Realistic Volume Conductor Model Research Articles

Towards an optimised deep brain stimulation using a large-scale computational network and realistic volume conductor model

Objective. Constructing a theoretical framework to improve deep brain stimulation (DBS) based on the neuronal spatiotemporal patterns of the stimulation-affected areas constitutes a primary target. Approach. We develop a large-scale biophysical network, paired with a realistic volume conductor model, to estimate theoretically efficacious stimulation protocols. Based on previously published anatomically defined structural connectivity, a biophysical basal ganglia-thalamo-cortical neuronal network is constructed using Hodgkin–Huxley dynamics. We define a new biomarker describing the thalamic spatiotemporal activity as a ratio of spiking vs. burst firing. The per cent activation of the different pathways is adapted in the simulation to minimise the differences of the biomarker with respect to its value under healthy conditions. Main results. This neuronal network reproduces spatiotemporal patterns that emerge in Parkinson’s disease. Simulations of the fibre per cent activation for the defined biomarker propose desensitisation of pallido-thalamic synaptic efficacy, induced by high-frequency signals, as one possible crucial mechanism for DBS action. Based on this activation, we define both an optimal electrode position and stimulation protocol using pathway activation modelling. Significance. A key advantage of this research is that it combines different approaches, i.e. the spatiotemporal pattern with the electric field and axonal response modelling, to compute the optimal DBS protocol. By correlating the inherent network dynamics with the activation of white matter fibres, we obtain new insights into the DBS therapeutic action.

Evaluating the influence of anatomical accuracy and electrode positions on EEG forward solutions

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout.Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors.We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.

Reliable EEG source analysis depends on sufficiently detailed and accurate head models. In this study, we investigate how uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments influence the results of EEG source analysis. In a single source scenario, the superficial and focal somatosensory P20/N20 component, we analyze the influence of varying conductivities on dipole reconstructions using a generalized polynomial chaos (gPC) approach. We find that in particular the conductivity uncertainties for skin and skull have a significant influence on the EEG inverse solution, leading to variations in source localization by several centimeters. The conductivity uncertainties for gray and white matter were found to have little influence on the source localization, but a strong influence on the strength and orientation of the reconstructed source, respectively. As the CSF conductivity is most accurately determined of all conductivities in a realistic head model, CSF conductivity uncertainties had a negligible influence on the source reconstruction. This small uncertainty is a further benefit of distinguishing the CSF in realistic volume conductor models.

Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique).

The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping Project. Generated artificial EEGs—with a known ground truth, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. Importantly, for practical applications, it is shown that an average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the “oracle” choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective to the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.

Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art

Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.

Transcutaneous spinal direct current stimulation of the lumbar and sacral spinal cord: a modelling study

Objective. Our aim was to perform a computational study of the electric field (E-field) generated by transcutaneous spinal direct current stimulation (tsDCS) applied over the thoracic, lumbar and sacral spinal cord, in order to assess possible neuromodulatory effects on spinal cord circuitry related with lower limb functions. Approach. A realistic volume conductor model of the human body consisting of 14 tissues was obtained from available databases. Rubber pad electrodes with a metallic connector and a conductive gel layer were modelled. The finite element (FE) method was used to calculate the E-field when a current of 2.5 mA was passed between two electrodes. The main characteristics of the E-field distributions in the spinal grey matter (spinal-GM) and spinal white matter (spinal-WM) were compared for seven montages, with the anode placed either over T10, T8 or L2 spinous processes (s.p.), and the cathode placed over right deltoid (rD), umbilicus (U) and right iliac crest (rIC) areas or T8 s.p. Anisotropic conductivity of spinal-WM and of a group of dorsal muscles near the vertebral column was considered. Main results. The average E-field magnitude was predicted to be above 0.15 V m−1 in spinal cord regions located between the electrodes. L2-T8 and T8-rIC montages resulted in the highest E-field magnitudes in lumbar and sacral spinal segments (>0.30 V m−1). E-field longitudinal component is 3 to 6 times higher than the ventral-dorsal and right-left components in both the spinal-GM and WM. Anatomical features such as CSF narrowing due to vertebrae bony edges or disks intrusions in the spinal canal correlate with local maxima positions. Significance. Computational modelling studies can provide detailed information regarding the electric field in the spinal cord during tsDCS. They are important to guide the design of clinical tsDCS protocols that optimize stimulation of application-specific spinal targets.

