PURPOSE: To assess (1) how large-scale correlation of intracranial EEG signals in the high-frequency range (80-200Hz) evolves from the pre-ictal, through the ictal into the postictal state and (2) whether the contribution of local neuronal activity to large-scale EEG correlation differentiates epileptogenic from non-epileptogenic brain tissue. METHODS: Large-scale correlation of intracranial EEG was assessed by the total correlation strength (TCS), a measure derived from the eigenvalue spectra of zero-lag correlation matrices computed in a time-resolved manner by using a moving window approach. The relative change of total correlation strength (Delta(j)) resulting from leaving out EEG channel j ("leave-one-out approach") was used to quantify the contribution of local neuronal activity to large-scale EEG correlation. RESULTS: 19 seizures of 3 patients were analyzed. On average, TCS increased significantly from the pre-ictal to the ictal, and from the ictal to the postictal state. In the pre-ictal state, Delta(j) was significantly more negative when EEG channels that recorded the electrical activity of brain tissue considered to be epileptogenic were left out; the identification of the epileptogenic area, that was subsequently surgically removed in two patients, was based on visual analysis. The spatio-temporal pattern of Delta(j) dramatically changed at seizure onsets and endings, revealing qualitative similarities between the seizures of different patients. DISCUSSION: The evolution of large-scale EEG correlation in the high-frequency range is qualitatively similar to the one previously described for the low-frequency range. Because the two patients who underwent surgery became seizure free, our findings are consistent with the hypothesis that epileptogenic brain tissue may be characterized by its relatively increased contribution to pre-ictal large-scale correlation. Copyright 2009 Elsevier B.V. All rights reserved.
PURPOSE: To assess (1) how large-scale correlation of intracranial EEG signals in the high-frequency range (80-200Hz) evolves from the pre-ictal, through the ictal into the postictal state and (2) whether the contribution of local neuronal activity to large-scale EEG correlation differentiates epileptogenic from non-epileptogenic brain tissue. METHODS: Large-scale correlation of intracranial EEG was assessed by the total correlation strength (TCS), a measure derived from the eigenvalue spectra of zero-lag correlation matrices computed in a time-resolved manner by using a moving window approach. The relative change of total correlation strength (Delta(j)) resulting from leaving out EEG channel j ("leave-one-out approach") was used to quantify the contribution of local neuronal activity to large-scale EEG correlation. RESULTS: 19 seizures of 3 patients were analyzed. On average, TCS increased significantly from the pre-ictal to the ictal, and from the ictal to the postictal state. In the pre-ictal state, Delta(j) was significantly more negative when EEG channels that recorded the electrical activity of brain tissue considered to be epileptogenic were left out; the identification of the epileptogenic area, that was subsequently surgically removed in two patients, was based on visual analysis. The spatio-temporal pattern of Delta(j) dramatically changed at seizure onsets and endings, revealing qualitative similarities between the seizures of different patients. DISCUSSION: The evolution of large-scale EEG correlation in the high-frequency range is qualitatively similar to the one previously described for the low-frequency range. Because the two patients who underwent surgery became seizure free, our findings are consistent with the hypothesis that epileptogenic brain tissue may be characterized by its relatively increased contribution to pre-ictal large-scale correlation. Copyright 2009 Elsevier B.V. All rights reserved.
Authors: Samuel P Burns; Sabato Santaniello; Robert B Yaffe; Christophe C Jouny; Nathan E Crone; Gregory K Bergey; William S Anderson; Sridevi V Sarma Journal: Proc Natl Acad Sci U S A Date: 2014-11-17 Impact factor: 11.205
Authors: Christian Rummel; Marc Goodfellow; Heidemarie Gast; Martinus Hauf; Frédérique Amor; Alexander Stibal; Luigi Mariani; Roland Wiest; Kaspar Schindler Journal: Neuroinformatics Date: 2013-04
Authors: Sabato Santaniello; Samuel P Burns; Alexandra J Golby; Jedediah M Singer; William S Anderson; Sridevi V Sarma Journal: Epilepsy Behav Date: 2011-12 Impact factor: 2.937