Neural Representation of Costs and Rewards in Decision Making

The role of the human hippocampus in decision-making under uncertainty

The role of the hippocampus in decision-making is beginning to be more understood. Because of its prospective and inferential functions, we hypothesized that it might be required specifically when decisions involve the evaluation of uncertain values. A group of individuals with autoimmune limbic encephalitis-a condition known to focally affect the hippocampus-were tested on how they evaluate reward against uncertainty compared to reward against another key attribute: physical effort. Across four experiments requiring participants to make trade-offs between reward, uncertainty and effort, patients with acute limbic encephalitis demonstrated blunted sensitivity to reward and effort whenever uncertainty was considered, despite demonstrating intact uncertainty sensitivity. By contrast, the valuation of these two attributes (reward and effort) was intact on uncertainty-free tasks. Reduced sensitivity to changes in reward under uncertainty correlated with the severity of hippocampal damage. Together, these findings provide evidence for a context-sensitive role of the hippocampus in value-based decision-making, apparent specifically under conditions of uncertainty.

A corticoamygdalar pathway controls reward devaluation and depression using dynamic inhibition code

Reward devaluation adaptively controls reward intake. It remains unclear how cortical circuits causally encode reward devaluation in healthy and depressed states. Here, we show that the neural pathway from the anterior cingulate cortex (ACC) to the basolateral amygdala (BLA) employs a dynamic inhibition code to control reward devaluation and depression. Fiber photometry and imaging of ACC pyramidal neurons reveal reward-induced inhibition, which weakens during satiation and becomes further attenuated in depression mouse models. Ablating or inhibiting these neurons desensitizes reward devaluation, causes reward intake increase and ultimate obesity, and ameliorates depression, whereas activating the cells sensitizes reward devaluation, suppresses reward consumption, and produces depression-like behaviors. Among various ACC neuron subpopulations, the BLA-projecting subset bidirectionally regulates reward devaluation and depression-like behaviors. Our study thus uncovers a corticoamygdalar circuit that encodes reward devaluation via blunted inhibition and suggests that enhancing inhibition within this circuit may offer a therapeutic approach for treating depression.

Potential biomarker of brain response to opioid antagonism in adolescents with eating disorders: a pilot study

Background

Eating Disorders (ED) affect up to 5% of youth and are associated with reward system alterations and compulsive behaviors. Naltrexone, an opioid antagonist, is used to treat ED behaviors such as binge eating and/or purging. The presumed mechanism of action is blockade of reward activation; however, not all patients respond, and the optimal dose is unknown. Developing a tool to detect objective drug response in the brain will facilitate drug development and therapeutic optimization. This pilot study evaluated neuroimaging as a pharmacodynamic biomarker of opioid antagonism in adolescents with ED.

Methods

Youth aged 13-21 with binge/purge ED completed functional magnetic resonance imaging (fMRI) pre- and post-oral naltrexone. fMRI detected blood oxygenation-level dependent (BOLD) signal at rest and during two reward probes (monetary incentive delay, MID, and passive food view, PFV) in predefined regions of interest associated with reward and inhibitory control. Effect sizes for Δ%BOLD (post-naltrexone vs. baseline) were estimated using linear mixed effects modeling.

Results

In 12 youth (16-21 years, 92% female), BOLD signal changes were detected following naltrexone in the nucleus accumbens during PFV (Δ%BOLD -0.08 ± 0.03; Cohen's d -1.06, p = 0.048) and anterior cingulate cortex during MID (Δ%BOLD 0.06 ± 0.03; Cohen's d 1.25, p = 0.086).

Conclusion

fMRI detected acute reward pathway modulation in this small sample of adolescents with binge/purge ED. If validated in future, larger trials, task-based Δ%BOLD detected by fMRI may serve as a pharmacodynamic biomarker of opioid antagonism to facilitate the development of novel therapeutics targeting the reward pathway, enable quantitative pharmacology trials, and inform drug dosing.

Clinical trial registration

https://clinicaltrials.gov/ct2/show/NCT04935931, NCT#04935931.

Assessing Distinct Cognitive Workload Levels Associated with Unambiguous and Ambiguous Pronoun Resolutions in Human–Machine Interactions

Pronoun resolution plays an important role in language comprehension. However, little is known about its recruited cognitive mechanisms. Our investigation aims to explore the cognitive mechanisms underlying various types of pronoun resolution in Chinese using an electroencephalograph (EEG). We used three convolutional neural networks (CNNs)—LeNeT-5, GoogleNet, and EffifcientNet—to discover high-level feature abstractions of the EEG spatial topologies. The output of the three models was then fused using different scales by principal component analysis (PCA) to achieve cognitive workload classification. Overall, the workload classification rate by fusing three deep networks can be achieved at 55–63% in a participant-specific manner. We provide evidence that both the behavioral indicator of reaction time and the neural indicator of cognitive workload collected during pronoun resolution vary depending on the type of the pronoun. We observed an increase in reaction time accompanied by a decrease of the theta power while participants were processing ambiguous pronoun resolution compared to unambiguous controls. We propose that ambiguous pronoun resolution involves a more time-consuming yet more flexible cognitive mechanism, consistent with the predictions of the decision-making framework from an influential pragmatic tradition. Our results extend previous research that the cognitive states of resolving ambiguous and unambiguous pronouns are differentiated, indicating that cognitive workload evaluated using the method of machine learning for analysis of EEG signals acts as a complementary indicator for studying pronoun resolution and serves as an important inspiration for human–machine interaction.