995 research outputs found
Influence of skull inhomogeneities on EEG source localization
We investigated the influence of using simplified models of the skull on electroencephalogram (EEG) source localization. An accurately segmented skull from computed tomography (CT) images, including spongy and compact bones as well as some airâfilled cavities, was used as a reference model. The simplified models approximated the skull as a homogeneous compartment with: (1) isotropic, and (2) anisotropic conductivity. The results showed that these approximations could lead to errors of more than 2 cm in dipole estimation. We recommend the use of anisotropy but considering a different ratio for each region of the skull, according to the amount of spongy bone
Incorporation of anisotropic conductivities in EEG source analysis
The electroencephalogram (EEG) is a measurement of brain activity over a period of time by placing electrodes at the scalp (surface EEG) or in the brain (depth EEG) and is used extensively in the clinical practice. In the past 20 years, EEG source analysis has been increasingly used as a tool in the diagnosis of neurological disorders (like epilepsy) and in the research of brain functionality. EEG source analysis estimates the origin of brain activity given the electrode potentials measured at the scalp. This involves solving an inverse problem where a forward solution, which depends on the source parameters, is fitted to the given set of electrode potentials. The forward solution are the electrode potentials caused by a source in a given head model. The head model is dependent on the geometry and the conductivity. Often an isotropic conductivity (i.e. the conductivity is equal in all directions) is used, although the skull and white matter have an anisotropic conductivity (i.e. the conductivity can differ depending on the direction the current flows). In this dissertation a way to incorporate the anisotropic conductivities is presented and the effect of not incorporating these anisotropic conductivities is investigated.
Spherical head models are simple head models where an analytical solution to the forward problem exists. A small simulation study in a 5 shell spherical head model was performed to investigate the estimation error due to neglecting the anisotropic properties of skull and white matter. The results show that the errors in the dipole location can be larger than 15 mm, which is unacceptable for an accurate dipole estimation in the clinical practice. Therefore, anisotropic conductivities have to be included in the head model. However, these spherical head models are not representative for the human head. Realistic head models are usually made from magnetic resonance scans through segmentation and are a better approximation to the geometry of the human head. To solve the forward problem in these head models numerical methods are needed. In this dissertation we proposed a finite difference technique that can incorporate anisotropic conductivities. Moreover, by using the reciprocity theorem the forward calculation time during an dipole source estimation procedure can be significantly reduced. By comparing the analytical solution for the dipole estimation problem with the one using the numerical method, the anisotropic finite difference with reciprocity method (AFDRM) is validated. Therefore, a cubic grid is made on the 5 shell spherical head model. The electrode potentials are obtained in the spherical head model with anisotropic conductivities by solving the forward problem using the analytical solution. Using these electrode potentials the inverse problem was solved in the spherical head model using the AFDRM. In this way we can determine the location error due to using the numerical technique.
We found that the incorporation of anisotropic conductivities results in a larger location error when the head models are fully isotropical conducting. Furthermore, the location error due to the numerical technique is smaller if the cubic grid is made finer. To minimize the errors due to the numerical technique, the cubic grid should be smaller than or equal to 1 mm.
Once the numerical technique is validated, a realistic head model can now be constructed. As a cubic grid should be used of at most 1 mm, the use of segmented T1 magnetic resonance images is best suited the construction. The anisotropic conductivities of skull and white matter are added as follows: The anisotropic conductivity of the skull is derived by calculating the normal and tangential direction to the skull at each voxel. The conductivity in the tangential direction was set 10 times larger than the normal direction. The conductivity of the white matter was derived using diffusion weighted magnetic resonance imaging (DW-MRI), a technique that measures the diffusion of water in several directions. As diffusion is larger along the nerve fibers, it is assumed that the conductivity along the nerve fibers is larger than the perpendicular directions to the nerve bundle. From the diffusion along each direction, the conductivity can be derived using two approaches. A simplified approach takes the direction with the largest diffusion and sets the conductivity along that direction 9 times larger than the orthogonal direction. However, by calculating the fractional anisotropy, a well-known measure indicating the degree of anisotropy, we can appreciate that a fractional anisotropy of 0.8715 is an overestimation. In reality, the fractional anisotorpy is mostly smaller and variable throughout the white matter. A realistic approach was therefore presented, which states that the conductivity tensor is a scaling of the diffusion tensor. The volume constraint is used to determine the scaling factor. A comparison between the realistic approach and the simplified approach was made. The results showed that the location error was on average 4.0 mm with a maximum of 10 mm. The orientation error was found that the orientation could range up to 60 degrees. The large orientation error was located at regions where the anisotropic ratio was low using the realistic approach but was 9 using the simplified approach.
