Multimodal Neurobiological Data for Binge-Type Eating Disorders, 110 Participants

Data Sheet 3 presents multimodal data from a study of 110 participants with bulimia nervosa, binge eating disorder, and matched controls. 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 figshare in April 2026.

Use Cases

• Classifying binge-type eating disorder diagnoses based on functional brain connectivity patterns.
• Predicting individual symptom expression variation using multimodal neurobiological features.
• Distinguishing bulimia nervosa from binge eating disorder using task-based fMRI with disorder-specific stimuli.
• Investigating shared versus disorder-specific neurobiological mechanisms across brain, behavior, and physiology.

Strengths

• Integrates five distinct data modalities: task-based fMRI, intrinsic connectivity, voxel-based morphometry, neuropsychological assessments, and blood biomarkers.
• Includes 110 participants across three groups (BN, BED, and matched controls).
• Reported classification accuracies, such as a mean balanced accuracy of 68.7% for diagnostic classification using functional connectivity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a 3.9 MB PDF, which may not contain the raw, structured data in a machine-readable format.

Provenance

Source

Lena Rommerskirchen via figshare.

Collection Method

Data collected from a study applying a multimodal machine learning framework to 110 participants.

Time Range

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Freshness

Last updated 2026-04-13 05:49:01; freshness should be verified.

Geography

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