Angol nyelvű Tudományos Szakcikk (Folyóiratcikk)

- SJR Scopus - Computer Science (miscellaneous): Q1

Azonosítók

- MTMT: 31023492
- DOI: 10.1109/ACCESS.2019.2934490
- WoS: 000482040300001

Szakterületek:

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.

2021-05-13 02:24