Tavildar Siddhi, Mogen Brian, Zanos Stavros, Seeman Stephanie, Perlmutter Steve, Fetz Eberhard, Ashrafi Ashkan
Computational Science Research Center, San Diego State University, San Diego CA, USA.
Center for Neurotechnology, Seattle WA, USA.
IEEE Access. 2019;7:109349-109362. doi: 10.1109/access.2019.2934490. Epub 2019 Aug 12.
A novel method to characterize connectivity between sites in the cerebral cortex of primates is proposed in this paper. Connectivity graphs for two macaque monkeys are inferred from Electrocorticographic (ECoG) activity recorded while the animals were alert. The locations of ECoG electrodes are considered as nodes of the graph, the coefficients of the auto-regressive (AR) representation of the signals measured at each node are considered as the signal on the graph and the connectivity strengths between the nodes are considered as the edges of the graph. Maximization of the graph smoothness defined from the Laplacian quadratic form is used to infer the connectivity map (adjacency matrix of the graph). The cortical evoked potential (CEP) map was obtained by stimulating different electrodes and recording the evoked potentials at the other electrodes. The maps obtained by the graph inference and the traditional method of spectral coherence are compared with the CEP map. The results show that the proposed method provides a description of cortical connectivity that is more similar to the stimulation-based measures than spectral coherence. The results are also tested by the surrogate map analysis in which the CEP map is randomly permuted and the distribution of the errors is obtained. It is shown that error between the two maps is comfortably outside the surrogate map error distribution. This indicates that the similarity between the map calculated by the graph inference and the CEP map is statistically significant.
本文提出了一种表征灵长类动物大脑皮质不同部位之间连接性的新方法。从两只猕猴在清醒状态下记录的皮层脑电图（ECoG）活动中推断出连接性图谱。将ECoG电极的位置视为图谱的节点，将在每个节点处测量的信号的自回归（AR）表示的系数视为图谱上的信号，并将节点之间的连接强度视为图谱的边。利用由拉普拉斯二次型定义的图谱平滑度最大化来推断连接性图谱（图谱的邻接矩阵）。通过刺激不同电极并在其他电极上记录诱发电位来获得皮质诱发电位（CEP）图谱。将通过图谱推断和传统谱相干方法获得的图谱与CEP图谱进行比较。结果表明，所提出的方法提供的皮质连接性描述比谱相干更类似于基于刺激的测量方法。还通过替代图谱分析对结果进行了测试，在替代图谱分析中，对CEP图谱进行随机置换并获得误差分布。结果表明，两张图谱之间的误差明显超出替代图谱误差分布范围。这表明通过图谱推断计算出的图谱与CEP图谱之间的相似性具有统计学意义。