Modeling trans-spinal direct current stimulation for the modulation of the lumbar spinal motor pathways

Objective. Trans-spinal direct current stimulation (tsDCS) is a potential new technique for the treatment of spinal cord injury (SCI). TsDCS aims to facilitate plastic changes in the neural pathways of the spinal cord with a positive effect on SCI recovery. To establish tsDCS as a possible treatment option for SCI, it is essential to gain a better understanding of its cause and effects. We seek to understand the acute effect of tsDCS, including the generated electric field (EF) and its polarization effect on the spinal circuits, to determine a cellular target. We further ask how these findings can be interpreted to explain published experimental results. Approach. We use a realistic full body finite element volume conductor model to calculate the EF of a 2.5 mA direct current for three different electrode configurations. We apply the calculated electric field to realistic motoneuron models to investigate static changes in membrane resting potential. The results are combined with existing knowledge about the theoretical effect on a neuronal level and implemented into an existing lumbar spinal network model to simulate the resulting changes on a network level. Main results. Across electrode configurations, the maximum EF inside the spinal cord ranged from 0.47 V m−1 to 0.82 V m−1. Axon terminal polarization was identified to be the dominant cellular target. Also, differences in electrode placement have a large influence on axon terminal polarization. Comparison between the simulated acute effects and the electrophysiological long-term changes observed in human tsDCS studies suggest an inverse relationship between the two. Significance. We provide methods and knowledge for better understanding the effects of tsDCS and serve as a basis for a more targeted and optimized application of tsDCS.

EP 29. Field distribution for deep brain stimulation using an anatomical partially anisotropic volume conductor model of the rat brain

Introduction Deep brain stimulation (DBS) is a common therapy for the late-stage treatment of Parkinson’s disease. In order to explore clinically relevant questions, DBS is studied both in vivo in animal models and in silico in computational models. Objectives Creating an anatomically realistic volume conductor model (VCM) of the rat brain allows for a close interrelationship of in vivo and in silico models. Thus, an optimization of the stimulation field distribution and an improvement of DBS in animal models is facilitated. Various quantities affect the resulting spatial distribution of the stimulation field in a VCM, namely the specific topology of the compartments and the electric properties of brain tissue (e.g., its anisotropy). Methods Based on the quasi-static modeling approach Laplace equation div( σ )grad(Φ) = 0 is solved, with σ as electric conductivity and Φ as electric potential. The potential distribution evolved by DBS is computed with COMSOL Multiphysics® (Comsol). With the digital rat brain atlas in Papp et al., 2014 , the target region for DBS is assembled as three-dimensional structures. Modeled grey matter nuclei are the subthalamic nucleus (STN) and the entopeduncular nucleus (EPN). In the cubic region of interest (3 mm edge length) all white matter structures and cerebrospinal fluid cavities are included. Whilst the electrical conductivities of grey matter may be assumed as isotropic, for white matter an anisotropic conductivity should be assigned. By assuming that white matter has a ten times higher longitudinal conductivity along the axonal fibers than in transverse direction ( Tuch et al., 2001 ), the conductivity tensor is defined. The orientation of the anisotropic properties follows curvilinear coordinates. These coordinates are computed by using the diffusion method implemented in Comsol by solving Laplace’s equation, −grad(U) = 0, where the gradient of the solution U forms the first basis vector of the coordinate system. Thereby the anisotropy follows the shape of the white matter fiber bundles. The bipolar electrode model and the stimulation parameters are based on the in vivo model in Badstubner, 2013 . Results In this model, an anisotropic conductivity following curvilinear coordinates is implemented in the corticofugal pathway (CP). In Fig. 1 the periprosthetic potential either for the fully isotropic or partially anisotropic model is compared. Here, the relative difference of the potential Φ is the difference between a baseline model, with a homogeneous conductivity in all structures, and both heterogeneous models. The relative difference varies up to 15% and 60% in the isotropic and anisotropic case, respectively. Conclusion Our results suggest that neglecting an anatomically realistic anisotropy would lead to incorrect assessments of the neuronal activation.

