Chronic Fatigue Syndrome (CFS) is a debilitating condition estimated to impact at least 1 million individuals in the United States, however there persists controversy about its existence. Machine learning algorithms have become a powerful methodology for evaluating multi-regional areas of fMRI activation that can classify disease phenotype from sedentary control. Uncovering objective biomarkers such as an fMRI pattern is important for lending credibility to diagnosis of CFS. fMRI scans were evaluated for 69 patients (38 CFS and 31 Control) taken before (Day 1) and after (Day 2) a submaximal exercise test while undergoing the n-back memory paradigm. A predictive model was created by grouping fMRI voxels into the Automated Anatomical Labeling (AAL) atlas, splitting the data into a training and testing dataset, and feeding these inputs into a logistic regression to evaluate differences between CFS and control. Model results were cross-validated 10 times to ensure accuracy. Model results were able to differentiate CFS from sedentary controls at a 80% accuracy on Day 1 and 76% accuracy on Day 2 (Table 3). Recursive features selection identified 29 ROI's that significantly distinguished CFS from control on Day 1 and 28 ROI's on Day 2 with 10 regions of overlap shared with Day 1 (Figure 3). These 10 shared regions included the putamen, inferior frontal gyrus, orbital (F3O), supramarginal gyrus (SMG), temporal pole; superior temporal gyrus (T1P) and caudate ROIs. This study was able to uncover a pattern of activated neurological regions that differentiated CFS from Control. This pattern provides a first step toward developing fMRI as a diagnostic biomarker and suggests this methodology could be emulated for other disorders. We concluded that a logistic regression model performed on fMRI data significantly differentiated CFS from Control.

中文翻译：

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一种从久坐控制中区分慢性疲劳综合征 (CFS) 的功能磁共振成像数据的机器学习方法

慢性疲劳综合症 (CFS) 是一种使人衰弱的疾病，据估计会影响美国至少 100 万人，但对其存在仍存在争议。机器学习算法已成为评估 fMRI 激活的多区域区域的强大方法，可以从久坐控制中对疾病表型进行分类。发现客观生物标志物（如 fMRI 模式）对于提高 CFS 诊断的可信度非常重要。对 69 名患者（38 名 CFS 和 31 名对照组）在接受 n-back 记忆范式的次极量运动测试之前（第 1 天）和之后（第 2 天）进行 fMRI 扫描评估。通过将 fMRI 体素分组到自动解剖标记 (AAL) 图集，将数据拆分为训练和测试数据集，创建了预测模型，并将这些输入输入逻辑回归以评估 CFS 和对照之间的差异。模型结果经过 10 次交叉验证以确保准确性。模型结果能够在第 1 天以 80% 的准确度和第 2 天的 76% 准确度将 CFS 与久坐控制区分开（表 3）。递归特征选择确定了 29 个 ROI，它们在第 1 天和第 2 天的 28 个 ROI 显着区分了 CFS 与第 1 天共享的重叠区域（图 3）。这10个共享区域包括壳核、额下回、眼眶（F3O）、缘上回（SMG）、颞极；颞上回 (T1P) 和尾状 ROI。这项研究能够揭示一种将 CFS 与对照区分开来的激活神经区域的模式。这种模式为开发 fMRI 作为诊断生物标志物迈出了第一步，并表明这种方法可以用于其他疾病。我们得出结论，对 fMRI 数据执行的逻辑回归模型显着区分了 CFS 与对照。