Analysis of neuronal source dynamics and connectivity during seizure using adaptive vector autoregressive models, sparse bayesian learning, independent component analysis, and electrocorticography
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1
University of California San Diego, United States
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2
Dept. of Neurology, Mayo Clinic, United States
Understanding the dynamics of neural processes critically involved in initiating and propagating a seizure may help in devising novel methods of seizure detection, intervention and treatment. Furthermore, applications of novel dynamical analysis methods in clinical situations where there is some "ground truth" can validate methods for more general application to cognitive neuroscience. In this poster we analyze neuronal dynamics during epileptic seizures using adaptive multivariate autoregressive (VAR) models applied to quasi-independent (ICA) sources of intracranial EEG (iEEG, ECoG) data recorded from subdural electrodes implanted in a human patient for presurgery monitoring. We analyze the time-frequency dynamics of directed information flow between sources using a multivariate granger-causal method, identifying distinct information flow motifs in different stages of the seizure. We then further examine the spatial distribution in the cortical source domain of causal sources and sinks of ictal activity using a novel combination of graph theoretic metrics and Sparse Bayesian Learning-based source localization. Finally, we apply an eigendecomposition method to decompose the VAR model into a system of decoupled oscillators and relaxators (eigenmodes) with characteristic damping times and frequencies. We demonstrate that analysis of a small subset of the most dynamically important eigenmodes may allow effective detection of ictal onset and offset, while also yielding insight into the dynamical structure of the neuronal system. Convergent evidence from these analyses reveals distinct stages in the seizure which correspond to shifts in the spatiotemporal dynamics and connectivity structure between sources in or near the clinically-identified epileptic foci. Funding: Supported by San Diego Fellowship (TM), Glushko Fellowhsip (TM), ONR, NIMH, NINDS, and The Swartz Foundation (Old Field, NY).
Keywords:
Epileptic seizures,
Intra-cranial Electrophysiology
Conference:
XI International Conference on Cognitive Neuroscience (ICON XI), Palma, Mallorca, Spain, 25 Sep - 29 Sep, 2011.
Presentation Type:
Poster Presentation
Topic:
Abstracts
Citation:
Mullen
TR,
Akalin Acar
Z,
Palmer
J,
Worrell
G and
Makeig
S
(2011). Analysis of neuronal source dynamics and connectivity during seizure using adaptive vector autoregressive models, sparse bayesian learning, independent component analysis, and electrocorticography.
Conference Abstract:
XI International Conference on Cognitive Neuroscience (ICON XI).
doi: 10.3389/conf.fnhum.2011.207.00069
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Received:
16 Nov 2011;
Published Online:
25 Nov 2011.
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Correspondence:
Dr. Tim R Mullen, University of California San Diego, San Diego, United States, mullen.tim@gmail.com