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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mullen, T. Acar, Z.A. Worrell, G. Makeig, S. |
| Copyright Year | 2011 |
| Description | Author affiliation: Faculty Department of Neurology, The Mayo Clinic, Rochester MN, USA (Worrell, G.) || Department of Cognitive Science and Swartz Center for Computational Neuroscience (SCCN), Institute for Neural Computation (INC), UCSD (Mullen, T.) || SCCN, INC, UCSD (Acar, Z.A.; Makeig, S.) |
| Abstract | Mapping the dynamics and spatial topography of brain source processes critically involved in initiating and propagating seizure activity is critical for effective epilepsy diagnosis, intervention, and treatment. In this report we analyze neuronal dynamics before and during epileptic seizures using adaptive multivariate autoregressive (VAR) models applied to maximally-independent (ICA) sources of intracranial EEG (iEEG, ECoG) data recorded from subdural electrodes implanted in a human patient for evaluation of surgery for epilepsy. We visualize the spatial distribution of causal sources and sinks of ictal activity on the cortical surface (gyral and sulcal) using a novel combination of multivariate Granger-causal and graph-theoretic metrics combined with distributed source localization by Sparse Bayesian Learning applied to a multi-scale patch basis. This analysis reveals and visualizes distinct, seizure stage-dependent shifts in inter-component spatiotemporal dynamics and connectivity including the clinically-identified epileptic foci. |
| Starting Page | 1411 |
| Ending Page | 1414 |
| File Size | 2193296 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424441211 |
| ISSN | 1557170X |
| e-ISBN | 9781457715891 |
| e-ISBN | 9781424441228 |
| DOI | 10.1109/IEMBS.2011.6090332 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Brain models Adaptation models Biological system modeling Electroencephalography Integrated circuit modeling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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