Dipole Localization Accuracy Research Articles

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Toward early cancer detection using superparamagnetic relaxometry in a SQUID-based ULF-MRI system

To detect cancer at a very early state it is essential to detect a very small quantity of cancerous cells. One very sensitive method relies on targeting the cancer cells using antibody labeled single-core magnetic nanoparticles and detecting the relaxation of the magnetization using instruments based on superconducting quantum interference devices (SQUIDs). However, the localization suffers from inverse-problem issues similar to those found in magnetoencephalography. On the other hand, the same magnetic nanoparticles can also work as contrast agents for magnetic resonance imaging. Through the combination of superparamagnetic relaxometry and ultra-low field magnetic resonance imaging (ULF MRI), in one and the same instrument, the accuracy of the magnetic moment localization can be enhanced and anatomical information can also be obtained. Results on superparamagnetic relaxometry and the dipole localization accuracy in our seven-channel low-Tc SQUID-gradiometer array are reported.

Reduced Conductivity Dependence Method for Increase of Dipole Localization Accuracy in the EEG Inverse Problem

The EEG is a neurological diagnostic tool with high temporal resolution. However, when solving the EEG inverse problem, its localization accuracy is limited because of noise in measurements and available uncertainties of the conductivity value in the forward model evaluations. This paper proposes the reduced conductivity dependence (RCD) method for decreasing the localization error in EEG source analysis by limiting the propagation of the uncertain conductivity values to the solutions of the inverse problem. We redefine the traditional EEG cost function, and in contrast to previous approaches, we introduce a selection procedure of the EEG potentials. The selected potentials are, as low as possible, affected by the uncertainties of the conductivity when solving the inverse problem. We validate the methodology on the widely used three-shell spherical head model with a single electrical dipole and multiple dipoles as source model. The proposed RCD method enhances the source localization accuracy with a factor ranging between 2 and 4, dependent on the dipole location and the noise in measurements.

Advances in Spike Localization with EEG Dipole Modeling

EEG interpretation by visual inspection of waveforms, using the assumption that activity at a given electrode is a representation of only the activity of the cortex immediately beneath it, has been the traditional form of EEG analysis since its inception. The relatively recent advent of digital EEG has allowed more advanced analysis of EEG data and has shown that the simple visual inspection described above is a simplistic form of analysis. This is especially true when one is attempting to localize an epileptogenic focus using EEG spikes or seizure onset data. Spatiotemporal analysis of scalp voltage fields has allowed for improved localization of likely cerebral origins of such waveforms. Equivalent dipole source modeling is one such technique and, although not perfect, provides improved characterization of spike and seizure sources as compared to previous methods when properly interpreted. The use of other modern techniques, such as 3D MRI reconstructions and realistic head models, can further improve accuracy of dipole localization and allow for the synthesis of EEG and imaging data, which may be invaluable, especially in cases of pre-surgical epilepsy evaluation.

Corpus callosotomy: A palliative therapeutic technique may help identify resectable epileptogenic foci

Corpus callosotomy has a long history as a palliative treatment for intractable epilepsy. Identification of a single epileptogenic zone is critical to performing successful resective surgery. We describe three patients in which corpus callosotomy allowed recognition of unapparent seizure foci, leading to subsequent successful resection. We retrospectively reviewed our epilepsy surgery database from 2003 to 2005 for children who had a prior callosotomy and were candidates for focal resection. All underwent magnetic resonance imaging and scalp video electroencephalograph monitoring, and two had magnetoencephalography, electrocorticography and/or intracranial video electroencephalograph monitoring. The children were 8 and 9 years old, and seizure onset varied from early infancy to early childhood. One child had a history of head trauma preceding seizure onset, one had a large intracerebral infarct and dysplastic cortex in the contralateral frontal lobe, and the other had an anterior temporal lobe resection without improvement in seizure frequency. After medical management failed, callosotomy was performed with the expectation of decreasing the seizure types affecting both hemispheres. Following transection of the callosal fibers, a single focus was recognized and resected, with resultant dramatic improvement in seizure control. In medically refractory epilepsy, where rapid secondary bisynchrony is suspected but the electroencephalograph is non-localizing, callosotomy should be considered as a means of treating generalized seizure types, but may also assist in identifying potentially operable seizure foci. Study limitations include its retrospective nature and cohort size. The findings, however, suggest the need for prospective, systematic, well-controlled studies of the use of corpus callostomy in this intractable patient population.

