Gulf War Illness (GWI) is a debilitating condition characterized by dysfunction of cognition, pain, fatigue, sleep, and diverse somatic symptoms with no known underlying pathology. As such, uncovering objective biomarkers such as differential regions of activity within a Functional Magnetic Resonance Imaging (fMRI) scan is important to enhance validity of the criteria for diagnosis. Symptoms are exacerbated by mild activity, and exertional exhaustion is a key complaint amongst sufferers. We modeled this exertional exhaustion by having GWI (n = 80) and sedentary control (n = 31) subjects perform submaximal exercise stress tests on two consecutive days. Cognitive differences were assessed by comparing fMRI scans performed during 2-Back working memory tasks before and after the exercise. Machine learning algorithms were used to identify differences in brain activation patterns between the two groups on Day 1 (before exercise) and Day 2 (after exercise). The numbers of voxels with t > 3.17 (corresponding to p < 0.001 uncorrected) were determined for brain regions defined by the Automated Anatomical Labeling (AAL) atlas. Data were divided 70:30 into training and test sets. Recursive feature selection identified twenty-nine regions of interest (ROIs) that significantly distinguished GWI from control on Day 1 and 28 ROIs on Day 2. Ten regions were present in both models between the two days, including right anterior insula, orbital frontal cortex, thalamus, bilateral temporal poles, and left supramarginal gyrus and cerebellar Crus 1. The models had 70% accuracy before exercise on Day 1 and 85% accuracy after exercise on Day 2, indicating the logistic regression model significantly differentiated subjects with GWI from the sedentary control group. Exercise caused changes in these patterns that may indicate the cognitive differences caused by exertional exhaustion. A second set of predictive models was able to classify previously identified GWI exercise subgroups START, STOPP, and POTS for both Days 1 and Days 2 with 67% and 69% accuracy respectively. This study was the first of its kind to differentiate GWI and the three sub-phenotypes START, STOPP, and POTS from a sedentary control using a logistic regression estimation method.

中文翻译：

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Logistic回归算法可区分久坐控件的海湾战争疾病（GWI）功能磁共振成像（fMRI）数据。

海湾战争疾病（GWI）是一种令人衰弱的疾病，其特征在于认知功能障碍，疼痛，疲劳，睡眠和各种躯体症状，尚无已知的病理基础。因此，在功能磁共振成像（fMRI）扫描中发现客观的生物标志物（例如，活动的不同区域）对于提高诊断标准的有效性至关重要。轻度活动会加剧症状，而劳累性疲惫是患者的主要抱怨。我们通过GWI（n = 80）和久坐控制（n= 31）受试者连续两天进行次最大运动压力测试。通过比较运动前后2背工作记忆任务期间执行的功能磁共振成像扫描来评估认知差异。使用机器学习算法来识别两组在第1天（运动前）和第2天（运动后）之间的大脑激活模式的差异。t > 3.17的体素数（对应于p对于由自动解剖标记（AAL）地图集定义的大脑区域，确定了<0.001（未校正）。数据按70:30分为训练集和测试集。递归特征选择确定了19个感兴趣的区域（ROI），它们在第1天显着地将GWI与对照区分开，并在第2天将其与28个ROI区分开来。两天的两个模型中都存在10个区域，包括右前岛突，眶额叶皮质，丘脑，双侧颞极以及左上指回和小脑Crus。该模型在运动前第1天的准确性为70％，在运动后第2天的准确性为85％，表明Logistic回归模型将GWI与久坐控制区分开来组。锻炼引起这些模式的改变，这可能表明劳累性筋疲力尽引起的认知差异。第二组预测模型能够对第1天和第2天的先前确定的GWI运动亚组START，STOPP和POTS进行分类，其准确度分别为67％和69％。这项研究是首次使用久后对照通过Logistic回归估算方法区分GWI和三个亚型START，STOPP和POTS。