Multimodal machine learning reveals neurobiological signatures of binge-type eating disorders.

INTRODUCTION

Binge-type eating disorders, including bulimia nervosa (BN) and binge eating disorder (BED), are associated with both shared and disorder-specific neurobiological mechanisms across brain, behavior, and physiology. A clearer distinction between shared mechanisms and disorder-specific alterations may advance our understanding of binge-type eating pathology.

METHODS

We applied a comprehensive multimodal machine learning framework to 110 participants (BN, BED, and age & weight matched controls), integrating task-based fMRI, intrinsic connectivity, voxel-based morphometry, neuropsychological assessments, and peripheral blood biomarkers. Both unimodal and multimodal machine learning models were trained to classify groups and to predict individual variation in symptom expression.

RESULTS

Functional brain connectivity achieved the highest accuracy for diagnostic classification and symptom prediction (with a mean balanced classification accuracy (bACC) of 68.7%), whereas task-based fMRI with disorder-specific food stimuli and peripheral blood biomarkers best distinguished BN from BED (mean bACC of 87%). Multimodal models did not generally outperform the best unimodal approaches, except from modest gains in a limited set of regression targets.

CONCLUSIONS

These findings suggest that functional brain connectivity carries robust predictive information for transdiagnostic classification, whereas task-evoked activation patterns and peripheral biomarkers show stronger predictive utility for distinguishing BN from BED. Whether these modality-specific patterns reflect underlying neurobiological mechanisms remains to be established in future hypothesis-driven work. Identifying which modalities best represent shared vulnerability vs. symptom-type-dependent variation may help to provide a foundation for a more mechanistic understanding of these disorders.