Sevel Landrew S, Boissoneault Jeff, Letzen Janelle E, Robinson Michael E, Staud Roland
Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA.
Department of Medicine, College of Medicine, University of Florida, PO Box 100277, Gainesville, 2610-0277, FL, USA.
Exp Brain Res. 2018 Aug;236(8):2245-2253. doi: 10.1007/s00221-018-5301-8. Epub 2018 May 30.
Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.
慢性疲劳综合征（CFS）是一种与疲劳、疼痛以及磁共振脑成像（MRI）中出现的结构/功能异常相关的疾病。因此，我们评估了结构MRI（sMRI）异常在区分CFS患者与健康对照中的表现，并将其与基于自我报告（SR）的机器学习（ML）分类进行比较。参与者包括18名CFS患者和1名健康对照（HC）。所有受试者均接受了T1加权sMRI检查，并提供了关于疲劳、疼痛强度、焦虑、抑郁、愤怒和睡眠质量的视觉模拟量表评分。使用FreeSurfer对sMRI数据进行分割，并根据先前报道的CFS患者的功能和结构异常确定了61个区域。在RapidMiner中使用线性支持向量机和自助法乐观校正进行分类。我们基于（1）61个先验sMRI区域估计值和（2）SR评分比较了ML分类器。sMRI模型的分类准确率达到79.58%。SR（准确率=95.95%）的表现优于两种sMRI模型。来自与认知、情感和记忆相关的多个脑区的估计对组分类有很大贡献。这是首次基于ML的CFS组分类。我们的研究结果表明，sMRI异常有助于区分CFS患者和HC，但SR评分在分类任务中仍然最有效。
