Search

A good-based model, the central neurobiological model of economic decision-making, proposes that the orbitofrontal cortex (OFC) represents binary choice outcome, that is, the chosen good. A good is defined by a group of determinants characterizing the conditions in which the commodity is offered, including commodity type, cost, risk, time delay, and ambiguity. Previous studies have found that the OFC represents the binary choice outcome in decision-making tasks involving commodity type, cost, risk, and delay. Real-life decisions are often complex and involve uncertainty, rewards, and penalties; however, whether the OFC represents binary choice outcomes in a complex decision-making situation, for example, Iowa gambling task (IGT), remains unclear. Here, we propose that the OFC represents binary choice outcome, that is, advantageous choice versus disadvantageous choice, in the IGT. We propose two hypotheses: first, the activity pattern in the human OFC represents an advantageous choice; and second, choice induces an OFC-related functional network. Using functional magnetic resonance imaging and advanced machine-learning tools, we found that the OFC represented an advantageous choice in the IGT. The OFC representation of advantageous choice was related to decision-making performance. Choice modulated the functional connectivity between the OFC and the superior medial gyrus. In conclusion, the OFC represents an advantageous choice during the IGT. In the framework of a good-based model, the results extend the role of the OFC to complex decision-making situation when making a binary choice.

As the previous studies have mainly focused on the reward system and the corresponding brain regions, the relationship between brain morphology and excessive internet use (EIU) were not clear; the purpose of the study was to investigate if the brain regions other than the reward system were associated with EIU. Data were acquired from 131 excessive internet users. Psychological measures included internet use, life quality, personality, mental illness symptoms, impulsivity, and thought suppression. The brain was scanned with 3T magnetic resonance imaging (MRI) and six types of brain morphological indexes were calculated. Lasso regression methods were used to select the predictors. Stepwise linear regression methods were used to build the models and verify the model. The variables remaining in the model were left precentral (curve), left superior temporal (surface area), right cuneus (folding index), right rostral anterior cingulate (folding index), and harm avoidance. The independent variable was the EIU score of the worst week in the past year. The study found that the brain morphological indexes other than the reward system, including the left precentral (curve), the left superior temporal (surface area), the right cuneus (folding index), and the right rostral anterior cingulate (folding index), can predict the severity of EIU, suggesting an extensive change in the brain. In this study, a whole-brain data analysis was conducted and it was concluded that the changes in certain brain regions were more predictive than the reward system and psychological measures or more important for EIU.

Background Chronic obstructive pulmonary disease (COPD) is a respiratory disease that causes a wide range of cognitive impairments. Although COPD-Smoking comorbidity is common, the relationship between smoking and cognitive function in COPD-Smoking comorbidity remains unclear. This study aimed to investigate the effect of smoking on cognitive function like attention in COPD-Smoking patients. Methods In this study, we used the Montreal Cognitive Assessment (MoCA) scale and resting-state functional magnetic resonance imaging (fMRI) to explore the effect of smoking on attention in patients with COPD. Results Behavioral analysis revealed that among patients with COPD the smokers had a shorter course of COPD and showed a worse attention performance than the non-smokers. Resting-state fMRI analysis revealed that among patients with COPD smokers showed lower regional homogeneity (ReHo) value of the fusiform gyrus than non-smokers. Importantly, the ReHo of the fusiform gyrus is positively associated with attention and mediates the effect of smoking on attention in COPD. Conclusions In summary, our study provides behavioral and neurobiological evidence supporting the positive effect of smoking on attention in COPD. This may be helpful for understanding and treating COPD and even other diseases comorbid with smoking.

Learning of prediction error (PE), including reward PE and risk PE, is crucial for updating the prediction in reinforcement learning (RL). Neurobiological and computational models of RL have reported extensive brain activations related to PE. However, the occurrence of PE does not necessarily predict updating the prediction, e.g., in a probability-known event. Therefore, the brain regions specifically engaged in updating the prediction remain unknown. Here, we conducted two functional magnetic resonance imaging (fMRI) experiments, the probability-unknown Iowa Gambling Task (IGT) and the probability-known risk decision task (RDT). Behavioral analyses confirmed that PEs occurred in both tasks but were only used for updating the prediction in the IGT. By comparing PE-related brain activations between the two tasks, we found that the rostral anterior cingulate cortex/ventral medial prefrontal cortex (rACC/vmPFC) and the posterior cingulate cortex (PCC) activated only during the IGT and were related to both reward and risk PE. Moreover, the responses in the rACC/vmPFC and the PCC were modulated by uncertainty and were associated with reward prediction-related brain regions. Electric brain stimulation over these regions lowered the performance in the IGT but not in the RDT. Our findings of a distributed neural circuit of PE processing suggest that the rACC/vmPFC and the PCC play a key role in updating the prediction through PE processing during decision making.