Investigations of dipole localization accuracy in MEG using the bootstrap

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.

Localization of realistic cortical activity in MEG using current multipoles

We present a novel approach to MEG source estimation based on a regularized first-order multipole solution. The Gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity. We compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex. The results, obtained with a realistic sensor configuration, a spherical head model, and given in terms of field and localization error, depict the performance of the dipolar and multipolar models as a function of variable source surface area (50–500 mm 2), noise conditions (20, 10, and 5 dB SNR), source orientation (0–90°), and source depth (3–11 cm). We show that as the sources increase in size, they become less accurately modeled as current dipoles. The regularized multipole systematically outperforms the single dipole model, increasingly so as the spatial extent of the sources increases. In addition, our simulations demonstrate that as the orientation of the sources becomes more radial, dipole localization accuracy decreases substantially, while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi-radial source configurations. Furthermore, our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole. These results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.

Effect of EEG electrode density on dipole localization accuracy using two realistically shaped skull resistivity models.

The effect of number of EEG electrodes on the dipole localization was studied by comparing the results obtained using the 10-20 and 10-10 electrode systems. Two anatomically detailed models with resistivity values of 177.6 omega m and 67.0 omega m for the skull were applied. Simulated potential values generated by current dipoles were applied to different combinations of the volume conductors and electrode systems. High and low resistivity models differed slightly in favour of the lower skull resistivity model when dipole localization was based on noiseless data. The localization errors were approximately three times larger using low resistivity model for generating the potentials, but applying high resistivity model for the inverse solution. The difference between the two electrode systems was minor in favour of the 10-10 electrode system when simulated, noiseless potentials were used. In the presence of noise the dipole localization algorithm operated more accurately using the denser electrode system. In conclusion, increasing the number of recording electrodes seems to improve the localization accuracy in the presence of noise. The absolute skull resistivity value also affects the accuracy, but using an incorrect value in modelling calculations seems to be the most serious source of error.

The effect of artifact rejection by signal-space projection on source localization accuracy in MEG measurements

The consequences of artifact suppression by means of signal-space projection on dipole localization accuracy for magnetoencephalography measurements are studied. Approximate analytical formulas, equivalent to the Cramer-Rao bound, are presented and verified by Monte Carlo simulations which relate the increase of localization error for individual coordinates to the similarity of the artifact field and respective (contravariant) quadrupole fields obtained by differentiating the dipole field with respect to its origin. The expressions simplify significantly for dipoles placed below the center of the measuring system giving rise to highly symmetric field patterns. Formulas are presented both for single- and for multiple-artifact rejection. As illustrative examples artifact fields are constructed which a) lead to highly decreasing signal-to-noise ratio and goodness-of-fit (GOF), while the localization error is unaffected for all coordinates and b) lead to an increase of localization error while the SNR and the GOF stays constant. Finally, the rich structure of localization error increase is demonstrated for a class of artifact fields originating from artifact current dipoles.

