TY - GEN
T1 - Modeling cortical source dynamics and interactions during seizure
AU - Mullen, Tim
AU - Acar, Zeynep Akalin
AU - Worrell, Gregory
AU - Makeig, Scott
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84862254094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862254094&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2011.6090332
DO - 10.1109/IEMBS.2011.6090332
M3 - Conference contribution
C2 - 22254582
AN - SCOPUS:84862254094
SN - 9781424441211
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1411
EP - 1414
BT - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
T2 - 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Y2 - 30 August 2011 through 3 September 2011
ER -