Literature DB >> 15661122

Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings.

Y Lai1, W van Drongelen, L Ding, K E Hecox, V L Towle, D M Frim, B He.   

Abstract

OBJECTIVE: The present study aims to accurately estimate the in vivo brain-to-skull conductivity ratio by means of cortical imaging technique. Simultaneous extra- and intra-cranial potential recordings induced by subdural current stimulation were analyzed to get the estimation.
METHODS: The effective brain-to-skull conductivity ratio was estimated in vivo for 5 epilepsy patients. The estimation was performed using multi-channel simultaneously recorded scalp and cortical electrical potentials during subdural electrical stimulation. The cortical imaging technique was used to compute the inverse cortical potential distribution from the scalp recorded potentials using a 3-shell head volume conductor model. The brain-to-skull conductivity ratio, which leads to the most consistent cortical potential estimates with respect to the direct intra-cranial measurements, is considered to be the effective brain-to-skull conductivity ratio.
RESULTS: The present estimation provided consistent results in 5 human subjects studied. The in vivo effective brain-to-skull conductivity ratio ranged from 18 to 34 in the 5 epilepsy patients.
CONCLUSIONS: The effective brain-to-skull conductivity ratio can be estimated from simultaneous intra- and extra-cranial potential recordings and the averaged value/standard deviation is 25+/-7. SIGNIFICANCE: The present results provide important experimental data on the brain-to-skull conductivity ratio, which is of significance for accurate brain source localization using piece-wise homogeneous head models.

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Year:  2005        PMID: 15661122     DOI: 10.1016/j.clinph.2004.08.017

Source DB:  PubMed          Journal:  Clin Neurophysiol        ISSN: 1388-2457            Impact factor:   3.708


  66 in total

1.  A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method.

Authors:  Yingchun Zhang; Lei Ding; Wim van Drongelen; Kurt Hecox; David M Frim; Bin He
Journal:  Neuroimage       Date:  2006-05-02       Impact factor: 6.556

2.  Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging.

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3.  Modeling of the human skull in EEG source analysis.

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Journal:  Hum Brain Mapp       Date:  2010-08-05       Impact factor: 5.038

4.  EEG-fMRI reciprocal functional neuroimaging.

Authors:  Lin Yang; Zhongming Liu; Bin He
Journal:  Clin Neurophysiol       Date:  2010-04-08       Impact factor: 3.708

5.  Noninvasive cortical imaging of epileptiform activities from interictal spikes in pediatric patients.

Authors:  Yuan Lai; Xin Zhang; Wim van Drongelen; Michael Korhman; Kurt Hecox; Ying Ni; Bin He
Journal:  Neuroimage       Date:  2010-07-17       Impact factor: 6.556

6.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks.

Authors:  Bradley J Edelman; Bryan Baxter; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2015-08-12       Impact factor: 4.538

7.  Ictal source analysis: localization and imaging of causal interactions in humans.

Authors:  Lei Ding; Gregory A Worrell; Terrence D Lagerlund; Bin He
Journal:  Neuroimage       Date:  2006-11-16       Impact factor: 6.556

8.  Effects of fMRI-EEG mismatches in cortical current density estimation integrating fMRI and EEG: a simulation study.

Authors:  Zhongming Liu; Fedja Kecman; Bin He
Journal:  Clin Neurophysiol       Date:  2006-06-09       Impact factor: 3.708

9.  A new magnetic resonance electrical impedance tomography (MREIT) algorithm: the RSM-MREIT algorithm with applications to estimation of human head conductivity.

Authors:  Nuo Gao; S A Zhu; Bin He
Journal:  Phys Med Biol       Date:  2006-05-31       Impact factor: 3.609

10.  Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain.

Authors:  Han Yuan; Alexander Doud; Arvind Gururajan; Bin He
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2008-10       Impact factor: 3.802

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