论文标题
使用图表上的信号处理估算和分析神经信息流
Estimating and Analyzing Neural Information Flow Using Signal Processing on Graphs
论文作者
论文摘要
将脑网络中的神经交流与行为和认知相关联，可以为健康和患病大脑的功能提供基本的见解。我们证明了如何使用图形信号处理中的概念从记录的神经活动中估算大脑中的通信。通信被建模为图形边缘上的流信号，并且自然来自图扩散过程。我们将扩散模型应用于来自两个非人类灵长类动物的感觉运动皮层的微皮质摄影（ECOG）记录，以估计兴奋性光遗传学期间的神经通信流。与基线模型的比较表明，添加神经流可以改善ECOG预测。最后，我们证明了如何将神经流分解为梯度和旋转成分，并证明梯度成分取决于刺激的位置。这项技术首次提供了以前所未有的时空量表研究神经交流的机会。
Correlating neural communication in brain networks with behavior and cognition can provide fundamental insights into the functionality of both healthy and diseased brains. We demonstrate how communication in the brain can be estimated from recorded neural activity using concepts from graph signal processing. The communication is modeled as a flow signals on the edges of a graph and naturally arises from a graph diffusion process. We apply the diffusion model to micro-electrocorticography (ECoG) recordings from sensorimotor cortex of two non-human primates to estimate the neural communication flow during excitatory optogenetics. Comparisons with a baseline model demonstrate that adding the neural flow can improve ECoG predictions. Finally, we demonstrate how the neural flow can be decomposed into a gradient and rotational component and show that the gradient component depends on the location of stimulation. This technique, for the first time, offers the opportunity to study neural communication on an unprecedented spatiotemporal scale.