A Study of Dipole Localization Accuracy for MEG and EEG using a Human Skull Phantom

Objective: To investigate the accuracy of forward and inverse techniques for EEG and MEG dipole localization. Design and Methods: A human skull phantom was constructed with brain, skull and scalp layers and realistic relative conductivities. Thirty two independent current dipoles were distributed within the `brain' region and EEG and MEG data collected separately for each dipole. The true dipole locations and orientations and the morphology of the brain, skull and scalp layers were extracted from X-ray CT data. The location of each dipole was estimated from the EEG and MEG data using the R-MUSIC inverse method and forward models based on spherical and realistic head geometries. Additional computer simulations were performed to investigate the factors affecting localization accuracy. Results: Localization errors using the relatively simpler locally fitted sphere approach are only slightly greater than those using a BEM approach. The average localization error over the 32 dipoles was 7–8 mm for EEG and 3 mm for MEG. Conclusion: The superior performance of MEG over EEG appears to be because the latter is more sensitive to errors in the forward model arising from simplifying assumptions concerning the conductivity of the skull, scalp and brain.

A study of dipole localization accuracy for MEG and EEG using a human skull phantom

Objective: To investigate the accuracy of forward and inverse techniques for EEG and MEG dipole localization. Design and Methods: A human skull phantom was constructed with brain, skull and scalp layers and realistic relative conductivities. Thirty two independent current dipoles were distributed within the `brain' region and EEG and MEG data collected separately for each dipole. The true dipole locations and orientations and the morphology of the brain, skull and scalp layers were extracted from X-ray CT data. The location of each dipole was estimated from the EEG and MEG data using the R-MUSIC inverse method and forward models based on spherical and realistic head geometries. Additional computer simulations were performed to investigate the factors affecting localization accuracy. Results: Localization errors using the relatively simpler locally fitted sphere approach are only slightly greater than those using a BEM approach. The average localization error over the 32 dipoles was 7–8 mm for EEG and 3 mm for MEG. Conclusion: The superior performance of MEG over EEG appears to be because the latter is more sensitive to errors in the forward model arising from simplifying assumptions concerning the conductivity of the skull, scalp and brain.

Improved dipole localization using local mesh refinement of realistic head geometries: an EEG simulation study

A systematic evaluation of dipole localization accuracy using the boundary element method is presented. EEG simulations are carried out with dipoles located in the right parietal and temporal regions of the head. Uniformly meshed and locally refined head models are considered in both spherical and realistic geometries. An initial study determines the influence upon the localization accuracy of the dipole depth below the brain surface, of its orientation (radial and tangential), and of the global and local mesh densities. Simulated potential data are computed analytically in the spherical case, and numerically using a very fine (locally refined) model in the realistic case. Results in both geometries show that in order to get localization errors of about 2–4 mm, uniformly meshed models may be used for dipoles located at depths greater than 20 mm, whereas locally refined models should be used for shallower dipoles. Two other studies show how the localization accuracy depends upon the size of the local refinement area and upon the number of electrodes (19, 32, 63). Results show that a large number of electrodes brings significant improvements, especially for shallow and tangential dipoles.

Accuracy of dipole localization with a spherical homogeneous model.

Dipole localization methods (DLM's) with a spherical, homogeneous, isotropic model were applied to the problem of locating and characterizing simulated dipole sources of the brainstem acoustic evoked response (BAER) in cats. Dipole source parameters considered were chosen to be consistent with measurements of gross potential within the brainstem during the BAER. The steepest ascent method was used to solve the least-squares minimization problem on a set of noiseperturbed surface voltages to obtain parameters of a single assumed dipole source. The magnitudes of errors in dipole postion and in dipole moment vectors were calculated for two surface voltage location sets, two assumed dipole source locations, and a range of surface signal-to-noise ratios. An approximate analytic approach to the simulation results attributed DLM errors to an apparent "noise dipole" calculated as the dipole term in the multipole expansion of the added surface noise. The standard deviation of the "noise dipole" magnitude was directly proportional to the standard deviation of surface noise voltage and inversely proportional to the root of the number of surface voltages. This analytic result was in general agreement with the mean of the dipole moment parameter errors in the simulation study. It was found that recalculation of the surface voltage set from the solution dipole of the simulation problem or from the "noise dipole" of the analytic treatment resulted in an improvement of signal-to-noise ratio at the surface.