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

The ability to predict an impending threat during Pavlovian conditioning diminishes the emotional responsethat is produced once the threat is encountered. Diminution of the threat response appears to be mediated by somewhatindependent associative learning and expectancy-related processes. Therefore, the present study was designed to betterunderstand the neural mechanisms that support associative learning processes, independent of expectancy, that influencethe emotional response to a threat. Healthy volunteers took part in a Pavlovian conditioning procedure during which traitanxiety, expectation of the unconditioned stimulus (UCS), skin conductance response (SCR), and functional magneticresonance imaging (fMRI) signal were assessed. The results showed no evidence for associative learning that wasindependent of expectation. Threat-related SCR expression was diminished on predictable trials vs. unpredictable trials ofthe UCS (i.e. conditioned UCR diminution). Similar to SCR, conditioned UCR diminution was observed within the leftdorsolateral PFC, dorsomedial PFC, ventromedial PFC, and left anterior insula. In contrast, potentiation of the threat-relatedfMRI signal response was observed within left dorsolateral PFC, inferior parietal lobule (IPL), and posteriorinsula. A negative relationship was observed between UCS expectancy and UCR expression within the dorsomedial PFC,ventromedial PFC, and anterior insula. Finally, the anticipatory fMRI signal responses within the PFC, posteriorcingulate, and amygdala showed an inverse relationship with threat-related activation within the brain regions that showedUCR diminution. The current findings suggest that the PFC and amygdala support learning-related processes that impactthe magnitude of the emotional response to a threat.

Previous studies showed alterations of brain function in the ventromedial prefrontal cortex of schizophrenia patients. Also, neurochemical changes, especially GABA level alteration, have been found in the medial prefrontal cortex of schizophrenia patients. However, the relationship between GABA level in the ventromedial prefrontal cortex and brain functional activity in schizophrenia patients remains unexplored. In total, 23 drug-naïve, first-episode psychosis patients and 26 matched healthy controls completed the study. The single voxel proton magnetic resonance spectroscopy data were acquired in ventromedial prefrontal cortex region, which was used as the seed region for resting-state functional connectivity analysis. The proton magnetic resonance spectroscopy data were processed to quantify the concentrations of GABA+, glutamine and glutamate, and N-acetylaspartate in ventromedial prefrontal cortex. Spearman correlation analysis was used to examine the relationship between metabolite concentration, functional connectivity and clinical variables. Pearson correlation analysis was used to examine the relationship between GABA+ concentration and functional connectivity value. In first-episode psychosis patients, GABA+ level in ventromedial prefrontal cortex was higher and was positively correlated with ventromedial prefrontal cortex-left middle orbital frontal cortex functional connectivity. N-acetylaspartate level was positively correlated with positive symptoms, and the functional connectivity between ventromedial prefrontal cortex and left precuneus was negatively associated with negative symptoms of first-episode psychosis patients. Our results indicated that ventromedial prefrontal cortex functional connectivity changes were positively correlated with higher local GABA+ level in first-episode psychosis patients. The altered neurochemical concentration and functional connectivity provide insights into the pathology of schizophrenia.

Summary“Near-miss” events, where unsuccessful outcomes are proximal to the jackpot, increase gambling propensity and may be associated with the addictiveness of gambling, but little is known about the neurocognitive mechanisms that underlie their potency. Using a simplified slot machine task, we measured behavioral and neural responses to gambling outcomes. Compared to “full-misses,” near-misses were experienced as less pleasant, but increased desire to play. This effect was restricted to trials where the subject had personal control over arranging their gamble. Near-miss outcomes recruited striatal and insula circuitry that also responded to monetary wins; in addition, near-miss-related activity in the rostral anterior cingulate cortex varied as a function of personal control. Insula activity to near-misses correlated with self-report ratings as well as a questionnaire measure of gambling propensity. These data indicate that near-misses invigorate gambling through the anomalous recruitment of reward circuitry, despite the objective lack of monetary reinforcement on these trials.