Furthermore, as the DW-MRI can also be used to measure the anisotropic diffusion in a gray matter voxel, we can derive a conductivity tensor. After investigating the errors due to neglecting these anisotropic conductivities of the gray matter, we found that the location error was very small (average dipole location error: 2.8 mm). The orientation error was ranged up to 40 degrees, although the mean was 5.0 degrees. The large errors were mostly found at the regions that had a high anisotropic ratio in the anisotropic conducting gray matter. Mostly these effects were due to missegmentation or to partial volume effects near the boundary interfaces of the gray and white matter compartment. After the incorporation of the anisotropic conductivities in the realistic head model, simulation studies can be performed to investigate the dipole estimation errors when these anisotropic conductivities of the skull and brain tissues are not taken into account. This can be done by comparing the solution to the dipole estimation problem in a head model with anisotropic conductivities with the one in a head model, where all compartments are isotropic conducting. This way we determine the error when a simplified head model is used instead of a more realistic one. When the anisotropic conductivity of both the skull and white matter or the skull only was neglected, it was found that the location
error between the original and the estimated dipole was on average, 10 mm (maximum: 25 mm). When the anisotropic conductivity of the brain tissue was neglected, the location error was much smaller (an average location error of 1.1 mm). It was found that the anisotropy of the skull acts as an extra shielding of the electrical activity as opposed to an isotropic skull. Moreover, we saw that if the dipole is close to a highly anisotropic region, the potential field is changed reasonable in the near vicinity of the location of the dipole. In reality EEG contains noise contributions. These noise contribution will interact with the systematical error by neglecting anisotropic conductivities. The question we wanted to solve was âIs it worthwhile to incorporate anisotropic conductivities, even if the EEG contains noise?â and âHow much noise should the EEG contain so that incorporating anisotropic conductivities improves the accuracy of EEG source analysis?â. When considering the anisotropic conductivities of the skull and brain tissues and the skull only, the location error due to the noise and neglecting the anisotropic conductivities is larger then the location error due to noise only. When only neglecting the anisotropic conductivities of the brain tissues only, the location error due to noise is similar to the location error due to noise and neglecting the anisotropic conductivities.
When more advanced MR techniques can be used a better model to construct the anisotropic conductivities of the soft brain tissues can be used, which could result in larger errors even in the presence of noise. However, this is subject to further investigation. This suggests that the anisotropic conductivities of the skull should be incorporated. The technique presented in the dissertation can be used to epileptic patients in the presurgical evaluation. In this procedure patients are evaluated by means of medical investigations to determine the cause of the epileptic seizures. Afterwards, a surgical procedure can be performed to render the patient seizure free. A data set from a patiënt was obtained from a database of the Reference Center of Refractory Epilepsy of the Department of Neurology and the Department of Radiology of the Ghent University Hospital (Ghent, Belgium). The patient was monitored with a video/EEG monitoring with scalp and with implanted depth electrodes. An MR image was taken from the patient with the implanted depth electrodes, therefore, we could pinpoint the hippocampus as the onset zone of the epileptic seizures. The patient underwent a resective surgery removing the hippocampus, which rendered the patient seizure free. As DW-MRI images were not available, the