Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing
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 cons...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8794547/ |
| _version_ | 1818596707653386240 |
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| author | Siddhi Tavildar Brian Mogen Stavros Zanos Stephanie C. Seeman Steve I. Perlmutter Eberhard Fetz Ashkan Ashrafi |
| author_facet | Siddhi Tavildar Brian Mogen Stavros Zanos Stephanie C. Seeman Steve I. Perlmutter Eberhard Fetz Ashkan Ashrafi |
| author_sort | Siddhi Tavildar |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T11:36:11Z |
| format | Article |
| id | doaj.art-7689b2d738344e78856bc5c07f88765e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T11:36:11Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7689b2d738344e78856bc5c07f88765e2022-12-21T22:33:06ZengIEEEIEEE Access2169-35362019-01-01710934910936210.1109/ACCESS.2019.29344908794547Inferring Cortical Connectivity From ECoG Signals Using Graph Signal ProcessingSiddhi Tavildar0https://orcid.org/0000-0002-7459-6527Brian Mogen1Stavros Zanos2Stephanie C. Seeman3Steve I. Perlmutter4Eberhard Fetz5Ashkan Ashrafi6https://orcid.org/0000-0001-7073-0883Computational Science Research Center, San Diego State University, San Diego, CA, USACenter for Neurotechnology, Seattle, WA, USACenter for Neurotechnology, Seattle, WA, USACenter for Neurotechnology, Seattle, WA, USACenter for Neurotechnology, Seattle, WA, USACenter for Neurotechnology, Seattle, WA, USAComputational Science Research Center, San Diego State University, San Diego, CA, USAA 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.https://ieeexplore.ieee.org/document/8794547/Brain connectivitycortical connectivityelectrocorticography (ECoG)graph learninggraph signal processingneural signal processing |
| spellingShingle | Siddhi Tavildar Brian Mogen Stavros Zanos Stephanie C. Seeman Steve I. Perlmutter Eberhard Fetz Ashkan Ashrafi Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing IEEE Access Brain connectivity cortical connectivity electrocorticography (ECoG) graph learning graph signal processing neural signal processing |
| title | Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing |
| title_full | Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing |
| title_fullStr | Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing |
| title_full_unstemmed | Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing |
| title_short | Inferring Cortical Connectivity From ECoG Signals Using Graph Signal Processing |
| title_sort | inferring cortical connectivity from ecog signals using graph signal processing |
| topic | Brain connectivity cortical connectivity electrocorticography (ECoG) graph learning graph signal processing neural signal processing |
| url | https://ieeexplore.ieee.org/document/8794547/ |
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