Role of Soft Bone, CSF and Gray Matter in EEG Simulations

Ramon, Ceon; Schimpf, P.H.; Haueisen, Jens; Holmes, Mark; Ishimaru, Akira

doi:10.1023/b:brat.0000032859.68959.76

Cited by 85 publications

(110 citation statements)

References 11 publications

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“…First, because CSF is highly conductive, minute shifts in CSF concentration can cause substantial alterations in EEG signals (Ramon et al, 2006;Ramon et al, 2004;Wendel et al, 2008). Using upright and recumbent MRI scanners, findings demonstrated that intracranial CSF concentration decreased when sitting up compared with lying down (Alperin et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

Posture alters human resting-state

Thibault

Lifshitz

Jones

et al. 2014

Cortex

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Neuroimaging is ubiquitous; however, neuroimagers seldom investigate the putative impact of posture on brain activity. Whereas participants in most psychological experiments sit upright, many prominent neuroimaging techniques (e.g., functional magnetic resonance imaging (fMRI)) require participants to lie supine. Such postural discrepancies may hold important implications for brain function in general and for fMRI in particular. We directly investigated the effect of posture on spontaneous brain dynamics by recording scalp electrical activity in four orthostatic conditions (lying supine, inclined at 45°, sitting upright, and standing erect). Here we show that upright versus supine posture increases widespread high-frequency oscillatory activity.Our electroencephalographic findings highlight the importance of posture as a determinant in neuroimaging. When generalizing supine imaging results to ecological human cognition, therefore, cognitive neuroscientists would benefit from considering the influence of posture on brain dynamics.

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Section: Discussionmentioning

confidence: 99%

Posture alters human resting-state

Thibault

Lifshitz

Jones

et al. 2014

Cortex

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“…To construct a realistic volume conductor model requires segmentation of the different tissues within the head with special attention to the poorly conducting human skull and the highly conductive CSF (Hämäläinen and Sarvas, 1987;Cuffin, 1996;Roth et al, 1993;Huiskamp et al, 1999;Ramon et al, 2004).…”

Section: Registration and Segmentationmentioning

confidence: 99%

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling

et al. 2006

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To achieve a deeper understanding of the brain, scientists, and clinicians use electroencephalography (EEG) and magnetoencephalography (MEG) inverse methods to reconstruct sources in the cortical sheet of the human brain. The influence of structural and electrical anisotropy in both the skull and the white matter on the EEG and MEG source reconstruction is not well understood.In this paper, we report on a study of the sensitivity to tissue anisotropy of the EEG/MEG forward problem for deep and superficial neocortical sources with differing orientation components in an anatomically accurate model of the human head.The goal of the study was to gain insight into the effect of anisotropy of skull and white matter conductivity through the visualization of field distributions, isopotential surfaces, and return current flow and through statistical error measures. One implicit premise of the study is that factors that affect the accuracy of the forward solution will have at least as strong an influence over solutions to the associated inverse problem.Major findings of the study include (1) anisotropic white matter conductivity causes return currents to flow in directions parallel to the white matter fiber tracts; (2) skull anisotropy has a smearing effect on the forward potential computation; and (3) the deeper a source lies and the more it is surrounded by anisotropic tissue, the larger the influence of this anisotropy on the resulting electric and magnetic fields. Therefore, for the EEG, the presence of tissue anisotropy both for the skull and white matter compartment substantially compromises the forward potential computation and as a consequence, the inverse source reconstruction. In contrast, for the MEG, only the anisotropy of the white matter compartment has a significant effect. Finally, return currents with high amplitudes were found in the highly conducting cerebrospinal fluid compartment, underscoring the need for accurate modeling of this space. D

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“…28 These have emphasized the role of accurate segmentation of the cerebrospinal fluid (CSF) and bone. 29 A detailed 3D 29 The electrical activity in the top portion of the brain, above the eye level, was simulated with 125 dipoles randomly located in different parts of the brain. The dipole intensity distribution was in the range of 0.0 to 0.4 mA meter with a uniform random distribution.…”

Section: Segmentation Of the Head Mri Data For Modelling Electrical Amentioning

confidence: 99%

“…The dipole intensity distribution was in the range of 0.0 to 0.4 mA meter with a uniform random distribution. An adaptive FEM solver, described in Ramon et al (2004), 29 and Schimpf et al (1998) 30 , was used to compute flux and potential distribution in the whole head model. 31 Figure 9 shows that the current flow pattern follows the anatomical tissue boundaries very accurately.…”

Section: Segmentation Of the Head Mri Data For Modelling Electrical Amentioning

confidence: 99%

CT and MRI assessment and characterization using segmentation and 3D modeling techniques: applications to muscle, bone and brain

et al. 2014

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This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN) denervation, 2. muscle recovery as induced by functional electrical stimulation (FES), 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD) and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological conditions by developing detailed 3D representations of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information with the equivalent electrical dipole sources as an input into the model.

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Role of Soft Bone, CSF and Gray Matter in EEG Simulations

Cited by 85 publications

References 11 publications

Posture alters human resting-state

Posture alters human resting-state

Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling

CT and MRI assessment and characterization using segmentation and 3D modeling techniques: applications to muscle, bone and brain

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