Using reciprocity for relating the simulation of transcranial current stimulation to the EEG forward problem

To explore the relationship between transcranial current stimulation (tCS) and the electroencephalography (EEG) forward problem, we investigate and compare accuracy and efficiency of a reciprocal and a direct EEG forward approach for dipolar primary current sources both based on the finite element method (FEM), namely the adjoint approach (AA) and the partial integration approach in conjunction with a transfer matrix concept (PI). By analyzing numerical results, comparing to analytically derived EEG forward potentials and estimating computational complexity in spherical shell models, AA turns out to be essentially identical to PI. It is then proven that AA and PI are also algebraically identical even for general head models. This relation offers a direct link between the EEG forward problem and tCS. We then demonstrate how the quasi-analytical EEG forward solutions in sphere models can be used to validate the numerical accuracies of FEM-based tCS simulation approaches. These approaches differ with respect to the ease with which they can be employed for realistic head modeling based on MRI-derived segmentations. We show that while the accuracy of the most easy to realize approach based on regular hexahedral elements is already quite high, it can be significantly improved if a geometry-adaptation of the elements is employed in conjunction with an isoparametric FEM approach. While the latter approach does not involve any additional difficulties for the user, it reaches the high accuracies of surface-segmentation based tetrahedral FEM, which is considerably more difficult to implement and topologically less flexible in practice. Finally, in a highly realistic head volume conductor model and when compared to the regular alternative, the geometry-adapted hexahedral FEM is shown to result in significant changes in tCS current flow orientation and magnitude up to 45° and a factor of 1.66, respectively.

Combining EEG and MEG for the Reconstruction of Epileptic Activity Using a Calibrated Realistic Volume Conductor Model

To increase the reliability for the non-invasive determination of the irritative zone in presurgical epilepsy diagnosis, we introduce here a new experimental and methodological source analysis pipeline that combines the complementary information in EEG and MEG, and apply it to data from a patient, suffering from refractory focal epilepsy. Skull conductivity parameters in a six compartment finite element head model with brain anisotropy, constructed from individual MRI data, are estimated in a calibration procedure using somatosensory evoked potential (SEP) and field (SEF) data. These data are measured in a single run before acquisition of further runs of spontaneous epileptic activity. Our results show that even for single interictal spikes, volume conduction effects dominate over noise and need to be taken into account for accurate source analysis. While cerebrospinal fluid and brain anisotropy influence both modalities, only EEG is sensitive to skull conductivity and conductivity calibration significantly reduces the difference in especially depth localization of both modalities, emphasizing its importance for combining EEG and MEG source analysis. On the other hand, localization differences which are due to the distinct sensitivity profiles of EEG and MEG persist. In case of a moderate error in skull conductivity, combined source analysis results can still profit from the different sensitivity profiles of EEG and MEG to accurately determine location, orientation and strength of the underlying sources. On the other side, significant errors in skull modeling are reflected in EEG reconstruction errors and could reduce the goodness of fit to combined datasets. For combined EEG and MEG source analysis, we therefore recommend calibrating skull conductivity using additionally acquired SEP/SEF data.

A new magneto-cardiogram study using a vector model with a virtual heart and the boundary element method

A cardiac vector model is presented and verified, and then the forward problem for cardiac magnetic fields and electric potential are discussed based on this model and the realistic human torso volume conductor model, including lungs. A torso—cardiac vector model is used for a 12-lead electrocardiographic (ECG) and magneto-cardiogram (MCG) simulation study by using the boundary element method (BEM). Also, we obtain the MCG wave picture using a compound four-channel HTc·SQUID system in a magnetically shielded room. By comparing the simulated results and experimental results, we verify the cardiac vector model and then do a preliminary study of the forward problem of MCG and ECG. Therefore, the results show that the vector model is reasonable in cardiac electrophysiology.

Computational representation of a realistic head and brain volume conductor model: electroencephalography simulation and visualization study