head model constructed in chapter 4 and 5 was used. A neuroradiologist aligned the hippocampus in the MR image from which the head model was constructed. A spike was picked from a dataset and was used to estimate the source in a head model where all compartments were isotropic conducting, on one hand, and where the skull and brain tissues were anisotropic conducting, on the other. It was found that using the anisotropic head model, the source was estimated closer to the segmented hippocampus than the isotropic head model. This example shows the possibilities of this technique and allows us to apply it in the clinical practice. Moreover, a thorough validation of the technique has yet to be performed. There is a lot of discussion in the clinical community whether the spikes and epileptical seizures originate from the same origin in the brain. This question can be solved by applying our technique in patient studies
Anisotropic EEG/MEG volume conductor modeling based on Diffusion Tensor Imaging
Die vorliegende Arbeit befasst sich mit der Volumenleitermodellierung auf
Basis der Finiten Elemente fĂŒr EEG/MEG Untersuchungen unter Einbeziehung
von Anistropieinformation, die mit Hilfe der
Magnetresonanzdiffusionstensorbildgebung (MR-DTI) gewonnen wurde. Im ersten
Teil der Arbeit wurde der Einfluss unvollstÀndig bestimmter
Wichtungsparamter (b-Matrix) auf die zu rekonstruierenden
Diffusionstensoren untersucht. Die UnvollstÀndigkeit bezieht sich dabei auf
die Tatsache, dass im Allgemeinen nur die starken Diffusionsgradienten zur
Berechnung der b-Matrix herangezogen werden. Es wurde gezeigt, dass
besonders bei Aufnahmen mit hoher rÀumlicher Auflösung der Anteil der
Bildgradienten an der b-Matrix nicht mehr vernachlÀssigbar ist. Weiterhin
wurde gezeigt, wie man die b-Matrizen korrekt analytisch bestimmt und damit
einen systematischen Fehler vermeidet. FĂŒr den Fall, dass nicht ausreichend
Informationen zur VerfĂŒgung stehen um die analytische Bestimmung
durchzufĂŒhren, wurde eine Lösung vorgeschlagen, die es mit Hilfe von
Phantommessungen ermöglicht eine parametrisierte b-Matrix zu bestimmen. Der
zweite Teil widmet sich der Erstellung hochaufgelöster realistischer
Volumenleitermodelle detailliert beschrieben. Besonders die Transformation
der Diffusionstensordaten in LeitfÀhigkeitstensoren. Zudem wurde eine
Vorgehensweise beschrieben, die es erlaubt, einen T1-gewichteten
MR-Datensatz vollautomatisch in fĂŒnf verschiedene Gewebesegmente (weiches
Gewebe, graue und weiĂe Substanz, CSF und SchĂ€delknochen) zu unterteilen.
Der dritte Teil der Arbeit befasst sich mit dem Einfluss der anisotropen
LeitfĂ€higkeit in der weiĂen Hirnsubstanz auf EEG und MEG unter Verwendung
eines Tier- sowie eines Humanmodells. Um den Einfluss der verschiedenen
Methoden der Transformation von DTI Daten in LeitfÀhigkeitsdaten zu
untersuchen, wurden verschiedenen Modelle sowohl mit gemessener als auch
mit kĂŒnstlicher Anisotropie erstellt. In der Tiermodellstudie wurden EEG
und in der Humanmodellstudie EEG und MEG Simulationen sowohl mit den
anisotropen Modellen als auch mit einem isotropen Modell durchgefĂŒhrt und
miteinander verglichen. Dabei wurde gefunden, dass sowohl der
topographische Fehler (RDM) als auch der Magnitudenfehler stark durch das
Einbeziehen von Anisotropieinformationen beeinflusst wird. Es wurde auch
gezeigt, dass sowohl die Position als auch die Orientierung einer
dipolaren Quelle in Bezug auf das anisotrope Segment einen groĂen Effekt
auf die untersuchten FehlermaĂe hat.In this work anisotropic electric tissue properties determined by
means of
diffusion tensor imaging were modeled into high resolution finite element
volume conductors. In first part of the work the influence of not
considering imaging gradient in the calculation of the b-matrices on the
correct determination of diffusion tensor data is shown and it was found
that especially with high resolution imaging protocols the contributions of
the imaging gradients is not negligible. It was also shown how correct
b-matrices considering all applied gradients can be calculated correctly.
