Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings. Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding. Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only. Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.

目标。白质组织约占人类大脑体积的 50%，它被广泛认为是在中枢神经系统区域之间传递信息的信使。然而，白质神经活动的特征以及白质神经记录是否有助于运动解码往往被忽视，并且仍然很大程度上未知。在这项工作中，我们使用侵入性神经记录进行定量分析以研究这两个重要问题。方法. 我们在视觉提示的运动任务中记录了 32 名人类受试者的立体脑电图 (SEEG) 数据，其中 SEEG 记录可以同时利用灰质和白质电活动。使用近端组织密度方法，我们确定了每个 SEEG 接触的位置（即灰质或白质）。关注 alpha 振荡和高伽马活动，我们比较了灰质和白质之间的激活模式。然后，我们评估了这种白质激活在运动解码中的性能。主要结果. 结果表明，白质在任务下也表现出激活，与灰质类似，但幅度明显较低。此外，这项工作还表明，与仅使用灰质信号相比，将白质神经活动与灰质神经活动结合在一起可以显着提高运动解码的准确性。意义。利用来自大量受试者的 SEEG 记录，我们揭示了任务下白质神经信号的响应特征，并展示了其在运动解码中的增强功能。这项研究强调了在进一步的科学研究和转化应用中考虑白质活动的重要性。