Cortical Potential Distribution Research Articles

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

Back-Projection Cortical Potential Imaging: Theory and Results.

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases. In this paper, a new cortical potential imaging (CPI) method is presented. The new method gives an estimation of the electrical activity on the cortex surface and thus removes the "smearing effect" caused by the skull. The scalp potentials are back-projected CPI (BP-CPI) onto the cortex surface by building a well-posed problem to the Laplace equation that is solved by means of the finite elements method on a realistic head model. A unique solution to the CPI problem is obtained by introducing a cortical normal current estimation technique. The technique is based on the same mechanism used in the well-known surface Laplacian calculation, followed by a scalp-cortex back-projection routine. The BP-CPI passed four stages of validation, including validation on spherical and realistic head models, probabilistic analysis (Monte Carlo simulation), and noise sensitivity tests. In addition, the BP-CPI was compared with the minimum norm estimate CPI approach and found superior for multi-source cortical potential distributions with very good estimation results (CC >0.97) on a realistic head model in the regions of interest, for two representative cases. The BP-CPI can be easily incorporated in different monitoring tools and help researchers by maintaining an accurate estimation for the cortical potential of ongoing or event-related potentials in order to have better neurological inferences from the EEG.

Electrical low‐frequency stimulation induces central neuroplastic changes of pain processing in man

Electrical low-frequency stimulation (LFS) inhibits pain perception and nociceptive processing as shown by psychophysical and electrophysiological means (long-term depression, LTD). Information regarding central mechanisms involved in LTD induction and maintenance are still missing. This study hypothesizes that electrical LFS induces changes in activation pattern of pain-related brain areas. Thirty-two electrophysiological and psychophysical experiments were performed in 16 healthy volunteers. Painful electrical test stimulation (0.125 Hz, 60 pulses) and conditioning LFS (1 Hz, 1200 pulses) were applied by a concentric electrode to the right hand. Test stimulation series were performed before (Pre) and after LFS (Post) or no stimulation period (Control). Volunteers rated pain perception according to a verbal rating scale (0-100). Somatosensory evoked cortical potentials were recorded with 64-channel electroencephalography. Individual dipole source modeling using CURRY software (Compumedics, Hamburg, Germany) yielded information about dipole location and strength. The strongest decrease in LFS-induced pain perception was shown after LFS (p < 0.01). Topographic distribution of cortical potentials revealed reproducible negative (N1, N2) and positive (P2) components. Dipole magnitude analysis showed a significant difference between Post LFS and Post Control for P2 (p < 0.01). P2 dipole location analysis yielded a significant posterior (p < 0.05) shift following LTD induction. Thus, data reveal central changes of pain processing after LTD induction. These experiments may help judging the potency of LTD as model for electrostimulation in future analgesic therapy.

Cortical Potential Imaging of Somatosensory Evoked Potentials by Means of the Boundary Element Method in Pediatric Epilepsy Patients

The aim of the present study was to assess the feasibility of identifying the primary hand sensory area and central sulcus in pediatric patients using the cortical potential imaging (CPI) method from the scalp recorded somatosensory evoked potentials (SEPs). The CPI method was used to reconstruct the cortical potential distribution from the scalp potentials with the boundary element (3-layer: scalp, skull and brain) head model based on MR images of individual subjects. The cortical potentials estimated from the pre-operative scalp SEPs of four pediatric patients, were compared with the post-op subdural SEP recordings made in the same subjects. Estimated and directly recorded cortical SEP maps showed comparable spatial patterns on the cortical surface in four patients (spatial correlation coefficient >0.7 in the SEP spikes). For two of four patients, the estimated waveforms correlated significantly to the waveforms obtained by direct cortical recordings. The present results demonstrated the feasibility of the cortical potential imaging approach in noninvasive imaging spatial distribution and temporal waveforms of cortical potentials for pediatric patients. These also suggest that the CPI method may provide a promising means of estimating the cortical potential and noninvasive localizing the central sulcus to aid surgical planning for pediatric patients.

Noninvasive cortical imaging of epileptiform activities from interictal spikes in pediatric patients

Improved non-invasive localization of the epileptogenic foci prior to epilepsy surgery would improve surgical outcome in patients with partial seizure disorders. A critical component for the identification of the epileptogenic brain is the analysis of electrophysiological data obtained during ictal activity from prolonged intracranial recordings. The development of a noninvasive means to identify the seizure onset zone (SOZ) would thus play an important role in treating patients with intractable epilepsy. In the present study, we have investigated non-invasive imaging of epileptiform activity in patients with medically intractable epilepsy by means of a cortical potential imaging (CPI) technique. Eight pediatric patients (1M/7F, ages 4–14years) with intractable partial epilepsy were studied. Each patient had multiple (6 to 14) interictal spikes (IIS) subjected to the CPI analysis. Realistic geometry boundary element head models were built using each individual's MRI in order to maximize the imaging precision. CPI analysis was performed on the IISs, and extrema in the estimated CPI images were compared with SOZs as determined from the ictal electrocorticogram (ECoG) recordings, as well as the resected areas in the patients and surgical outcomes. The distances between the maximum cortical activities of the IISs reflected by the estimated cortical potential distributions and the SOZs were determined to quantitatively evaluate the performance of the CPI in localizing the epileptogenic zone. Ictal ECoG recordings revealed that six patients exhibited a single epileptogenic focus while two patients had multiple foci. In each patient, the CPI results revealed an area of activity overlapping with the SOZs as identified by ictal ECoG. The distance from the extreme of the CPI images at the peak of IIS to the nearest intracranial electrode associated with the onset of the ictal activity was evaluated for each patient and the averaged distance was 4.6mm. In the group of patients studied, the CPI imaged epileptogenic foci were within the resected areas. According to the follow-up of the eight patients included, two were seizure free and six had substantial reduction in seizure frequency. These promising results demonstrate the potential for noninvasive localization of the epileptogenic focus from interictal scalp EEG recordings. Confirmation of our results may have a significant impact on the process of presurgical planning in pediatric patients with intractable epilepsy by dramatically reducing or potentially eliminating the use of intracranial recording.

