Messina Roberta, Sudre Carole H, Wei Diana Y, Filippi Massimo, Ourselin Sebastien, Goadsby Peter J
Division of Neuroscience, Neuroimaging Research Unit, Institute of Experimental Neurology, Milan, Italy.
Neurology Unit, San Raffaele Scientific Institute, Milan, Italy.
Ann Neurol. 2023 Apr;93(4):729-742. doi: 10.1002/ana.26583. Epub 2023 Jan 4.
This study was undertaken to identify magnetic resonance imaging (MRI) biomarkers that differentiate migraine from cluster headache patients and imaging features that are shared.
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.
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.
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. ANN NEUROL 2023;93:729-742.
本研究旨在识别可区分偏头痛患者与丛集性头痛患者的磁共振成像（MRI）生物标志物以及两者共有的影像学特征。
获取了20名偏头痛患者、20名丛集性头痛患者和15名健康对照者的临床、功能和结构MRI数据。使用支持向量机算法和逐步剔除过程来区分头痛患者与对照者以及患者亚组。还研究了组间区域差异以及影像学特征与患者临床特征之间的关联。
将头痛患者与对照者进行分类的准确率为80%。区分偏头痛与对照者的分类准确率为89%，区分丛集性头痛与对照者的准确率为98%。对于区分丛集性头痛与偏头痛患者，MRI分类器的准确率为78%，而MRI - 临床联合分类模型的准确率达到99%。双侧下丘脑和导水管周围灰质（PAG）功能网络是区分偏头痛和丛集性头痛患者与对照者的最重要MRI特征。左丘脑网络是区分偏头痛与丛集性头痛患者的最具鉴别力的MRI特征。与偏头痛相比，丛集性头痛患者左侧丘脑与介导内感受和感觉整合的皮质区域之间的功能交互减少。烦躁不安的存在是区分两组患者的最重要临床特征。
偏头痛和丛集性头痛患者共有包括下丘脑和PAG网络在内的功能生物标志物。丘脑皮质通路可能是区分具有不同临床特征的偏头痛和丛集性头痛发作的神经基础。《神经病学年鉴》2023年；93:729 - 742。
