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      Stochastic Behavior of Phase Synchronization Index and Cross-Frequency Couplings in Epileptogenic Zones during Interictal Periods Measured with Scalp dEEG

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          Abstract

          The stochastic behavior of the phase synchronization index (SI) and cross-frequency couplings on different days during a hospital stay of three epileptic patients was studied for non-invasive localization of the epileptogenic areas from high density, 256-channel, scalp EEG (dEEG) recordings. The study was performed with short-duration (0–180 s), seizure-free, epileptiform-free, and spike-free interictal dEEG data on different days of three subjects. The seizure areas were localized with subdural recordings with an 8 × 8 macro-electrode grid array and strip electrodes. The study was performed in theta (3–7 Hz), alpha (7–12 Hz), beta (12–30 Hz), and low gamma (30–50 Hz) bands. A detrended fluctuation analysis was used to find the long range temporal correlations in the SI that reveals the stochastic behavior of the SI in a given time period. The phase synchronization was computed after taking Hilbert transform of the EEG data. Contour plots were constructed with 20 s time-frames using a montage of the layout of 256 electrode positions. It was found that the stochastic behavior of the SI was higher in epileptogenic areas and in nearby areas on different days for each subject. The low gamma band was found to be the best to localize the epileptic sites. Also, a stable higher pattern of SI emerged after 60–120 s in the epileptogenic areas. The cross-frequency couplings of SI in theta–gamma, beta–gamma, and alpha–gamma bands were decreased and spatial patterns were fragmented in epileptogenic areas. Combinations of an increase in the stochastic behavior of the SI and decrease in cross-frequency couplings are potential markers to assist in localizing epileptogenic areas. These findings suggest that it is possible to localize the epileptogenic areas non-invasively from a short-duration (∼180 s), seizure-free and spike-free interictal scalp dEEG recordings.

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          Most cited references18

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          Detection ofn:mPhase Locking from Noisy Data: Application to Magnetoencephalography

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            Phase synchrony among neuronal oscillations in the human cortex.

            Synchronization of neuronal activity, often associated with network oscillations, is thought to provide a means for integrating anatomically distributed processing in the brain. Neuronal processing, however, involves simultaneous oscillations in various frequency bands. The mechanisms involved in the integration of such spectrally distributed processing have remained enigmatic. We demonstrate, using magnetoencephalography, that robust cross-frequency phase synchrony is present in the human cortex among oscillations with frequencies from 3 to 80 Hz. Continuous mental arithmetic tasks demanding the retention and summation of items in the working memory enhanced the cross-frequency phase synchrony among alpha (approximately 10 Hz), beta (approximately 20 Hz), and gamma (approximately 30-40 Hz) oscillations. These tasks also enhanced the "classical" within-frequency synchrony in these frequency bands, but the spatial patterns of alpha, beta, and gamma synchronies were distinct and, furthermore, separate from the patterns of cross-frequency phase synchrony. Interestingly, an increase in task load resulted in an enhancement of phase synchrony that was most prominent between gamma- and alpha-band oscillations. These data indicate that cross-frequency phase synchrony is a salient characteristic of ongoing activity in the human cortex and that it is modulated by cognitive task demands. The enhancement of cross-frequency phase synchrony among functionally and spatially distinct networks during mental arithmetic tasks posits it as a candidate mechanism for the integration of spectrally distributed processing.
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              Detrended Fluctuation Analysis: A Scale-Free View on Neuronal Oscillations

              Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT) and links to the NBT tutorial website http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.
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                Author and article information

                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                17 March 2013
                16 May 2013
                2013
                : 4
                : 57
                Affiliations
                [1] 1Department of Electrical Engineering, University of Washington Seattle, WA, USA
                [2] 2Department of Bioengineering, Reykjavik University Reykjavik, Iceland
                [3] 3Department of Neurology, University of Washington Seattle, WA, USA
                Author notes

                Edited by: Don Tucker, Electrical Geodesics, Inc., USA; University of Oregon, USA

                Reviewed by: Jose F. Tellez-Zenteno, University of Saskatchewan, Canada; Silvia Kochen, University of Buenos Aires, Argentina

                *Correspondence: Ceon Ramon, Department of Electrical Engineering, University of Washington, Campus Box 352500, Seattle, WA 98195, USA. e-mail: ceon@ 123456u.washington.edu

                These results in part were presented as a poster at the 66th Annual Meeting of American Epilepsy Society, 30 November–04 December 2012, San Diego, CA.

                This article was submitted to Frontiers in Epilepsy, a specialty of Frontiers in Neurology.

                Article
                10.3389/fneur.2013.00057
                3655632
                23720651
                f71fac05-4e9d-4444-a750-f955bc0d96c6
                Copyright © 2013 Ramon and Holmes.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

                History
                : 15 February 2013
                : 30 April 2013
                Page count
                Figures: 9, Tables: 0, Equations: 3, References: 24, Pages: 11, Words: 6297
                Categories
                Neuroscience
                Original Research

                Neurology
                epilepsy localization,deeg,phase synchronization,stochastic behavior of eeg,cross-frequency couplings

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