dorsal/arxiv
View SchemaComplex networks in brain electrical activity
| Authors | G. Ruffini, C. Ray, J. Marco, L. Fuentemilla, C. Grau |
|---|---|
| Categories | |
| ArXiv ID | physics/0511137 |
| URL | https://arxiv.org/abs/physics/0511137 |
Abstract
We analyze the complex networks associated with brain electrical activity. Multichannel EEG measurements are first processed to obtain 3D voxel activations using the tomographic algorithm LORETA. Then, the correlation of the current intensity activation between voxel pairs is computed to produce a voxel cross-correlation coefficient matrix. Using several correlation thresholds, the cross-correlation matrix is then transformed into a network connectivity matrix and analyzed. To study a specific example, we selected data from an earlier experiment focusing on the MMN brain wave. The resulting analysis highlights significant differences between the spatial activations associated with Standard and Deviant tones, with interesting physiological implications. When compared to random data networks, physiological networks are more connected, with longer links and shorter path lengths. Furthermore, as compared to the Deviant case, Standard data networks are more connected, with longer links and shorter path lengths--i.e., with a stronger ``small worlds'' character. The comparison between both networks shows that areas known to be activated in the MMN wave are connected. In particular, the analysis supports the idea that supra-temporal and inferior frontal data work together in the processing of the differences between sounds by highlighting an increased connectivity in the response to a novel sound.
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"abstract": "We analyze the complex networks associated with brain electrical activity.\nMultichannel EEG measurements are first processed to obtain 3D voxel\nactivations using the tomographic algorithm LORETA. Then, the correlation of\nthe current intensity activation between voxel pairs is computed to produce a\nvoxel cross-correlation coefficient matrix. Using several correlation\nthresholds, the cross-correlation matrix is then transformed into a network\nconnectivity matrix and analyzed. To study a specific example, we selected data\nfrom an earlier experiment focusing on the MMN brain wave. The resulting\nanalysis highlights significant differences between the spatial activations\nassociated with Standard and Deviant tones, with interesting physiological\nimplications. When compared to random data networks, physiological networks are\nmore connected, with longer links and shorter path lengths. Furthermore, as\ncompared to the Deviant case, Standard data networks are more connected, with\nlonger links and shorter path lengths--i.e., with a stronger ``small worlds\u0027\u0027\ncharacter. The comparison between both networks shows that areas known to be\nactivated in the MMN wave are connected. In particular, the analysis supports\nthe idea that supra-temporal and inferior frontal data work together in the\nprocessing of the differences between sounds by highlighting an increased\nconnectivity in the response to a novel sound.",
"arxiv_id": "physics/0511137",
"authors": [
"G. Ruffini",
"C. Ray",
"J. Marco",
"L. Fuentemilla",
"C. Grau"
],
"categories": [
"physics.bio-ph",
"physics.data-an",
"q-bio.NC"
],
"title": "Complex networks in brain electrical activity",
"url": "https://arxiv.org/abs/physics/0511137"
},
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