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首页> 外文会议>International Conference on Augmented Cognition >Machine Learning-Based Prediction of Changes in Behavioral Outcomes Using Functional Connectivity and Clinical Measures in Brain-Computer Interface Stroke Rehabilitation
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Machine Learning-Based Prediction of Changes in Behavioral Outcomes Using Functional Connectivity and Clinical Measures in Brain-Computer Interface Stroke Rehabilitation

机译：基于机器学习的行为结果的预测使用功能连通性和脑电电脑界面冲程康复中的临床措施

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摘要

The goal of this work is to evaluate if changes in brain connectivity can predict behavioral changes among subjects who have suffered stroke and have completed brain-computer interface (BCI) interventional therapy. A total of 23 stroke subjects, with persistent upper-extremity motor deficits, received the stroke rehabilitation therapy using a closed-loop neurofeedback BCI device. Over the course of the entire interventional therapy, resting-state fMRI were collected at two time points: prior to start and immediately upon completion of therapy. Behavioral assessments were administered at each time point via neuropsychological testing to collect measures on Action Research Arm Test, Nine-Hole Peg Test, Barthel Index and Stroke Impact Scale. Resting-state functional connectivity changes in the motor network were computed from pre- to post-interventional therapy and were combined with clinical data corresponding to each subject to estimate the change in behavioral performance between the two time-points using a machine learning based predictive model. Inter-hemispheric correlations emerged as stronger predictors of changes across multiple behavioral measures in comparison to intra-hemispheric links. Additionally, age predicted behavioral changes better than other clinical variables such as gender, pre-stroke handedness, etc. Machine learning model serves as a valuable tool in predicting BCI therapy-induced behavioral changes on the basis of functional connectivity and clinical data.
机译：这项工作的目的是评估如果脑连通性的变化可以预测谁遭受中风，并已完成了脑机接口（BCI）介入治疗的受试者中的行为变化。共计23组行程的受试者，持续性上肢运动缺陷，接收使用闭环神经反馈BCI装置中的中风康复治疗。立即治疗完成后启动事先：在整个介入治疗的过程中，静息态功能磁共振成像在两个时间点收集。行为评估是在通过神经心理测试，以行动研究的ARM测试，九洞棍测试，Barthel指数和卒中影响量表收集措施，每一个时间点给予。电动机网络中的静息态功能连接的变化是从之前到之后的介入治疗计算并合并与对应于每个受试者以估计在使用机器的两个时间点之间的行为表现的变化的临床数据基于学习的预测模型。跨半球的相关性成为相较于内部半球链接在多个行为的措施变化的强预测。此外，年龄预测行为的变化比其他临床变量，如性别较好，预行程霸道等机器学习模型可作为预测功能连通性和临床数据的基础上，BCI治疗引起的行为变化的重要工具。

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