Many real-life decision making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as ventromedial prefrontal cortex, employ a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure, as in standard reinforcement learning. To discriminate between these possibilities we scanned human subjects with fMRI while they performed two different decision making tasks with higher-order structure: probabilistic reversal learning, in which subjects had to infer which of two choices was the more rewarding and then flexibly switch their choice when contingencies changed; and the inspection game, in which subjects had to successfully compete against an intelligent adversary by mentalizing the opponent’s state of mind in order to anticipate the opponent’s behavior in future. For both tasks we found that neural activity in a key decision making region: ventromedial prefrontal cortex, was more consistent with computational models that exploit higher-order structure, than with simple reinforcement learning. Moreover, in the social interaction game, subjects were found to employ a sophisticated strategy whereby they used knowledge of how their actions would influence the actions of their opponent to guide their choices. Specific computational signals required for the implementation of such a strategy were present in medial prefrontal cortex and superior temporal sulcus, providing insight into the basic computations underlying competitive strategic interactions. These results suggest that brain regions such as ventromedial prefrontal cortex employ an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.

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 individual's 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.

Autism spectrum disorders (ASDs) and social anxiety disorder (SAD) are both characterized by social dysfunction, but no study to date has compared neural responses to social rewards in ASDs and SAD. Neural responses during social and non-social reward anticipation and outcomes were examined in individuals with ASD (n = 16), SAD (n = 15) and a control group (n = 19) via functional magnetic resonance imaging. Analyses modeling all three groups revealed increased nucleus accumbens (NAc) activation in SAD relative to ASD during monetary reward anticipation, whereas both the SAD and ASD group demonstrated decreased bilateral NAc activation relative to the control group during social reward anticipation. During reward outcomes, the SAD group did not differ significantly from the other two groups in ventromedial prefrontal cortex activation to either reward type. Analyses comparing only the ASD and SAD groups revealed greater bilateral amygdala activation to social rewards in SAD relative to ASD during both anticipation and outcome phases, and the magnitude of left amygdala hyperactivation in the SAD group during social reward anticipation was significantly correlated with the severity of trait anxiety symptoms. Results suggest reward network dysfunction to both monetary and social rewards in SAD and ASD during reward anticipation and outcomes, but that NAc hypoactivation during monetary reward anticipation differentiates ASD from SAD.

Differential Neural Activation for Updating Rule versus Stimulus Information in Working Memory

Little is known about the neural networks supporting value computation during complex social decisions. We investigated this question using functional magnetic resonance imaging while subjects made donations to different charities. We found that the blood oxygenation level-dependent signal in ventral medial prefrontal cortex (VMPFC) correlated with the subjective value of voluntary donations. Furthermore, the region of the VMPFC identified showed considerable overlap with regions that have been shown to encode for the value of basic rewards at the time of choice, suggesting that it might serve as a common valuation system during decision making. In addition, functional connectivity analyses indicated that the value signal in VMPFC might integrate inputs from networks, including the anterior insula and posterior superior temporal cortex, that are thought to be involved in social cognition.

During the transformative period of adolescence, social influence plays a prominent role in shaping young people's emerging social identities, and can impact their propensity to engage in prosocial or risky behaviors. In this study, we examine the neural correlates of social influence from both parents and peers, two important sources of influence. Nineteen adolescents (age 16-18 years) completed a social influence task during a functional magnetic resonance imaging (fMRI) scan. Social influence from both sources evoked activity in brain regions implicated in mentalizing (medial prefrontal cortex, left temporoparietal junction, right temporoparietal junction), reward (ventromedial prefrontal cortex), and self-control (right ventrolateral prefrontal cortex). These results suggest that mental state reasoning, social reward and self-control processes may help adolescents to evaluate others' perspectives and overcome the prepotent force of their own antecedent attitudes to shift their attitudes toward those of others. Findings suggest common neural networks involved in social influence from both parents and peers.

