Ganesh G, Burdet E, Haruno M, Kawato M
Department of Computational Neurobiology, ATR International, Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
Neuroimage. 2008 Oct 1;42(4):1463-72. doi: 10.1016/j.neuroimage.2008.06.018. Epub 2008 Jun 25.
In humans, it is generally not possible to use invasive techniques in order to identify brain activity corresponding to activity of individual muscles. Further, it is believed that the spatial resolution of non-invasive brain imaging modalities is not sufficient to isolate neural activity related to individual muscles. However, this study shows that it is possible to reconstruct muscle activity from functional magnetic resonance imaging (fMRI). We simultaneously recorded surface electromyography (EMG) from two antagonist muscles and motor cortices activity using fMRI, during an isometric task requiring both reciprocal activation and co-activation of the wrist muscles. Bayesian sparse regression was used to identify the parameters of a linear mapping from the fMRI activity in areas 4 (M1) and 6 (pre-motor, SMA) to EMG, and to reconstruct muscle activity in an independent test data set. The mapping obtained by the sparse regression algorithm showed significantly better generalization than those obtained from algorithms commonly used in decoding, i.e., support vector machine and least square regression. The two voxel sets corresponding to the activity of the antagonist muscles were intermingled but disjoint. They were distributed over a wide area of pre-motor cortex and M1 and not limited to regions generally associated with wrist control. These results show that brain activity measured by fMRI in humans can be used to predict individual muscle activity through Bayesian linear models, and that our algorithm provides a novel and non-invasive tool to investigate the brain mechanisms involved in motor control and learning in humans.
在人类中，通常不可能使用侵入性技术来识别与单个肌肉活动相对应的大脑活动。此外，人们认为非侵入性脑成像模态的空间分辨率不足以分离与单个肌肉相关的神经活动。然而，本研究表明，可以从功能磁共振成像（fMRI）中重建肌肉活动。在一项需要手腕肌肉进行交互激活和共同激活的等长任务期间，我们同时使用fMRI记录了来自两块拮抗肌的表面肌电图（EMG）和运动皮层活动。贝叶斯稀疏回归用于识别从4区（M1）和6区（运动前区、辅助运动区）的fMRI活动到EMG的线性映射参数，并在独立测试数据集中重建肌肉活动。通过稀疏回归算法获得的映射显示出比从解码中常用的算法（即支持向量机和最小二乘回归）获得的映射具有显著更好的泛化能力。与拮抗肌活动相对应的两组体素相互交织但不重叠。它们分布在运动前皮层和M1的广泛区域，并不局限于通常与手腕控制相关的区域。这些结果表明，通过fMRI测量的人类大脑活动可用于通过贝叶斯线性模型预测个体肌肉活动，并且我们的算法提供了一种新颖的非侵入性工具来研究人类运动控制和学习所涉及的大脑机制。