Computational head and brain volume conductor modeling is a practical and non-invasive method to investigate neuroelectrical activity in the brain. Anatomical structures included in a model affect the flow of volume currents and the resulting scalp surface potentials. The influence of different tissues within the head on scalp surface potentials was investigated by constructing five highly detailed, realistic head models from segmented and processed Visible Human Man digital images. The models were: (1) model with 20 different tissues, that is, skin, dense connective tissue (fat), aponeurosis (muscle), outer, middle and inner tables of the scalp, dura matter, arachnoid layer (including cerebrospinal fluid), pia matter, six cortical layers, eye tissue, muscle around the eye, optic nerve, temporal muscle, white matter and internal air, (2) model with three main inhomogeneities, that is, scalp, skull, brain, (3) model with homogeneous scalp and remaining inhomogeneities, (4) model with homogeneous skull and remaining inhomogeneities, and (5) model with homogeneous brain matter and remaining inhomogeneities. Scalp potentials because of three different dipolar sources in the parietal-occipital lobe were computed for all five models. Results of a forward solution revealed that tissues included in the model and the dipole source location directly affect the simulated scalp surface potentials. The major finding indicates that significant change in the scalp surface potentials is observed when the brain's distinctions are removed. The other modifications, for example, layers of the scalp and skull are important too, but they have less effect on the overall results.

Simulation study of a magnetocardiogram based on a virtual heart model: effect of a cardiac equivalent source and a volume conductor**Project supported by the State Key Development Program for Basic Research of China (Grant Nos. 2007CB512100 and 2006CB601007), the National Natural Science Foundation of China (Grant No. 10674006), the National High Technology Research and Development Program of China (Grant No.

In this paper, we present a magnetocardiogram (MCG) simulation study using the boundary element method (BEM) and based on the virtual heart model and the realistic human volume conductor model. The different contributions of cardiac equivalent source models and volume conductor models to the MCG are deeply and comprehensively investigated. The single dipole source model, the multiple dipoles source model and the equivalent double layer (EDL) source model are analysed and compared with the cardiac equivalent source models. Meanwhile, the effect of the volume conductor model on the MCG combined with these cardiac equivalent sources is investigated. The simulation results demonstrate that the cardiac electrophysiological information will be partly missed when only the single dipole source is taken, while the EDL source is a good option for MCG simulation and the effect of the volume conductor is smallest for the EDL source. Therefore, the EDL source is suitable for the study of MCG forward and inverse problems, and more attention should be paid to it in future MCG studies.

Sensitivity of beamformer source analysis to deficiencies in forward modeling

Beamforming approaches have recently been developed for the field of electroencephalography (EEG) and magnetoencephalography (MEG) source analysis and opened up new applications within various fields of neuroscience. While the number of beamformer applications thus increases fast-paced, fundamental methodological considerations, especially the dependence of beamformer performance on leadfield accuracy, is still quite unclear. In this article, we present a systematic study on the influence of improper volume conductor modeling on the source reconstruction performance of an EEG-data based synthetic aperture magnetometry (SAM) beamforming approach. A finite element model of a human head is derived from multimodal MR images and serves as a realistic volume conductor model. By means of a theoretical analysis followed by a series of computer simulations insight is gained into beamformer performance with respect to reconstruction errors in peak location, peak amplitude, and peak width resulting from geometry and anisotropy volume conductor misspecifications, sensor noise, and insufficient sensor coverage. We conclude that depending on source position, sensor coverage, and accuracy of the volume conductor model, localization errors up to several centimeters must be expected. As we could show that the beamformer tries to find the best fitting leadfield (least squares) with respect to its scanning space, this result can be generalized to other localization methods. More specific, amplitude, and width of the beamformer peaks significantly depend on the interaction between noise and accuracy of the volume conductor model. The beamformer can strongly profit from a high signal-to-noise ratio, but this requires a sufficiently realistic volume conductor model.

Boundary Element Discretization in Realistic Volume Conductor Modeling in Fetal Magnetoencephalography