For the case that information about the sequence are missing an
experimental approach of determining a parameterized b-matrix using phantom
measurements is proposed. In the second part the procedure of generating
anisotropic volume conductor models is regarded. The main focus of this
part was to facilitate the derivation of anisotropy information from DTI
measurements and the inclusion of this information into an anisotropic
volume conductor. It was shown, that it is possible to generate a
sophisticated high resolution anisotropic model without any manual steps
into five different tissue layers. The third part studied the influence of
anisotropic white matter employing an animal as well as a human model. To
compare the different ways of converting the anisotropy information from
DTI into conductivity information, different models were investigated,
having artificial as well as measured anisotropy. In the animal study the
EEG and in the human study the EEG and MEG forward solution was studies
using the anisotropic models and compared to the solution derived using an
isotropic model. It was found that both, the topography error (RDM) as well
as the magnitude error (MAG), are significantly affected if anisotropy is
considered in the volume conductor. It was also shown, that the position as
well as the orientation of the dipole with respect to white matter has a
large effect on the amount of the error quantities. Finally, it is claimed
that if one uses high resolution volume conductor models for EEG/MEG
studies, the anisotropy has to be considered, since the average error of
neglecting anisotropy is larger than the accuracy which can be achieved
using such models
Experimental validation of the influence of white matter anisotropy on the intracranial EEG forward solution
Forward solutions with different levels of complexity are employed for localization of current generators, which are responsible for the electric and magnetic fields measured from the human brain. The influence of brain anisotropy on the forward solution is poorly understood. The goal of this study is to validate an anisotropic model for the intracranial electric forward solution by comparing with the directly measured âgold standardâ. Dipolar sources are created at known locations in the brain and intracranial electroencephalogram (EEG) is recorded simultaneously. Isotropic models with increasing level of complexity are generated along with anisotropic models based on Diffusion tensor imaging (DTI). A Finite Element Method based forward solution is calculated and validated using the measured data. Major findings are (1) An anisotropic model with a linear scaling between the eigenvalues of the electrical conductivity tensor and water self-diffusion tensor in brain tissue is validated. The greatest improvement was obtained when the stimulation site is close to a region of high anisotropy. The model with a global anisotropic ratio of 10:1 between the eigenvalues (parallel: tangential to the fiber direction) has the worst performance of all the anisotropic models. (2) Inclusion of cerebrospinal fluid as well as brain anisotropy in the forward model is necessary for an accurate description of the electric field inside the skull. The results indicate that an anisotropic model based on the DTI can be constructed non-invasively and shows an improved performance when compared to the isotropic models for the calculation of the intracranial EEG forward solution
Influence of anisotropic conductivity of the white matter tissue on EEG source reconstruction a FEM simulation study
The aim of this study was to quantify the influence of the inclusion of anisotropic conductivity on EEG source reconstruction. We applied high-resolution finite element modeling and performed forward and inverse simulation with over 4000 single dipoles placed around an anisotropic volume block (with an anisotropic ratio of 1:10) in a rabbit brain. We investigated three different orientation of the dipoles with respect to the anisotropy in the white matter block. We found a weak influence of the anisotropy in the forward simulation on the electric potential. The relative difference measure (RDM) between the potentials simulated with and without taking into account anisotropy was less than 0.009. The changes in magnitude (MAG) ranged from 0.944 to 1.036. Using the potentials of the forward simulation derived with the anisotropic model and performing source reconstruction by employing the isotropic model led to dipole shifts of up to 2 mm, however the mean shift over all dipoles and orientations of 0.05 mm was smaller than the grid size of the FEM model (0.6 mm). However, we found the source strength estimation to be more influenced by the anisotropy (up to 7-times magnified dipole strength)
Influence of local and remote white matter conductivity anisotropy for a thalamic source on EEG/MEG field and return current computation
nverse methods are used to reconstruct current sources in the human brain by means of Electroencephalogra- phy (EEG) and Magnetoencephalography (MEG) measure- ments of event related fields or epileptic seizures. There exists a persistent uncertainty regarding the influence of anisotropy of the white matter compartment on neural source reconstruc- tion. In this paper, we study the sensitivity to anisotropy of the EEG/MEG forward problem for a thalamic source in a high resolution finite element volume conductor. The influence of anisotropy on computed fields will be presented by both high resolution visualization of fields and return current flow and topography and magnitude error measures. We pay particular attention to the influence of local conductivity changes in the neighborhood of the source. The combination of simulation and visualization provides deep insight into the effect of white matter conductivity anisotropy. We found that for both EEG and MEG formulations, the local presence of electrical anisotropy in the tissue surroun- ding the source substantially compromised the forward field computation, and correspondingly, the inverse source recons- truction. The degree of error resulting from the uncompen- sated presence of tissue anisotropy depended strongly on the proximity of the anisotropy to the source; remote anisotropy had a much weaker influence than anisotropic tissue that included the source
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