Biomarkers of Migraine and Cluster Headache: Differences and Similarities

Objective: This study was undertaken to identify magnetic resonance imaging (MRI) biomarkers that differentiate migraine from cluster headache patients and imaging features that are shared. Methods: Clinical, functional, and structural MRI data were obtained from 20 migraineurs, 20 cluster headache patients, and 15 healthy controls. Support vector machine algorithms and a stepwise removal process were used to discriminate headache patients from controls, and subgroups of patients. Regional between-group differences and association between imaging features and patients' clinical characteristics were also investigated. Results: The accuracy for classifying headache patients from controls was 80%. The classification accuracy for discrimination between migraine and controls was 89%, and for cluster headache and controls it was 98%. For distinguishing cluster headache from migraine patients, the MRI classifier yielded an accuracy of 78%, whereas MRI-clinical combined classification model achieved an accuracy of 99%. Bilateral hypothalamic and periaqueductal gray (PAG) functional networks were the most important MRI features in classifying migraine and cluster headache patients from controls. The left thalamic network was the most discriminative MRI feature in classifying migraine from cluster headache patients. Compared to migraine, cluster headache patients showed decreased functional interaction between the left thalamus and cortical areas mediating interoception and sensory integration. The presence of restlessness was the most important clinical feature in discriminating the two groups of patients. Interpretation: Functional biomarkers, including the hypothalamic and PAG networks, are shared by migraine and cluster headache patients. The thalamocortical pathway may be the neural substrate that differentiates migraine from cluster headache attacks with their distinct clinical features.

Automated summary

The objective of this study was to identify MRI biomarkers that distinguish between migraine and cluster headache patients, as well as investigate shared imaging features. Clinical, functional, and structural MRI data were collected from 20 migraineurs, 20 cluster headache patients, and 15 healthy controls. Support vector machine algorithms were used to classify headache patients from controls, and regional differences and associations with clinical characteristics were examined. The results showed that MRI could accurately classify headache patients from controls, with accuracies of 80% for all headache patients, 89% for migraine, and 98% for cluster headache. The bilateral hypothalamic and periaqueductal gray functional networks were found to be important in classifying both migraine and cluster headache patients. The presence of restlessness was the most important clinical feature in distinguishing between the two groups.