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Inferring Spatiotemporal Network Patterns from Intracranial EEG Data. / Ossadtchi, A.; Greenblatt, R.E.; Towle, V.L.; Kohrman, M.H.; Kamada, K.

In: Clinical Neurophysiology, Vol. 121, No. 6, 2010, p. 823-835.

Research output: Contribution to journal › Article

Harvard

Ossadtchi, A, Greenblatt, RE, Towle, VL, Kohrman, MH & Kamada, K 2010, 'Inferring Spatiotemporal Network Patterns from Intracranial EEG Data', Clinical Neurophysiology, vol. 121, no. 6, pp. 823-835. https://doi.org/10.1016/j.clinph.2009.12.036

APA

Ossadtchi, A., Greenblatt, R. E., Towle, V. L., Kohrman, M. H., & Kamada, K. (2010). Inferring Spatiotemporal Network Patterns from Intracranial EEG Data. Clinical Neurophysiology, 121(6), 823-835. https://doi.org/10.1016/j.clinph.2009.12.036

Vancouver

Ossadtchi A, Greenblatt RE, Towle VL, Kohrman MH, Kamada K. Inferring Spatiotemporal Network Patterns from Intracranial EEG Data. Clinical Neurophysiology. 2010;121(6):823-835. https://doi.org/10.1016/j.clinph.2009.12.036

Author

Ossadtchi, A. ; Greenblatt, R.E. ; Towle, V.L. ; Kohrman, M.H. ; Kamada, K. / Inferring Spatiotemporal Network Patterns from Intracranial EEG Data. In: Clinical Neurophysiology. 2010 ; Vol. 121, No. 6. pp. 823-835.

BibTeX

@article{9f80d69a444049bb97e2e97e2eefa6ca,
title = "Inferring Spatiotemporal Network Patterns from Intracranial EEG Data",
abstract = "OBJECTIVE: The characterization of spatial network dynamics is desirable for a better understanding of seizure physiology. The goal of this work is to develop a computational method for identifying transient spatial patterns from intracranial electroencephalographic (iEEG) data. METHODS: Starting with bivariate synchrony measures, such as phase correlation, a two-step clustering procedure is used to identify statistically significant spatial network patterns, whose temporal evolution can be inferred. We refer to this as the composite synchrony profile (CSP) method. RESULTS: The CSP method was verified with simulated data and evaluated using ictal and interictal recordings from three patients with intractable epilepsy. Application of the CSP method to these clinical iEEG datasets revealed a set of distinct CSPs with topographies consistent with medial temporal/limbic and superior parietal/medial frontal networks thought to be involved in the seizure generation process. CONCLUSIONS: By combining relatively stra",
keywords = "Epilepsy, network dynamics, spatial dynamics, seizure onset, phase synchrony",
author = "A. Ossadtchi and R.E. Greenblatt and V.L. Towle and M.H. Kohrman and K. Kamada",
year = "2010",
doi = "10.1016/j.clinph.2009.12.036",
language = "не определен",
volume = "121",
pages = "823--835",
journal = "Clinical Neurophysiology",
issn = "1388-2457",
publisher = "Elsevier",
number = "6",

}

RIS

TY - JOUR

T1 - Inferring Spatiotemporal Network Patterns from Intracranial EEG Data

AU - Ossadtchi, A.

AU - Greenblatt, R.E.

AU - Towle, V.L.

AU - Kohrman, M.H.

AU - Kamada, K.

PY - 2010

Y1 - 2010

N2 - OBJECTIVE: The characterization of spatial network dynamics is desirable for a better understanding of seizure physiology. The goal of this work is to develop a computational method for identifying transient spatial patterns from intracranial electroencephalographic (iEEG) data. METHODS: Starting with bivariate synchrony measures, such as phase correlation, a two-step clustering procedure is used to identify statistically significant spatial network patterns, whose temporal evolution can be inferred. We refer to this as the composite synchrony profile (CSP) method. RESULTS: The CSP method was verified with simulated data and evaluated using ictal and interictal recordings from three patients with intractable epilepsy. Application of the CSP method to these clinical iEEG datasets revealed a set of distinct CSPs with topographies consistent with medial temporal/limbic and superior parietal/medial frontal networks thought to be involved in the seizure generation process. CONCLUSIONS: By combining relatively stra

AB - OBJECTIVE: The characterization of spatial network dynamics is desirable for a better understanding of seizure physiology. The goal of this work is to develop a computational method for identifying transient spatial patterns from intracranial electroencephalographic (iEEG) data. METHODS: Starting with bivariate synchrony measures, such as phase correlation, a two-step clustering procedure is used to identify statistically significant spatial network patterns, whose temporal evolution can be inferred. We refer to this as the composite synchrony profile (CSP) method. RESULTS: The CSP method was verified with simulated data and evaluated using ictal and interictal recordings from three patients with intractable epilepsy. Application of the CSP method to these clinical iEEG datasets revealed a set of distinct CSPs with topographies consistent with medial temporal/limbic and superior parietal/medial frontal networks thought to be involved in the seizure generation process. CONCLUSIONS: By combining relatively stra

KW - Epilepsy

KW - network dynamics

KW - spatial dynamics

KW - seizure onset

KW - phase synchrony

U2 - 10.1016/j.clinph.2009.12.036

DO - 10.1016/j.clinph.2009.12.036

M3 - статья

VL - 121

SP - 823

EP - 835

JO - Clinical Neurophysiology

JF - Clinical Neurophysiology

SN - 1388-2457

IS - 6

ER -

ID: 5093977