Literature DB >> 12450358

Enhancement of phase clustering in the EEG/MEG gamma frequency band anticipates transitions to paroxysmal epileptiform activity in epileptic patients with known visual sensitivity.

Stiliyan Kalitzin1, Jaime Parra, Demetrios N Velis, F H Lopes da Silva.   

Abstract

A new analytical method for quantifying brain activity from magnetoelectroencephalogram (MEG) and electroencephalogram (EEG) recordings during periodic light stimulation is proposed. It consists in estimating the phase clustering of harmonically related frequency components of a subject's MEG/EEG responses evoked by the light stimulation. The method was developed to test the hypothesis that changes in the dynamics of brain systems in the course of intermittent photic stimulation (IPS) may precede the transition to seizure activity in photosensitive patients. We assumed that such changes would be reflected in the phase of harmonic components of the evoked responses. Thus, we determined the phase clustering for different harmonic components of these MEG/EEG signals. We found that the patients who develop epileptiform discharges during IPS present an enhancement of the phase clustering index at the gamma frequency band, compared with that at the driving frequency. We introduce a quantity--relative phase clustering index (rPCI)--by means of which this enhancement can be quantified. We argue that this quantity reflects the degree of excitability of the underlying dynamical system and it can indicate presence of nonlinear dynamics. rPCI can be applied to detect transitions to epileptic seizure activity in patients with known sensitivity to IPS.

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Year:  2002        PMID: 12450358     DOI: 10.1109/TBME.2002.804593

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  9 in total

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Journal:  Neurosurg Clin N Am       Date:  2011-10       Impact factor: 2.509

2.  Auditory fear conditioning modifies steady-state evoked potentials in the rat inferior colliculus.

Authors:  André Luiz Vieira Lockmann; Flávio Afonso Gonçalves Mourão; Marcio Flávio Dutra Moraes
Journal:  J Neurophysiol       Date:  2017-04-26       Impact factor: 2.714

3.  Studying network mechanisms using intracranial stimulation in epileptic patients.

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Journal:  Front Syst Neurosci       Date:  2010-10-20

4.  Modeling brain resonance phenomena using a neural mass model.

Authors:  Andreas Spiegler; Thomas R Knösche; Karin Schwab; Jens Haueisen; Fatihcan M Atay
Journal:  PLoS Comput Biol       Date:  2011-12-22       Impact factor: 4.475

5.  Rod Driven Frequency Entrainment and Resonance Phenomena.

Authors:  Christina Salchow; Daniel Strohmeier; Sascha Klee; Dunja Jannek; Karin Schiecke; Herbert Witte; Arye Nehorai; Jens Haueisen
Journal:  Front Hum Neurosci       Date:  2016-08-18       Impact factor: 3.169

6.  Absence Seizure Control by a Brain Computer Interface.

Authors:  Vladimir A Maksimenko; Sabrina van Heukelum; Vladimir V Makarov; Janita Kelderhuis; Annika Lüttjohann; Alexey A Koronovskii; Alexander E Hramov; Gilles van Luijtelaar
Journal:  Sci Rep       Date:  2017-05-29       Impact factor: 4.379

7.  Coupled CP Decomposition of Simultaneous MEG-EEG Signals for Differentiating Oscillators During Photic Driving.

Authors:  Kristina Naskovska; Stephan Lau; Alexey A Korobkov; Jens Haueisen; Martin Haardt
Journal:  Front Neurosci       Date:  2020-04-09       Impact factor: 4.677

8.  Active probing to highlight approaching transitions to ictal states in coupled neural mass models.

Authors:  Vinícius Rezende Carvalho; Márcio Flávio Dutra Moraes; Sydney S Cash; Eduardo Mazoni Andrade Marçal Mendes
Journal:  PLoS Comput Biol       Date:  2021-01-25       Impact factor: 4.475

Review 9.  Inferring functional neural connectivity with phase synchronization analysis: a review of methodology.

Authors:  Junfeng Sun; Zhijun Li; Shanbao Tong
Journal:  Comput Math Methods Med       Date:  2012-04-22       Impact factor: 2.238

  9 in total

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