A Double Dissociation of Ventromedial Prefrontal Cortical Responses to Sad and Happy Stimuli in Depressed and Healthy Individuals

When we make decisions, the benefits of an option often need to be weighed against accompanying costs. Little is known, however, about the neural systems underlying such cost-benefit computations. Using functional magnetic resonance imaging and choice modeling, we show that decision making based on cost-benefit comparison can be explained as a stochastic accumulation of cost-benefit difference. Model-driven functional MRI shows that ventromedial and left dorsolateral prefrontal cortex compare costs and benefits by computing the difference between neural signatures of anticipated benefits and costs from the ventral striatum and amygdala, respectively. Moreover, changes in blood oxygen level dependent (BOLD) signal in the bilateral middle intraparietal sulcus reflect the accumulation of the difference signal from ventromedial prefrontal cortex. In sum, we show that a neurophysiological mechanism previously established for perceptual decision making, that is, the difference-based accumulation of evidence, is fundamental also in value-based decisions. The brain, thus, weighs costs against benefits by combining neural benefit and cost signals into a single, difference-based neural representation of net value, which is accumulated over time until the individual decides to accept or reject an option.

Modeling heterogeneity in field crops is a key issue for a better characterization of field production. This paper presents some experimental data on sugar beet illustrating this heterogeneity. Several sources of individual variability within plant populations are identified: namely, initial condition (seed biomass, emergence delay), genetic variability (including phyllochron) and environment (including spacing and competition). A mathematical framework is introduced to integrate the different sources of variability in plant growth models. It is based on the classical method of Taylor Series Expansion, which allows the propagation of uncertainty in the dynamic system of growth and the computation of the approximate means and standard deviations of the model outputs. The method is applied to the GreenLab model of plant growth and more specifically to sugar beet. It opens perspectives in order to assess the different sources of variability in plant populations and estimate their parameters from experimental data. However important issues like optimization of data collection and system identifiability have to be resolved first, since the uncertainty effects may be mixed in an inextricable way or may necessitate a too huge amount of experimental data for their estimation.

How semantic representations are manifest over the brain remains a topic of active debate. A semantic representation may be determined by specific semantic features (e.g. sensorimotor information), or may abstract away from specific features and represent generalized semantic characteristics (general semantic representation). Here we tested whether nodes of the semantic system code for a general semantic representation and/or possess representational spaces linked to particular semantic features. In an fMRI study, eighteen participants performed a typicality judgment task with written words drawn from sixteen different categories. Multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) were adopted to investigate the sensitivity of the brain regions to semantic content and the type of semantic representation coded (general or feature-based). We replicated previous findings of sensitivity to general semantic similarity in posterior middle/inferior temporal gyrus (pMTG/ITG) and precuneus (PC) and additionally observed general semantic representations in ventromedial prefrontal cortex (PFC). Finally, two brain regions of the semantic network were sensitive to semantic features: the left pMTG/ITG was sensitive to haptic perception and the left ventral temporal cortex (VTC) to size. This finding supports the involvement of both general semantic representation and feature-based representations in the brain’s semantic system.

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Anxiety is linked to compromised interactions between the amygdala and the dorsal and ventral medial prefrontal cortex (mPFC). While numerous task-based neuroimaging studies show that anxiety levels predict amygdala-mPFC connectivity and response magnitude, here we tested the hypothesis that anxiety would predict functional connectivity between these brain regions even during rest. Resting-state functional magnetic resonance imaging scans and self-reported measures of anxiety were acquired from healthy subjects. At rest, individuals with high anxiety were characterized by negatively correlated amygdala-ventral mPFC functional connectivity, while low anxious subjects showed positively correlated activity. Further, high anxious subjects showed amygdala-dorsal mPFC activity that was uncorrelated, while low anxious subjects showed negatively correlated activity. These data show that amygdala-mPFC connectivity at rest indexes normal individual differences in anxiety.