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Updated: May 30, 2025

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

为精确的大脑刺激预测大脑状态:当前的方法和未来的前景.

Matteo De Matola¹, Carlo Miniussi¹

¹Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068 Rovereto (TN), Italy.

|January 27, 2025

概括

个性化横磁刺激 (TMS) 需要实时预测大脑状态,以克服延迟. 这种方法通过准预测的大脑活动,提高了TMS在神经和精神病治疗中的可靠性.

科学领域:

神经科学是一个神经科学.
计算神经科学是一种神经科学.
生物医学工程生物医学工程

背景情况:

跨磁刺激 (TMS) 提供了理解大脑功能和治疗神经/精神疾病的潜力.
在TMS结果的低可重现性是一个重大挑战,通常归因于个体大脑的变化.
使用神经成像数据的个性化刺激协议可以改善准,但面临理论和技术障碍.

研究的目的:

为了应对在线功能向在实时大脑状态依赖的TMS中的挑战.
调查电脑电图 (EEG) 触发的TMS中补偿硬件/软件延迟的方法.
探索预测大脑状态的潜力,以准确,实时的TMS传递.

主要方法:

审查大脑状态依赖刺激的最先进技术.
讨论适用于EEG时间序列分析的两类预测方法.
在TMS的背景下检查数据驱动预测的证据.

主要成果:

实时EEG信号处理和预测是必要的,以弥补在线TMS的系统延迟.
预测大脑状态允许TMS设备针对预测的,而不是测量的大脑活动.
这种方法在初步研究中取得了成功,为个性化大脑刺激铺平了道路.

关键词:

大脑状态大脑状态深度学习是一种深度学习.这是一个EEGEEGEEGEEGEEGEEGEEG.预测预测预测预测神经网络的神经网络的神经网络国家依赖的刺激.在TMSMS中使用.

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结论:

预测方法对于克服当前实时,大脑状态依赖的TMS的局限性至关重要.
数据驱动的预测对推进TMS方法和理解大脑动态有很大的潜力.
使用大型开放数据集可以进一步改变个性化的脑刺激治疗.