R. McKell Carter

A nexus model of the temporal–parietal junction

The temporal-parietal junction (TPJ) has been proposed to support either specifically social functions or non-specific processes of cognition such as memory and attention. To account for diverse prior findings, we propose a nexus model for TPJ function: overlap of basic processes produces novel secondary functions at their convergence. We present meta-analytic evidence that is consistent with the anatomical convergence of attention, memory, language, and social processing in the TPJ, leading to a higher-order role in the creation of a social context for behavior. The nexus model accounts for recent examples of TPJ contributions specifically to decision making in a social context and provides a potential reconciliation for competing claims about TPJ function.

A Distinct Role of the Temporal-Parietal Junction in Predicting Socially Guided Decisions

You Must Be Human Are there specific brain structures associated with social cognition or with aspects of information processing that frequently occur together with social cognition? Carter et al. (p. 109) invited subjects to play a simplified virtual poker game against either a human or a computer and examined brain scans collected during the game. The brains scans when the cards shown were used to predict the participants decision 6 seconds later. Activity in one region, the temporal-parietal junction, was one of the best predictors of future decisions against a human opponent, but the single worst predictor against a computer opponent. A single region of the brain is particularly engaged when one is beating an opponent at poker. To make adaptive decisions in a social context, humans must identify relevant agents in the environment, infer their underlying strategies and motivations, and predict their upcoming actions. We used functional magnetic resonance imaging, in conjunction with combinatorial multivariate pattern analysis, to predict human participants’ subsequent decisions in an incentive-compatible poker game. We found that signals from the temporal-parietal junction provided unique information about the nature of the upcoming decision, and that information was specific to decisions against agents who were both social and relevant for future behavior.

Activation in the VTA and nucleus accumbens increases in anticipation of both gains and losses

To represent value for learning and decision making, the brain must encode information about both the motivational relevance and affective valence of anticipated outcomes. The nucleus accumbens (NAcc) and ventral tegmental area (VTA) are thought to play key roles in representing these and other aspects of valuation. Here, we manipulated the valence (i.e., monetary gain or loss) and personal relevance (i.e., self-directed or charity-directed) of anticipated outcomes within a variant of the monetary incentive delay task. We scanned young-adult participants using functional magnetic resonance imaging (fMRI), utilizing imaging parameters targeted for the NAcc and VTA. For both self-directed and charity-directed trials, activation in the NAcc and VTA increased to anticipated gains, as predicted by prior work, but also increased to anticipated losses. Moreover, the magnitude of responses in both regions was positively correlated for gains and losses, across participants, while an independent reward-sensitivity covariate predicted the relative difference between and gain- and loss-related activation on self-directed trials. These results are inconsistent with the interpretation that these regions reflect anticipation of only positive-valence events. Instead, they indicate that anticipatory activation in reward-related regions largely reflects the motivational relevance of an upcoming event.

Resting state networks distinguish human ventral tegmental area from substantia nigra.

Dopaminergic networks modulate neural processing across a spectrum of function from perception to learning to action. Multiple organizational schemes based on anatomy and function have been proposed for dopaminergic nuclei in the midbrain. One schema originating in rodent models delineated ventral tegmental area (VTA), implicated in complex behaviors like addiction, from more lateral substantia nigra (SN), preferentially implicated in movement. However, because anatomy and function in rodent midbrain differs from the primate midbrain in important ways, the utility of this distinction for human neuroscience has been questioned. We asked whether functional definition of networks within the human dopaminergic midbrain would recapitulate this traditional anatomical topology. We first developed a method for reliably defining SN and VTA in humans at conventional MRI resolution. Hand-drawn VTA and SN regions-of-interest (ROIs) were constructed for 50 participants, using individually-localized anatomical landmarks and signal intensity. Individual segmentation was used in seed-based functional connectivity analysis of resting-state functional MRI data; results of this analysis recapitulated traditional anatomical targets of the VTA versus SN. Next, we constructed a probabilistic atlas of the VTA, SN, and the dopaminergic midbrain region (comprised of SN plus VTA) from individual hand-drawn ROIs. The combined probabilistic (SN plus VTA) ROI was then used for connectivity-based dual-regression analysis in two independent resting-state datasets (n = 69 and n = 79). Results of the connectivity-based, dual-regression functional segmentation recapitulated results of the anatomical segmentation, validating the utility of this probabilistic atlas for future research.

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.

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.

Neurocognitive Development of Risk Aversion from Early Childhood to Adulthood

Human adults tend to avoid risk. In behavioral economic studies, risk aversion is manifest as a preference for sure gains over uncertain gains. However, children tend to be less averse to risk than adults. Given that many of the brain regions supporting decision-making under risk do not reach maturity until late adolescence or beyond it is possible that mature risk-averse behavior may emerge from the development of decision-making circuitry. To explore this hypothesis, we tested 5- to 8-year-old children, 14- to 16-year-old adolescents, and young adults in a risky-decision task during functional magnetic resonance imaging (fMRI) data acquisition. To our knowledge, this is the youngest sample of children in an fMRI decision-making task. We found a number of decision-related brain regions to increase in activation with age during decision-making, including areas associated with contextual memory retrieval and the incorporation of prior outcomes into the current decision-making strategy, e.g., insula, hippocampus, and amygdala. Further, children who were more risk-averse showed increased activation during decision-making in ventromedial prefrontal cortex and ventral striatum. Our findings indicate that the emergence of adult levels of risk aversion co-occurs with the recruitment of regions supporting decision-making under risk, including the integration of prior outcomes into current decision-making behavior. This pattern of results suggests that individual differences in the development of risk aversion may reflect differences in the maturation of these neural processes.

What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.