Multiple Dissociations Between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence From Computationally Informed EEG

Computational Psychiatry

卷:3

Michael J. Frank¹  John J. B. Allen²  James F. Cavanagh³  Andrew W. Bismark⁴ 

[1] Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island, USA;

[2] Department of Psychology, University of Arizona, Tucson, Arizona, USA;

[3] Department of Psychology, University of New Mexico, Albuquerque, New Mexico, USA;

[4] VA San Diego Healthcare System, San Diego, California, USA;

关键词: depression;    anxiety;    FRN;    Rew-P;    reinforcement learning;    computational psychiatry;   

DOI  :  10.1162/cpsy_a_00024

来源: DOAJ

【 摘 要 】

In this report, we provide the first evidence that mood and anxiety dimensions are associated with unique aspects of EEG responses to reward and punishment, respectively. We reanalyzed data from our prior publication of a categorical depiction of depression to address more sophisticated dimensional hypotheses. Highly symptomatic depressed individuals (N = 46) completed a probabilistic learning task with concurrent EEG. Measures of anxiety and depression symptomatology were significantly correlated with each other; however, only anxiety predicted better avoidance learning due to a tighter coupling of negative prediction error signaling with punishment-specific EEG features. In contrast, depression predicted a smaller reward-related EEG feature, but this did not affect prediction error coupling or the ability to learn from reward. We suggest that this reward-related alteration reflects motivational or hedonic aspects of reward and not a diminishment in the ability to represent the information content of reinforcements. These findings compel further research into the domain-specific neural systems underlying dimensional aspects of psychiatric disease.

【 授权许可】

Unknown