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Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

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M2M-InvNet:使用TMS和生成的3D卷积网络从多肌反应绘制人类运动皮质.

Md Navid Akbar, Mathew Yarossi, Sumientra Rampersad

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

|March 18, 2024

概括

这项研究使用了一种新的深度学习模型,M2M-InvNet,以使用运动唤起潜能 (MEP) 识别运动皮质上的特定大脑刺激位置. 这促进了针对性大脑刺激,以精确激活肌肉.

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Last Updated: Jun 30, 2025

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科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
机器学习机器学习

背景情况:

跨磁刺激 (TMS) 针对运动皮质,以引起肌肉中的运动唤起潜能 (MEP).
TMS应用的确切位置会影响产生的MEP,这表明刺激部位和神经激活之间存在因果关系.

研究的目的:

调查是否可以使用运动唤起潜力 (MEP) 来推断运动皮层的刺激区域.
开发和评估这种反向成像任务的深度学习模型,旨在指导TMS线圈放置所需的肌肉反应.

主要方法:

利用先前开发的3D卷积神经网络 (CNN) 来预测来自电场的MEP.
开发并评估了五种不同的反向成像CNN架构,包括常规和生成模型.
使用多个重建准确度指标评估模型性能.

主要成果:

一个拟议的架构,M2M-InvNet,在从MEP数据中估计刺激的皮质区域方面表现出卓越的性能.
该研究成功地解决了反向成像问题,将欧洲议会议员映射回他们的皮质起源.

结论:

根据MEP,M2M-InvNet架构对准确识别基于TMS刺激的运动皮质区域具有显著的前景.
这种方法可以实现更精确的TMS线圈定位,以实现特定的肌肉激活模式.