Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders. Methods: We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an “ensemble” series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected. Results: A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Naïve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum. Conclusion: We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.

中文翻译：

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机器学习检测慢性疲劳综合征（CFS）和海湾战争疾病（GWI）之间的功能磁共振成像（fMRI）数据差异的模式。

背景：海湾战争疾病（GWI）和慢性疲劳综合症（CFS）是两种使人衰弱的疾病，它们在运动后具有类似的慢性疼痛，疲劳和劳累力竭症状。许多医生继续相信这两者都是心身疾病，迄今为止尚未发现潜在的病因。因此，发现客观的生物标志物对于提高诊断标准的可信度并帮助区分这两种疾病很重要。方法：我们通过比较运动前后两背工作记忆任务期间相应的功能磁共振成像扫描，以分别模拟正常活动期间和劳累性疲劳后的大脑激活情况，从而评估了80名GWI患者和38名CFS患者的认知差异。根据总活动量将体素分组到“自动解剖标记（AAL）”图集中，并在“整体”系列机器学习算法中使用该体素，以评估在fMRI扫描中是否可以检测到差异的多区域模式。结果：K最近邻（70％/ 81％），线性支持向量机（SVM）（70％/ 77％），决策树（82％/ 82％），随机森林（77％/ 78％）， AdaBoost（69％/ 81％），朴素贝叶斯（74％/ 78％），二次判别分析（QDA）（73％/ 75％），Logistic回归模型（82％/ 82％）和神经网络（76％） / 77％）能够将运动前后CFS与GWI进行区分，所有模型在运动前和运动后的平均预测准确率达到75％。基于递归特征消除（RFE）和随机森林重要性的迭代特征选择和移除过程选择了运动前30个区域和运动后33个区域，这些区域在所有模型中均将CFS与GWI进行了区分，并在运动前产生了82％的最终最佳精度。单个模型通过Logistic回归或决策树进行运动后为82％，而任何六个或更多模型选择运动后为100％。这两天的差异性激活包括右前岛，左壳核和双侧眶额，腹侧前额叶皮层，上，下和前丘脑（内侧）顶叶以及颞外侧区域。第2天有小脑，左辅助运动区和中枢回前后双侧。几天之间的变化包括在第2天，右Rolandic oper向左切换，双侧中耳回向左前扣带。结论：我们得出结论，基于整体机器学习模型，使用fMRI活动模式可以显着区分CFS和GWI。