A foundation-model approach to pediatric headache classification from rs-fMRI

2026-08-07

AI Summary

This study addresses the challenge of objectively diagnosing pediatric headache and accurately differentiating its subtypes, which has long been hindered by the lack of reliable biomarkers. For the first time, the authors apply the neuroimaging foundation model NeuroSTORM to pediatric headache classification using resting-state functional MRI (rs-fMRI) data. By leveraging representation learning and fine-tuning, the model achieves effective few-shot transfer without relying on conventional functional connectivity features. In binary classification between headache patients and healthy controls, the model attains an AUROC of 0.82 and an AUPRC of 0.93. Furthermore, in distinguishing among three headache subtypes, it achieves a macro-AUROC of 0.69, significantly outperforming traditional approaches. These results demonstrate the promising potential of foundation models in diagnosing pediatric neurological disorders.

Abstract

Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tuned it to distinguish healthy controls from children with headache and subsequently classify headache subtypes. We compared NeuroSTORM with a standard neuroscience approach using functional-connectivity (FC) matrices derived from brain activity as predictors. Using 189 rs-fMRI scans from 110 individuals collected across two visits (prevalence of any headache: 74%), NeuroSTORM achieved an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% CI, 0.82-0.82) and an area under the precision-recall curve (AUPRC) of 0.93 (95% CI, 0.93-0.94) for discriminating headache from non-headache. In contrast, models trained on FC matrices showed lower performance (AUROC, 0.67 [95% CI, 0.67-0.67]; AUPRC, 0.85 [95% CI, 0.85-0.85]). In multiclass classification of healthy controls, chronic migraine, and non-chronic headaches (e.g., post-viral headache, new daily persistent headache, post-traumatic headache), NeuroSTORM achieved a macro-AUROC of 0.69 (95% CI, 0.68-0.69). Results suggest that the approach can distinguish chronic migraine but has difficulty differentiating other headache subtypes from chronic migraine. Overall, under limited-data conditions, NeuroSTORM appears to capture latent rs-fMRI representations that transfer to headache-related tasks without relying on FC features. These findings provide proof of concept for fMRI-based prediction of pediatric headache and highlight potential future utility for subtype identification and individualized treatment strategies.

Problem

• pediatric headache
• headache classification
• rs-fMRI
• headache subtypes
• neurological disorder

Innovation

• foundation model
• rs-fMRI
• pediatric headache classification
• NeuroSTORM
• functional connectivity