Literature DB >> 23746499

Forward and inverse electroencephalographic modeling in health and in acute traumatic brain injury.

Andrei Irimia1, S Y Matthew Goh, Carinna M Torgerson, Micah C Chambers, Ron Kikinis, John D Van Horn.   

Abstract

OBJECTIVE: EEG source localization is demonstrated in three cases of acute traumatic brain injury (TBI) with progressive lesion loads using anatomically faithful models of the head which account for pathology.
METHODS: Multimodal magnetic resonance imaging (MRI) volumes were used to generate head models via the finite element method (FEM). A total of 25 tissue types-including 6 types accounting for pathology-were included. To determine the effects of TBI upon source localization accuracy, a minimum-norm operator was used to perform inverse localization and to determine the accuracy of the latter.
RESULTS: The importance of using a more comprehensive number of tissue types is confirmed in both health and in TBI. Pathology omission is found to cause substantial inaccuracies in EEG forward matrix calculations, with lead field sensitivity being underestimated by as much as ≈ 200% in (peri-) contusional regions when TBI-related changes are ignored. Failing to account for such conductivity changes is found to misestimate substantial localization error by up to 35 mm.
CONCLUSIONS: Changes in head conductivity profiles should be accounted for when performing EEG modeling in acute TBI. SIGNIFICANCE: Given the challenges of inverse localization in TBI, this framework can benefit neurotrauma patients by providing useful insights on pathophysiology.
Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Electroencephalography; Magnetic resonance imaging; Traumatic brain injury; finite element method

Mesh:

Year:  2013        PMID: 23746499      PMCID: PMC3805748          DOI: 10.1016/j.clinph.2013.04.336

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


  52 in total

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10.  Acute cognitive deficits after traumatic brain injury predict Alzheimer's disease-like degradation of the human default mode network.

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