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

Obes Surg. 2026 Jul 10. doi: 10.1007/s11695-026-08834-8. Online ahead of print.

ABSTRACT

OBJECTIVE: To determine whether weight regain after metabolic bariatric surgery reflects a systematic reorganization of neural decision signals elicited by food cues and longitudinal affective states, and to develop an interpretable, trackable, and stratifiable multimodal computational psychiatry framework.

METHODS: This study was based on a multi-timepoint longitudinal cohort of patients undergoing primary metabolic bariatric surgery, integrating a standardized food-choice task, 64-channel EEG, psychometric measures, and longitudinal affective-state data. Food stimuli were first standardized across five domains: subjective experience, health/naturalness attributes, reward-control attributes, nutritional composition, and visual features. EEG data underwent predefined quality-control procedures, after which N2, P3, centroparietal positivity, feedback-related negativity, feedback P3, and frontal theta indices were extracted. Food-choice behavior was decomposed using a hierarchical drift-diffusion model into drift rate, boundary separation, starting point, and non-decision time. A multimodal RT-by-frontal-theta interaction model was then fitted, and GIMME dynamic network modelling was used to characterize shared, subgroup-specific, and individual pathways within affect-reward-control states.

RESULTS: Food stimuli were decomposed into 39 auditable variables, forming a continuous representational space spanning health, reward, control, nutritional, and visual-salience dimensions, with high reliability across domains. EEG quality control preserved clinically expected heterogeneity while excluding recordings at high risk of biasing ERP and time-frequency estimates. Behavioral and DDM results showed that the weight-regain group had longer mean reaction times than the maintained-weight-loss group (0.726 s vs. 0.651 s), together with lower drift rate (0.93 vs. 1.27), lower boundary separation (1.20 vs. 1.37), lower starting point (0.42 vs. 0.51), and longer non-decision time (0.31 s vs. 0.27 s). ERP findings indicated that weight-regain-related differences spanned early control/conflict monitoring, posterior stimulus evaluation, evidence accumulation, and feedback processing, with posterior P3/CPP and feedback-related components contributing the dominant late-stage separation. The multimodal interaction model showed an RT-by-frontal-theta interaction of approximately 0.92% points in the primary adjusted model (95% CI, 0.36-1.49), with a consistent direction across alternative model specifications. GIMME identified stable affect-reward-control dynamic pathways and separated three psychological dynamic subtypes: affect-reactive, reward-sensitized, and control-resilient.

CONCLUSION: Weight regain after metabolic bariatric surgery is not merely an endpoint of body-weight change, but a dynamic risk process shaped jointly by food-cue evaluation, evidence-accumulation efficiency, frontal control modulation, and longitudinal affect-reward state transitions. This multimodal computational framework reframes postoperative weight regain as a mechanistically interpretable neurobehavioral phenotype, providing a translational basis for earlier risk stratification, individualized follow-up, and interventions targeting emotion, craving, and control.

PMID:42426551 | DOI:10.1007/s11695-026-08834-8