Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not recognize 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 classify 61 FMD patients from 59 healthy controls using features derived from resting state functional MRI (rs-fMRI). 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. We identified 29 features of interest that were highly predictive of FMD condition, classifying patients from controls with 80% accuracy. The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward and sensorimotor integration, potentially mediating relationships 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.

中文翻译：

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功能性运动障碍的静息态 fMRI 分类

功能性运动障碍 (FMD) 是一种功能性神经障碍，其特征是患者无法识别为自身产生的异常运动。先前的成像研究显示了一种复杂的活动模式，将大脑中涉及情绪反应、运动控制和代理的区域联系起来。本研究旨在通过支持向量机 (SVM) 构建分类器来更好地表征这些关系，以使用静息状态功能 MRI (rs-fMRI) 衍生的特征对来自 59 名健康对照的 61 名 FMD 患者进行准确分类。首先，我们根据先前的相关研究选择了 66 个种子区域，然后计算它们之间的完整相关矩阵，然后执行递归特征消除以将特征集筛选为最具预测性的特征并构建分类器。我们确定了 29 个对 FMD 状况具有高度预测性的感兴趣特征，以 80% 的准确率将患者与对照组进行分类。模型选择的特征突出了与情绪、奖励和感觉运动整合相关的区域之间相互关联关系的重要性，潜在地调解了与运动功能、注意力和执行功能相关的区域之间的关系。探索性机器学习能够识别这种独特的异常模式，表明这些区域之间功能联系的改变可能是许多 FMD 患者病情的一致特征。模型选择的特征突出了与情绪、奖励和感觉运动整合相关的区域之间相互关联关系的重要性，潜在地调解了与运动功能、注意力和执行功能相关的区域之间的关系。探索性机器学习能够识别这种独特的异常模式，表明这些区域之间功能联系的改变可能是许多 FMD 患者病情的一致特征。模型选择的特征突出了与情绪、奖励和感觉运动整合相关的区域之间相互关联关系的重要性，潜在地调解了与运动功能、注意力和执行功能相关的区域之间的关系。探索性机器学习能够识别这种独特的异常模式，表明这些区域之间功能联系的改变可能是许多 FMD 患者病情的一致特征。