Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States of America.
Neurobiology Section, Division of Biological Sciences, University of California San Diego, La Jolla, CA, United States of America.
J Neural Eng. 2021 Nov 15;18(6). doi: 10.1088/1741-2552/ac33e7.
. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain-computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions.. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings.. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials.. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.
. 脑表面的神经活动电记录已广泛应用于基础神经科学研究和临床实践中,用于研究神经回路功能、脑机接口和神经障碍的治疗。传统上,这些表面电位被认为主要反映局部神经活动。目前尚不清楚局部记录的表面电位对于多个皮质区域的神经活动有多么有信息。. 为了研究这一点,我们在清醒固定头部的小鼠中同时进行局部电记录和宽场钙成像。使用递归神经网络模型,我们试图从局部电记录中解码多个皮质区域的钙荧光活动。. 不同皮质区域的平均活动可以从局部记录的表面电位中解码。此外,表面电位的不同频带以不同的方式编码来自多个皮质区域的活动,因此在解码模型中包含所有频带可以获得最高的解码性能。尽管记录通道之间的间距很近,但来自不同通道的表面电位提供了关于大尺度皮质活动的补充信息,并且随着更多通道的加入,解码性能继续提高。最后,我们展示了使用局部记录的表面电位成功地以像素级解码整个背侧皮质活动。. 这些结果表明,局部记录的表面电位确实包含丰富的大尺度神经活动信息,可以进一步分离以恢复单个皮质区域的神经活动。在未来,我们的跨模态推断方法可以适用于虚拟重建皮层宽脑活动,在不增加侵入性的情况下大大扩展表面电记录的空间范围。此外,它可以用于促进自由运动动物整个皮层的神经活动成像,而不需要头固定显微镜配置。