Multimodal Neurobiological Data for Binge-Type Eating Disorder Classification

110 participants with bulimia nervosa, binge eating disorder, and matched controls were studied using a multimodal machine learning framework. The dataset integrates task-based fMRI, intrinsic connectivity, voxel-based morphometry, neuropsychological assessments, and peripheral blood biomarkers. It was authored by Lena Rommerskirchen and last updated on 2026-04 13.

Use Cases

• Training diagnostic classifiers based on functional brain connectivity patterns.
• Predicting individual symptom variation using multimodal neurobiological data.
• Distinguishing between bulimia nervosa and binge eating disorder using task-based fMRI with disorder-specific stimuli.
• Comparing the predictive utility of unimodal versus multimodal machine learning approaches for psychiatric classification.

Strengths

• Integrates five distinct data modalities: fMRI, connectivity, morphometry, neuropsychology, and blood biomarkers.
• Includes 110 participants across three diagnostic groups (BN, BED, and controls).
• Reports specific model performance metrics, such as a mean balanced classification accuracy of 68.7% for functional connectivity.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 7.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Data collected from 110 participants using neuroimaging, behavioral, and physiological assessments.

Time Range

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Freshness

Last updated 2026-04-13 05:49:02; freshness should be verified.

Geography

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