Namgung Jong Young, Noh Eunchan, Jang Yurim, Lee Mi Ji, Park Bo-Yong
Department of Data Science, Inha University, Incheon, Republic of Korea.
College of Medicine, Inha University, Incheon, Republic of Korea.
J Headache Pain. 2025 Jan 9;26(1):5. doi: 10.1186/s10194-024-01946-5.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians.
症状的个体间差异以及脑病理生理学的动态性质给构建一个强大的偏头痛诊断模型带来了重大挑战。在本研究中，我们旨在整合提供结构和功能信息的不同类型的磁共振成像（MRI），并通过测试超参数的多种组合来开发一个强大的机器学习模型，以将偏头痛患者与健康对照进行分类，确保在不同偏头痛阶段和纵向重复数据中的稳定性。具体而言，我们构建了一个诊断模型，将发作性偏头痛患者与健康对照进行分类，并在发作期和发作间期以及纵向环境中验证其性能。我们从50例发作性偏头痛患者和50例年龄及性别匹配的健康对照中获取了T1加权和静息态功能MRI数据，并在一年后收集了随访数据。利用了形态学特征，包括皮质厚度、曲率和脑沟深度，以及功能连接特征，如功能连接的低维表示（梯度）、度中心性和介数中心性。我们采用基于正则化的特征选择方法结合随机森林分类器来构建诊断模型。通过在严格的交叉验证框架内测试具有不同特征组合、惩罚项和空间粒度的模型，我们发现曲率、脑沟深度、皮质厚度和功能梯度的组合实现了强大的分类性能。使用测试数据集评估模型性能，在区分偏头痛患者与健康对照时，准确率达到87%，曲线下面积（AUC）为0.94，在发作间期和发作期/发作周围期分别为85%、0.97和84%、0.93。当使用模型训练期间未包含的随访数据进行验证时，该模型在总阶段、发作间期和发作期/发作周围期的准确率分别达到91%、94%、89%，AUC分别为0.96、0.94、0.98，证实了其稳健性。特征重要性和临床关联分析表明，躯体运动、边缘和默认模式区域可能是偏头痛的可靠标志物。我们的研究结果表明了使用多模态MRI特征和机器学习框架具有强大的诊断性能，可能为不同队列的临床诊断提供一种有价值的方法，并有助于减轻临床医生的决策负担。
