Khatri Uttara U, Pulliam Kristen, Manesiya Muskan, Cortez Melanie Vieyra, Millán José Del R, Hussain Sara J
Movement and Cognitive Rehabilitation Science Program, Department of Kinesiology and Health Education, The University of Texas at Austin, Austin, TX, USA.
Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA; Department of Neurology, The University of Texas at Austin, Austin, TX, USA; Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
Brain Stimul. 2025 Jan-Feb;18(1):64-76. doi: 10.1016/j.brs.2024.12.1193. Epub 2024 Dec 21.
Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are needed.
As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) activation (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single-pulse TMS during classifier-predicted personalized CST states.
MEP amplitudes elicited in real-time during classifier-predicted personalized strong CST states were significantly larger than those elicited during corresponding weak and random CST states. MEP amplitudes elicited in real-time during classifier-predicted personalized strong CST states were also significantly less variable than those elicited during corresponding weak CST states. Personalized CST states lasted for ∼1-2 s at a time and ∼1 s elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between classifier-predicted personalized strong and weak CST states.
Our results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.
经颅磁刺激(TMS)干预可有效治疗与中风相关的运动障碍,但其效果差异很大。脑状态依赖的TMS方法有望解决这一问题,但病变位置和振荡动力学的个体差异使得将其应用于中风后脑具有挑战性。需要专门设计以应对这些挑战的个性化脑状态依赖方法。
作为朝着这一目标迈出的第一步,我们测试了一种基于机器学习的新型脑电图 - TMS系统,该系统可实时识别反映健康成年人皮质脊髓束(CST)强弱激活(强弱CST状态)的个性化脑活动模式。参与者完成了一项单节段研究,包括获取TMS - EEG - EMG训练数据集、个性化分类器训练,以及在分类器预测的个性化CST状态期间进行实时脑电图引导的单脉冲TMS。
在分类器预测的个性化强CST状态期间实时诱发的运动诱发电位(MEP)幅度显著大于在相应的弱CST状态和随机CST状态期间诱发的幅度。在分类器预测的个性化强CST状态期间实时诱发的MEP幅度的变异性也显著小于在相应的弱CST状态期间诱发的变异性。个性化CST状态每次持续约1 - 2秒,连续相似状态之间间隔约1秒。个体参与者在分类器预测的个性化强CST状态和弱CST状态之间的频谱空间脑电图模式上表现出独特差异。
我们的结果首次表明,个性化的全脑脑电图活动模式可实时预测健康人类的CST激活。这些发现代表了朝着使用个性化脑状态依赖的TMS干预来促进中风后CST功能迈出的关键一步。