The processes involved in value evaluation and self-control are critical when making behavioral choices. However, the evidence linking these two types of processes to behavioral choices in intertemporal decision-making remains elusive. As the ventromedial prefrontal cortex (vmPFC), striatum, and dorsolateral prefrontal cortex (dlPFC) have been associated with these two processes, we focused on these three regions. We employed functional magnetic resonance imaging during a delayed discounting task (DDT) using a relatively large sample size, three independent samples. We evaluated how much information about a specific choice could be decoded from local patterns in each brain area using multivoxel pattern analysis (MVPA). To investigate the relationship between the dlPFC and vmPFC/striatum regions, we performed a psychophysiological interaction (PPI) analysis. In Experiment I, we found that the vmPFC and dlPFC, but not the striatum, could determine choices in healthy participants. Furthermore, we found that the dlPFC showed significant functional connectivity with the vmPFC, but not the striatum, when making decisions. These results could be replicated in Experiment II with an independent sample of healthy participants. In Experiment III, the choice-decoding accuracy in the vmPFC and dlPFC was lower in patients with addiction (smokers and participants with Internet gaming disorder) than in healthy participants, and decoding accuracy in the dlPFC was related to impulsivity in addicts. Taken together, our findings may provide neural evidence supporting the hypothesis that value evaluation and self-control processes both guide the intertemporal choices, and might provide potential neural targets for the diagnosis and treatment of impulsivity-related brain disorders.

Deficits in the computational processes of reinforcement learning have been suggested to underlie addiction. Additionally, environmental uncertainty, which is encoded in the anterior cingulate cortex (ACC), modulates reward prediction errors (RPEs) during reinforcement learning and exacerbates addiction. The present study tested whether and how the ACC would have an essential role in drug addiction by failing to use uncertainty to modulate the RPEs during reinforcement learning. In Experiment I, we found that the ACC/medial prefrontal cortex (MPFC) did not modulate RPE learning according to uncertainty in smokers. The effect of uncertainty × RPE in the ACC/MPFC was correlated with the learning rate of RPEs and the duration of nicotine use. Experiment II demonstrated that serotonin, but not dopamine, receptor mRNA expression significantly decreased in the ACC of the nicotine exposed compared to the control rats. Furthermore, there was a positive correlation between learning rate and serotonin receptor mRNA expression in the ACC. Therefore, all present results suggest that impairments in uncertainty modulation in the ACC disrupt reinforcement learning processes in chronic nicotine users and contribute to maladaptive decision-making. These findings support interventions for pathological decision-making in drug addiction that strongly focus on the serotonin system in ACC.

Nicotine addiction is associated with risky behaviors and abnormalities in local brain areas related to risky decision-making such as the dorsal anterior cingulate cortex (dACC), anterior insula (AI) and thalamus. Although these brain abnormalities are anatomically separated, they may in fact belong to one neural network. However, it is unclear whether circuit-level abnormalities lead to risky decision-making in smokers. In the current study, we used task-based functional magnetic resonance imaging (fMRI) and examined resting-state functional connectivity (RSFC) to study how connectivity between the dACC, insula and thalamus influence risky decision-making in nicotine addicts. We found that an increase in risky decision-making was associated with stronger nicotine dependence and stronger RSFC of the dACC-rAI (right AI), the dACC-thalamus, the dACC-lAI (left AI) and the rAI-lAI, but that risky decision-making was not associated with risk level-related activation. Furthermore, the severity of nicotine dependence positively correlated with RSFC of the dACC-thalamus but was not associated with risk level-related activation. Importantly, the dACC-thalamus coupling fully mediated the effect of nicotine-dependent severity on risky decision-making. These results suggest that circuit-level connectivity may be a critical neural link between risky decision-making and severity of nicotine dependence in smokers.

Novelty seeking (NS) is a personality trait reflecting excitement in response to novel stimuli. High NS is usually a predictor of risky behaviour such as drug abuse. However, the relationships between NS and risk-related cognitive processes, including individual risk preference and the brain activation associated with risk prediction, remain elusive. In this fMRI study, participants completed the Tridimensional Personality Questionnaire to measure NS and performed a probabilistic decision making task. Using a mathematical model, we estimated individual risk preference. Brain regions associated with risk prediction were determined via fMRI. The NS score showed a positive correlation with risk preference and a negative correlation with the activation elicited by risk prediction in the right posterior insula (r-PI), left anterior insula (l-AI), right striatum (r-striatum) and supplementary motor area (SMA). Within these brain regions, only the activation associated with risk prediction in the r-PI showed a correlation with NS after controlling for the effect of risk preference. Resting-state functional connectivity between the r-PI and r-striatum/l-AI was negatively correlated with NS. Our results suggest that high NS may be associated with less aversion to risk and that the r-PI plays an important role in relating risk prediction to NS.