Literature DB >> 27328313

EEG and MEG: sensitivity to epileptic spike activity as function of source orientation and depth.

A Hunold1, M E Funke, R Eichardt, M Stenroos, J Haueisen.   

Abstract

Simultaneous electroencephalography (EEG) and magnetoencephalography (MEG) recordings of neuronal activity from epileptic patients reveal situations in which either EEG or MEG or both modalities show visible interictal spikes. While different signal-to-noise ratios (SNRs) of the spikes in EEG and MEG have been reported, a quantitative relation of spike source orientation and depth as well as the background brain activity to the SNR has not been established. We investigated this quantitative relationship for both dipole and patch sources in an anatomically realistic cortex model. Altogether, 5600 dipole and 3300 patch sources were distributed on the segmented cortical surfaces of two volunteers. The sources were classified according to their quantified depths and orientations, ranging from 20 mm to 60 mm below the skin surface and radial and tangential, respectively. The source time-courses mimicked an interictal spike, and the simulated background activity emulated resting activity. Simulations were conducted with individual three-compartment boundary element models. The SNR was evaluated for 128 EEG, 102 MEG magnetometer, and 204 MEG gradiometer channels. For superficial dipole and superficial patch sources, EEG showed higher SNRs for dominantly radial orientations, and MEG showed higher values for dominantly tangential orientations. Gradiometers provided higher SNR than magnetometers for superficial sources, particularly for those with dominantly tangential orientations. The orientation dependent difference in SNR in EEG and MEG gradually changed as the sources were located deeper, where the interictal spikes generated higher SNRs in EEG compared to those in MEG for all source orientations. With deep sources, the SNRs in gradiometers and magnetometers were of the same order. To better detect spikes, both EEG and MEG should be used.

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Year:  2016        PMID: 27328313     DOI: 10.1088/0967-3334/37/7/1146

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  14 in total

1.  Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy.

Authors:  Rasheda Arman Chowdhury; Giovanni Pellegrino; Ümit Aydin; Jean-Marc Lina; François Dubeau; Eliane Kobayashi; Christophe Grova
Journal:  Hum Brain Mapp       Date:  2017-11-21       Impact factor: 5.038

2.  The Effect of Head Model Simplification on Beamformer Source Localization.

Authors:  Frank Neugebauer; Gabriel Möddel; Stefan Rampp; Martin Burger; Carsten H Wolters
Journal:  Front Neurosci       Date:  2017-11-09       Impact factor: 4.677

3.  Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data.

Authors:  Miguel C Soriano; Guiomar Niso; Jillian Clements; Silvia Ortín; Sira Carrasco; María Gudín; Claudio R Mirasso; Ernesto Pereda
Journal:  Front Neuroinform       Date:  2017-06-30       Impact factor: 4.081

4.  Dry Electrodes for Surface Electromyography Based on Architectured Titanium Thin Films.

Authors:  Marco S Rodrigues; Patrique Fiedler; Nora Küchler; Rui P Domingues; Cláudia Lopes; Joel Borges; Jens Haueisen; Filipe Vaz
Journal:  Materials (Basel)       Date:  2020-05-05       Impact factor: 3.623

5.  Theta-Band Oscillations as an Indicator of Mild Traumatic Brain Injury.

Authors:  Hanna Kaltiainen; Liisa Helle; Mia Liljeström; Hanna Renvall; Nina Forss
Journal:  Brain Topogr       Date:  2018-08-10       Impact factor: 3.020

6.  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

7.  Head phantoms for bioelectromagnetic applications: a material study.

Authors:  Alexander Hunold; René Machts; Jens Haueisen
Journal:  Biomed Eng Online       Date:  2020-11-23       Impact factor: 2.819

Review 8.  A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.

Authors:  Aina Puce; Matti S Hämäläinen
Journal:  Brain Sci       Date:  2017-05-31

9.  Using MEG to Understand the Progression of Light Sleep and the Emergence and Functional Roles of Spindles and K-Complexes.

Authors:  Andreas A Ioannides; Lichan Liu; Vahe Poghosyan; George K Kostopoulos
Journal:  Front Hum Neurosci       Date:  2017-06-16       Impact factor: 3.169

10.  A comprehensive study on electroencephalography and magnetoencephalography sensitivity to cortical and subcortical sources.

Authors:  Maria Carla Piastra; Andreas Nüßing; Johannes Vorwerk; Maureen Clerc; Christian Engwer; Carsten H Wolters
Journal:  Hum Brain Mapp       Date:  2020-11-06       Impact factor: 5.399

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