Sarkar Arkaprovo, Dipani Alish, Leodori Giorgio, Popa Traian, Kassavetis Panagiotis, Hallett Mark, Thirugnanasambandam Nivethida
Human Motor Neurophysiology and Neuromodulation Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India.
National Brain Research Centre (NBRC), Manesar 122052, India.
Brain Sci. 2022 Oct 17;12(10):1401. doi: 10.3390/brainsci12101401.
Variability in the response of individuals to various non-invasive brain stimulation protocols is a major problem that limits their potential for clinical applications. Baseline motor-evoked potential (MEP) amplitude is the key predictor of an individual's response to transcranial magnetic stimulation protocols. However, the factors that predict MEP amplitude and its variability remain unclear. In this study, we aimed to identify the input-output curve (IOC) parameters that best predict MEP amplitude and its variability. We analysed IOC data from 75 subjects and built a general linear model (GLM) using the IOC parameters as regressors and MEP amplitude at 120% resting motor threshold (RMT) as the response variable. We bootstrapped the data to estimate variability of IOC parameters and included them in a GLM to identify the significant predictors of MEP amplitude variability. Peak slope, motor threshold, and maximum MEP amplitude of the IOC were significant predictors of MEP amplitude at 120% RMT and its variability was primarily driven by the variability of peak slope and maximum MEP amplitude. Recruitment gain and maximum corticospinal excitability are the key predictors of MEP amplitude and its variability. Inter-individual variability in motor output may be reduced by achieving a uniform IOC slope.
个体对各种非侵入性脑刺激方案的反应存在变异性,这是一个主要问题,限制了它们在临床应用中的潜力。基线运动诱发电位(MEP)幅度是个体对经颅磁刺激方案反应的关键预测指标。然而,预测MEP幅度及其变异性的因素仍不清楚。在本研究中,我们旨在确定最能预测MEP幅度及其变异性的输入-输出曲线(IOC)参数。我们分析了75名受试者的IOC数据,并使用IOC参数作为回归变量,以120%静息运动阈值(RMT)时的MEP幅度作为响应变量,建立了一个通用线性模型(GLM)。我们对数据进行了自助抽样,以估计IOC参数的变异性,并将其纳入GLM中,以确定MEP幅度变异性的显著预测指标。IOC的峰值斜率、运动阈值和最大MEP幅度是120%RMT时MEP幅度的显著预测指标,其变异性主要由峰值斜率和最大MEP幅度的变异性驱动。募集增益和最大皮质脊髓兴奋性是MEP幅度及其变异性的关键预测指标。通过实现均匀的IOC斜率,可减少个体间运动输出的变异性。