Event Abstract Back to Event Boundary Element Discretization in Realistic Volume Conductor Modeling in Fetal Magnetoencephalography Jens Haueisen1*, Roland Eichardt2, Uwe Schneider3 and Ekkehard Schleussner3 1 Technical University Ilmenau, Germany 2 University Hospital Jena, Germany 3 Friedrich Schiller University Jena, University Hospital, Dept. of Obstetrics,, Germany Source reconstruction in fetal Magnetoencephalography (fMEG) has a high potential to become a valuable tool for both basic and clinical research in developmental neurology. However, volume conductor modeling, which is an important prerequisite for source reconstruction, has not been sufficiently addressed so far. In this paper, we investigate the influence of the boundary element discretization on the simulated fMEG. We construct realistic volume conductor models based on 3D ultrasound images. Two different models are considered: a three compartment model including the fetal head, the vernix caseosa, and the maternal abdomen and a three compartment model including the entire fetus, the vernix caseosa, and the maternal abdomen. The boundary element discretization is varied between 20 mm and 12 mm triangle side length for the maternal abdomen, between 7 mm and 2 mm for the vernix caseosa, and between 7 mm and 2 mm for the fetus or the fetal head. The computed magnetic fields caused by dipolar sources positioned in the different models with different discretizations are compared with the help of the magnitude factor and the correlation coefficient. In line with our earlier investigation we find high differences in the computed amplitudes (minimum MAG of 0.47) between the two models for all BEM discretizations, while at the same time the correlation coefficients are above 0.95. This indicates that the entire fetus needs to be considered in the modeling approach. Comparing the different BEM discretizations, we obtain that the largest difference between the coarsest model (20, 7, 7 mm) and the finest model (12, 2, 2 mm) is 26 % in magnitude and the correlation is 0.97. Magnitude differences of less than 6 % and correlation coefficients higher than 0.99 are obtained for a BEM discretization of 16 mm for the abdomen, 5 mm for the vernix caseosa and the fetus. Our simulations provide a first guideline for the construction of boundary element models in fMEG source reconstruction procedures. Conference: Biomag 2010 - 17th International Conference on Biomagnetism , Dubrovnik, Croatia, 28 Mar - 1 Apr, 2010. Presentation Type: Oral Presentation Topic: Fetal and neonatal biomagnetism Citation: Haueisen J, Eichardt R, Schneider U and Schleussner E (2010). Boundary Element Discretization in Realistic Volume Conductor Modeling in Fetal Magnetoencephalography. Front. Neurosci. Conference Abstract: Biomag 2010 - 17th International Conference on Biomagnetism . doi: 10.3389/conf.fnins.2010.06.00092 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Mar 2010; Published Online: 23 Mar 2010. * Correspondence: Jens Haueisen, Technical University Ilmenau, Ilmenau, Germany, jens.haueisen@tu-ilmenau.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jens Haueisen Roland Eichardt Uwe Schneider Ekkehard Schleussner Google Jens Haueisen Roland Eichardt Uwe Schneider Ekkehard Schleussner Google Scholar Jens Haueisen Roland Eichardt Uwe Schneider Ekkehard Schleussner PubMed Jens Haueisen Roland Eichardt Uwe Schneider Ekkehard Schleussner Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Anisotropic WM conductivity reconstruction based on diffusion tensor magnetic resonance imaging: a simulation study

The present study aims to estimate the in vivo anisotropic conductivities of the White Matter (WM) tissues by means of Magnetic Resonance Electrical Impedance Tomography (MREIT) technique. The realistic anisotropic volume conductor model with different conductivity properties (scalp, skull, CSF, gray matter and WM) is constructed based on the Diffusion Tensor Magnetic Resonance Imaging (DT- MRI) from a healthy human subject. The Radius Basic Function (RBF)-MREIT algorithm of using only one magnetic flux density component was applied to evaluate the eigenvalues of the anisotropic WM with target values set according to the DT-MRI data based on the Wolter’s model, which is more physiologically reliable. The numerical simulations study performed on the five-layer realistic human head model showed that the conductivity reconstruction method had higher accuracy and better robustness against noise. The pilot research was used to judge the feasibility, meaningfulness and reliability of the MREIT applied on the electrical impedance tomography of the complicated human head tissues including anisotropic characteristics.

Characterization of gastric electrical activity using magnetic field measurements: a simulation study.

