LAUSR

INTRODUCTION Functional movement disorder (FMD) is a type of functional neurological disorder (FND) characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier via support vector machine (SVM) to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting state functional MRI (rs-fMRI). METHODS First, we selected 66 seed regions based on prior related studies, then calculated the full correlation matrix between them, before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. RESULTS We identified 29 features of interest that were highly predictive of FMD condition, classifying patients from controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, the left dorsolateral prefrontal cortex (dlPFC), the left cerebellum and the left posterior insula. CONCLUSION The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive, abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients.