其他摘要 | Major depressive disorder (MDD) is a common mental disorder that impairs social function. Social cognition is an important factor in determining social functioning. More and more researchers are beginning to focus on the social cognitive impairments in MDD. Social norms are behavioral guidelines that apply to interpersonal interactions, allowing people to identify and respond to behavior that violates social norms, demonstrating norm adaptation. Studying the social cognition from the perspective of norm adaptation provides a new approach to MDD studies. Fairness is an important social norm, often studied by using the ultimatum game (UG) in the laboratory. Previous studies have found that depressed patients exhibited abnormal behavior as responders in the UG, suggesting a maladaptation to social norms. However, there is currently no empirical researches on this, and it is not clear about the neural basis of poor social norm adaptation in depressed patients. To explore the psychological mechanisms and neural basis behind abnormal ultimatum game behavior in patients with MDD, this study recruited patients with MDD and healthy controls to play the ultimatum game as responders and conducted two studies from the perspective of norm adaptation.
The aim of the study 1 was to verify whether decision-making behavior during the UG was abnormal in patients with MDD and then to explore the underlying psychological mechanisms from the perspective of norm adaptation through computational modeling analysis. The participants in this study were from two different datasets (dataset 1:121 participants, patients/healthy controls: 66/55, male/female: 51/70, mean age 26.96 years; dataset 2: 101 participants, patients/healthy controls: 52/49, male/female: 34/67, mean age 27.61 years). Conventional statistical analysis and computational modeling analysis were performed on the decision-making behavior of each dataset and the merged dataset. Using conventional statistical analysis, the study found that depressed patients exhibited abnormal decision-making behavior in the UG, as their acceptance rates were significantly lower than those of healthy controls in all three datasets (ps<0.001).Furthermore, computational modeling analysis revealed that in all three datasets, depressed patients had significantly lower fairness norm learning rates than healthy controls (dataset 1:89% HDIs: [-0.07,-0.01】;dataset 2: 89% HDIs: [-0.10,-0.02]; merged dataset: 89% HDIs: [-0.07,-0.03]); in the merged dataset, patients were significantly more sensitive to unfairness than healthy controls(βh、:89% HDIs: [0.00, 0.04];βc。:89% HDIs: [0.00, 0.05]).
Study 2 aimed to explore the neural basis of abnormal ultimatum game behavior in patients with MDD from the perspective of social norm adaptation by combining computational modeling and functional neuroimaging analysis methods. 52 patients and 49 healthy controls from dataset 2 were included in the study, and all participants completed the UG task as responders while being scanned in the scanner. The study used model-based functional neuroimaging analysis methods to examine the differences in brain activity modulated by key parameters of the computational model, such as norm prediction error (PE), between patients with MDD and healthy controls during the UG task. The study also examined the relationship between key parameters in the best model and brain activity and furthermore investigated whether brain activity mediated the relationship between participant type and behavioral performance (model key parameters) and thus explored the neural basis of abnormal behavior in patients with MDD. The main results of the study are as follows: (1) using brain activation detection analysis, the study found that the interaction effect of participant type and fairness level significantly influenced the activation level of the right inferior cerebellar (cluster level FWE p<0.05); (2) using parameter analysis with PE as a key parameter of the computational model, the study found that as the PE increased, the degree of negative modulation of brain activity in the left middle occipital gyros, right cerebellum, angular gyros, and middle frontal gyros in the healthy control group was higher than that in the patient group (cluster level FWE p<0.05); (3) the brain-behavioral analysis between the learning rate and the brain activity modulated by PE found that the degree of modulation of brain activity in multiple brain regions by PE was negatively correlated with the learning rate, including the medial prefrontal cortex, bilateral middle frontal gyri, right inferior parietal lobule, left inferior frontal gyros, and left anterior insula. Further analysis found that the degree of modulation of the medial prefrontal cortex, left middle frontal gyros, right inferior parietal lobule, and left anterior insula by PE mediated the relationship between subject type and learning rate.
In summary, this study innovatively adopts a computational modeling analysis method and traditional behavioral analysis methods, and explores the psychological mechanism of depressed patients' abnormal decision-making behavior from the perspective of social norm adaptation through behavioral experiments. And by combining computational modelling and functional neuroimaging analyses, this study explores the neural basis behind depressed patients' abnormal behavior. The results of this study confirmed the existence of abnormal decision-making behavior in UG in patients with MDD, revealed the poor norm adaptation in these patients indicated by the reduced learning rate through computational modeling analysis, and also revealed that the brain activities modulated by PE in prefrontal cortex, anterior insula and other brain regions play an important role in explaining poor fairness norm adaptation in patients with MDD. These findings deepen the understanding of the social dysfunction in patients with MDD, and provide a theoretical basis for the development of interventions and rehabilitation measures to improve the social function of patients with MDD. |
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