Herszage Jasmine, Dayan Eran, Sharon Haggai, Censor Nitzan
School of Psychological Sciences - Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Department of Radiology and Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Front Neurosci. 2020 Feb 5;14:76. doi: 10.3389/fnins.2020.00076. eCollection 2020.
Motor performance varies substantially between individuals. This variance is rooted in individuals' innate motor abilities, and should thus have a neural signature underlying these differences in behavior. Could these individual differences be detectable with neural measurements acquired at rest? Here, we tested the hypothesis that motor performance can be predicted by resting motor-system functional connectivity and motor-evoked-potentials (MEPs) induced by non-invasive brain stimulation. Twenty healthy right handed subjects performed structural and resting-state fMRI scans. On a separate day, MEPs were measured using transcranial magnetic stimulation (TMS) over the contrateral primary motor cortex (M1). At the end of the session, participants performed a finger-tapping task using their left non-dominant hand. Resting-state functional connectivity between the contralateral M1 and the supplementary motor area (SMA) predicted motor task performance, indicating that individuals with stronger resting M1-SMA functional connectivity exhibit better motor performance. This prediction was neither improved nor reduced by the addition of corticospinal excitability to the model. These results confirm that motor behavior can be predicted from neural measurements acquired prior to task performance, primarily relying on resting functional connectivity rather than corticospinal excitability. The ability to predict motor performance from resting neural markers, provides an opportunity to identify the extent of successful rehabilitation following neurological damage.
个体之间的运动表现差异很大。这种差异源于个体的先天运动能力，因此在行为差异背后应该有一个神经特征。能否通过静息状态下获取的神经测量来检测这些个体差异呢？在这里，我们测试了这样一个假设，即运动表现可以通过静息运动系统功能连接以及非侵入性脑刺激诱发的运动诱发电位（MEP）来预测。20名健康的右利手受试者进行了结构和静息态功能磁共振成像扫描。在另一天，通过对侧初级运动皮层（M1）进行经颅磁刺激（TMS）来测量MEP。在实验结束时，参与者用其非优势左手进行了手指敲击任务。对侧M1与辅助运动区（SMA）之间的静息态功能连接预测了运动任务表现，这表明静息M1-SMA功能连接较强的个体表现出更好的运动表现。在模型中加入皮质脊髓兴奋性后，这一预测既没有得到改善也没有降低。这些结果证实，运动行为可以从任务执行前获取的神经测量中预测出来，主要依赖于静息功能连接而非皮质脊髓兴奋性。从静息神经标志物预测运动表现的能力，为确定神经损伤后成功康复的程度提供了一个机会。
