Journal Article

Brain predictors of fatigue in rheumatoid arthritis: A machine learning study

Goñi, María, Murray, Alison D., Basu, Neil, Waiter, Gordon D.

2022

Overview

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 ([DELTA]CFS[greater than or equal to] 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Analysis / Arthritis / Biology and Life Sciences / Biomarkers / Brain / Classifiers / Complications and side effects / Computer and Information Sciences / Datasets / Decision making / Eigenvalues / Fatigue / Feature selection / Genetic algorithms / Kernel functions / Machine learning / Magnetic resonance / Magnetic resonance imaging / Mathematical analysis / Medical imaging / Medicine and Health Sciences / Neuroimaging / Patients / Physical Sciences / Psychological aspects / Questionnaires / Radial basis function / Research and Analysis Methods / Rheumatoid arthritis / Risk factors / Superior temporal sulcus / Support vector machines / Tensors