Gastric disorders are often associated with abnormal propagation of gastric electrical activity (GEA). The identification of clinically relevant parameters of GEA using noninvasive measures would therefore be highly beneficial for clinical diagnosis. While magnetogastrograms (MGG) are known to provide a noninvasive representation of GEA, standard methods for their analysis are limited. It has previously been shown in simplistic conditions that the surface current density (SCD) calculated from multichannel MGG measurements provides an estimate of the gastric source location and propagation velocity. We examine the accuracy of this technique using more realistic source models and an anatomically realistic volume conductor model. The results showed that the SCD method was able to resolve the GEA parameters more reliably when the dipole source was located within 100 mm of the sensor. Therefore, the theoretical accuracy of SCD method would be relatively diminished for patients with a larger body habitus, and particularly in those patients with significant truncal obesity. However, many patients with gastric motility disorders are relatively thin due to food intolerance, meaning that the majority of the population of gastric motility patients could benefit from the methods developed here. Large errors resulted when the source was located deep within the body due to the distorting effects of the secondary sources on the magnetic fields. Larger errors also resulted when the dipole was oriented normal to the sensor plane. This was believed to be due to the relatively small contribution of the dipole source when compared to the field produced by the volume conductor. The use of three orthogonal magnetic field components rather than just one component to calculate the SCD yielded marginally more accurate results when using a realistic dipole source. However, this slight increase in accuracy may not warrant the use of more complex vector channels in future superconducting quantum interference device designs. When multiple slow waves were present in the stomach, the SCD map contained only one maximum point corresponding to the more dominant source located in the distal stomach. Parameters corresponding to the slow wave in the proximal stomach were obtained once the dominant slow terminated at the antrum. Additional validation studies are warranted to address the utility of the SCD method to resolve parameters related to gastric slow waves in a clinical setting.

Imaging functional brain connectivity patterns from high‐resolution EEG and fMRI via graph theory

We describe a set of computational tools able to estimate cortical activity and connectivity from high-resolution EEG and fMRI recordings in humans. These methods comprise the estimation of cortical activity using realistic geometry head volume conductor models and distributed cortical source models, followed by the evaluation of cortical connectivity between regions of interest coincident with the Brodmann areas via the use of Partial Directed Coherence. Connectivity patterns estimated on the cortical surface in different frequency bands are then imaged and interpreted with measures based on graph theory. These computational tools were applied on a set of EEG and fMRI data from a Stroop task to demonstrate the potential of the proposed approach. The present findings suggest that the methodology is able to identify differences in functional connectivity patterns elicited by different experimental tasks or conditions.

Anodal vs cathodal stimulation of motor cortex: A modeling study

ObjectiveTo explore the effects of electrical stimulation performed by an anode, a cathode or a bipole positioned over the motor cortex for chronic pain management. MethodsA realistic 3D volume conductor model of the human precentral gyrus (motor cortex) was used to calculate the stimulus-induced electrical field. The subsequent response of neural elements in the precentral gyrus and in the anterior wall and lip of the central sulcus was simulated using compartmental neuron models including the axon, soma and dendritic trunk. ResultsWhile neural elements perpendicular to the electrode surface are preferentially excited by anodal stimulation, cathodal stimulation excites those with a direction component parallel to its surface. When stimulating bipolarly, the excitation of neural elements parallel to the bipole axis is additionally facilitated. The polarity of the contact over the precentral gyrus determines the predominant response. Inclusion of the soma-dendritic model generally reduces the excitation threshold as compared to simple axon model. ConclusionsElectrode polarity and electrode position over the precentral gyrus and central sulcus have a large and distinct influence on the response of cortical neural elements to stimuli. SignificanceModeling studies like this can help to identify the effects of electrical stimulation on cortical neural tissue, elucidate mechanisms of action and ultimately to optimize the therapy.

Volume Conduction in an Anatomically Based Surface EMG Model

A finite-element model to simulate surface electromyography (EMG) in a realistic human upper arm is presented. The model is used to explore the effect of limb geometry on surface-detected muscle fiber action potentials. The model was based on magnetic resonance images of the subject's upper arm and includes both resistive and capacitive material properties. To validate the model geometry, experimental and simulated potentials were compared at different electrode sites during the application of a subthreshold sinusoidal current source to the skin surface. Of the material properties examined, the closest approximation to the experimental data yielded a mean root-mean-square (rms) error of the normalized surface potential of 18% or 27%, depending on the site of the applied source. Surface-detected action potentials simulated using the realistic volume conductor model and an idealized cylindrical model based on the same limb geometry were then compared. Variation in the simulated limb geometry had a considerable effect on action potential shape. However, the rate of decay of the action potential amplitude with increasing distance from the fiber was similar in both models. Inclusion of capacitive material properties resulted in temporal low-pass filtering of the surface action potentials. This effect was most pronounced in the end-effect components of action potentials detected at locations far from the active fiber. It is concluded that accurate modeling of the limb geometry, asymmetry, tissue capacitance and fiber curvature is important when the specific action potential shapes are of interest. However, if the objective is to examine more qualitative features of the surface EMG signal, then an idealized volume conductor model with appropriate tissue thicknesses provides a close approximation.