Aberdeen Biomedical Imaging Centre (ABIC), Lilian Sutton Building, Foresterhill, University of Aberdeen, Aberdeen, United Kingdom.
Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom.
PLoS One. 2022 Jun 27;17(6):e0269952. doi: 10.1371/journal.pone.0269952. eCollection 2022.
Fatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline.
Fifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel.
The highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus.
The results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.
疲劳是类风湿关节炎（RA）的一种常见且负担沉重的症状，但人们对此知之甚少。目前，临床医生仅依靠疲劳问卷，这是一种固有主观的测量方法。为了有效开发未来的疗法和分层，确定疲劳的生物标志物至关重要。在这项研究中，我们根据 Chalder 疲劳量表变化（ΔCFS≥2），确定了 RA 患者中疲劳水平改善和未改善的患者之间的大脑差异，并比较了不同分类器的性能，以区分基线时的这些样本。
54 名疲劳的 RA 患者在基线和 6 个月后进行了磁共振（MR）扫描。在 6 个月时，我们确定了那些疲劳水平改善和那些没有改善的患者。评估了三个数据集（包括临床、结构磁共振成像（sMRI）和弥散张量成像（DTI）数据）中的 900 多个大脑特征作为疲劳改善的潜在预测因子。使用遗传算法进行特征选择。使用三种分类器对疲劳改善者和非改善者进行区分：最小二乘线性判别（LSLD）、线性支持向量机（SVM）和具有径向基函数核的 SVM。
sMRI 组的准确率最高（67.9%），其次是 DTI 组（63.8%），而使用临床特征的分类性能处于随机水平。在特征选择步骤中，左侧颞上回的平均曲率被选择的最为强烈，其次是右侧额极的表面积和左侧颞上回的外侧的表面积。
研究结果表明，大脑指标在预测疲劳变化方面优于临床指标。进一步探索这些方法可能有助于临床医生将患者分诊为最适合的疲劳缓解治疗。
