc) and anterior insula (aINS) predicted individual variation in relative motivation between our reward modalities. NAcc activation, however, mediated the effects of aINS, indicating the NAcc is the likely source of this relative weighting. These results demonstrate that neural idiosyncrasies in reward efficacy exist even in the absence of explicit choices, and extend the role of NAcc as a critical brain region for such choice-free motivation.

Functional connectivity with ventromedial prefrontal cortex reflects subjective value for social rewards

According to many studies, the ventromedial prefrontal cortex (VMPFC) encodes the subjective value of disparate rewards on a common scale. Yet, a host of other reward factors-likely represented outside of VMPFC-must be integrated to construct such signals for valuation. Using functional magnetic resonance imaging (fMRI), we tested whether the interactions between posterior VMPFC and functionally connected brain regions predict subjective value. During fMRI scanning, participants rated the attractiveness of unfamiliar faces. We found that activation in dorsal anterior cingulate cortex, anterior VMPFC and caudate increased with higher attractiveness ratings. Using data from a post-scan task in which participants spent money to view attractive faces, we quantified each individuals subjective value for attractiveness. We found that connectivity between posterior VMPFC and regions frequently modulated by social information-including the temporal-parietal junction (TPJ) and middle temporal gyrus-was correlated with individual differences in subjective value. Crucially, these additional regions explained unique variation in subjective value beyond that extracted from value regions alone. These findings indicate not only that posterior VMPFC interacts with additional brain regions during valuation, but also that these additional regions carry information employed to construct the subjective value for social reward.

Local Pattern Classification Differentiates Processes of Economic Valuation

For effective decision making, individuals must be able to form subjective values from many types of information. Yet, the neural mechanisms that underlie potential differences in value computation across different decision scenarios are incompletely understood. Here, we used functional magnetic resonance imaging (fMRI), in conjunction with the machine learning technique of support vector machines (SVM), to identify brain regions that contain unique local information associated with different types of valuation. We used a combinatoric approach that evaluated the unique contributions of different brain regions to model generalization strength. Local voxel patterns in left posterior parietal cortex contained unique information differentiating probabilistic and intertemporal valuation, a result that was not accessible using standard fMRI analyses. We conclude that the early valuation phases for these reward types differ on a fine spatial scale, suggesting the existence of computational topographies along the value construction pathway.

Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches

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 component analysis (ICA). We estimated voxel-wise 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.

Foundations of neuroeconomics: from philosophy to practice.

How can we use neuroscience to better understand economic behavior? By quelling concerns about the nascent field of neuroeconomics, the authors defend future integrations of the biological and social sciences.