Neural Decision-Making and Affective Dynamics in Weight Regain after Metabolic and Bariatric Surgery: A Multimodal Longitudinal Computational Psychiatry Study

To investigate the relationship between neural decision signals and daily emotional dynamics in weight regain after metabolic and bariatric surgery.

Study Design: A multimodal longitudinal computational psychiatry study was developed to analyze weight regain in postoperative patients.
Standardization of Food Stimuli: Food stimuli were standardized across subjective, nutritional, reward-control, and visual dimensions.
Neural Measures: Clinical EEG, ERP/frontal theta measures, and drift-diffusion modeling were utilized to characterize decision-making processes.
Dynamic Network Approaches: Dynamic network methods like GIMME were employed to model temporal processes affecting weight regain.

Postoperative weight regain is influenced by the interaction of neural decision signals and emotional dynamics.
Food-cue reactivity can amplify cravings and affect eating behavior.
Negative affect and loss-of-control eating show time-varying relationships in daily life.

The study seeks to elucidate how cognitive and emotional factors contribute to weight regain after surgery.

Challenges in reproducibility and parameter reliability of computational models.
Potential biases in self-reported measures of affect and eating behavior.

Integrating computational psychiatry with clinical research may improve understanding of mechanisms behind postoperative weight regain.