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.