Modeling brain electrical activity by an image-based boundary element method

Electroencephalography (EEG) source localization of brain activity is of high diagnostic value. This work aims to improve the low-spatial-resolution scalp EEG measurement through noninvasive numerical procedures. An image-based boundary element method (BEM) is developed to reconstruct cortical brain potential distribution from the scalp input. The developed BEM circumvents a practical challenge of linking scan images to model-based computation by translating the scan surface tessellation directly into mesh discretization for the BEM. Related issues, such as the numerical regularization of the ill-posed inverse problem, which are crucial to achieving reliable solutions, are discussed. The numerical studies show that the developed BEM can effectively handle the potential reconstruction on a detailed brain surface from blurry scalp potential input, and may become a promising tool to aid clinical diagnosis of brain-related problems.

Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter

BackgroundThe electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull. In this paper, we compute the potential distribution over the closed surface covering the brain (cortex) from the EEG scalp potential. We compare two methods – L-curve and generalised cross validation (GCV) used to obtain the regularisation parameter and also investigate the feasibility in applying such techniques to N170 component of the visually evoked potential (VEP) data.MethodsUsing the image data set of the visible human man (VHM), a finite difference method (FDM) model of the head was constructed. The EEG dataset (256-channel) used was the N170 component of the VEP. A forward transfer matrix relating the cortical potential to the scalp potential was obtained. Using Tikhonov regularisation, the potential distribution over the cortex was obtained.ResultsThe cortical potential distribution for three subjects was solved using both L-curve and GCV method. A total of 18 cortical potential distributions were obtained (3 subjects with three stimuli each – fearful face, neutral face, control objects).ConclusionsThe GCV method is a more robust method compared to L-curve to find the optimal regularisation parameter. Cortical potential imaging is a reliable method to obtain the potential distribution over cortex for VEP data.

Effect of measurement noise and electrode density on the spatial resolution of cortical potential distribution with different resistivity values for the skull

The purpose of the present theoretical study was to examine the spatial resolution of electroencephalography (EEG) by means of the accuracy of the inverse cortical EEG solution. The study focused on effect of the amount of measurement noise and the number of electrodes on the spatial resolution with different resistivity ratios for the scalp, skull and brain. The results show that if the relative skull resistivity is lower than earlier believed, the spatial resolution of different electrode systems is less sensitive to the measurement noise. Furthermore, there is then also greater advantage to be obtained with high-resolution EEG at realistic noise levels.

A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method

In the present study, we have validated the cortical potential imaging (CPI) technique for estimating cortical potentials from scalp EEG using simultaneously recorded electrocorticogram (ECoG) in the presence of strong local inhomogeneity, i.e., Silastic ECoG grid(s). The finite element method (FEM) was used to model the realistic postoperative head volume conductor, which includes the scalp, skull, cerebrospinal fluid (CSF) and brain, as well as the Silastic ECoG grid(s) implanted during the surgical evaluation in epilepsy patients, from the co-registered magnetic resonance (MR) and computer tomography (CT) images. A series of computer simulations were conducted to evaluate the present FEM-based CPI technique and to assess the effect of the Silastic ECoG grid on the scalp EEG forward solutions. The present simulation results show that the Silastic ECoG grid has substantial influence on the scalp potential forward solution due to the distortion of current pathways in the presence of the extremely low conductive materials. On the other hand, its influence on the estimated cortical potential distribution is much less than that on the scalp potential distribution. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the feasibility of estimating the cortical potentials from the scalp EEG with the implanted Silastic ECoG gird(s), in both computer simulations and in human experimentation. In an epilepsy patient undergoing surgical evaluation, the cortical potentials were reconstructed from the simultaneously recorded scalp EEG, in which main features of spatial patterns during interictal spike were preserved and over 0.75 correlation coefficient value was obtained between the recorded and estimated cortical potentials. The FEM-based CPI technique provides a means of connecting the simultaneous recorded ECoG and the scalp EEG and promises to become an effective tool to evaluate and validate CPI techniques using clinic data.

Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings

Objective The present study aims to accurately estimate the in vivo brain-to-skull conductivity ratio by means of cortical imaging technique. Simultaneous extra- and intra-cranial potential recordings induced by subdural current stimulation were analyzed to get the estimation. Methods The effective brain-to-skull conductivity ratio was estimated in vivo for 5 epilepsy patients. The estimation was performed using multi-channel simultaneously recorded scalp and cortical electrical potentials during subdural electrical stimulation. The cortical imaging technique was used to compute the inverse cortical potential distribution from the scalp recorded potentials using a 3-shell head volume conductor model. The brain-to-skull conductivity ratio, which leads to the most consistent cortical potential estimates with respect to the direct intra-cranial measurements, is considered to be the effective brain-to-skull conductivity ratio. Results The present estimation provided consistent results in 5 human subjects studied. The in vivo effective brain-to-skull conductivity ratio ranged from 18 to 34 in the 5 epilepsy patients. Conclusions The effective brain-to-skull conductivity ratio can be estimated from simultaneous intra- and extra-cranial potential recordings and the averaged value/standard deviation is 25±7. Significance The present results provide important experimental data on the brain-to-skull conductivity ratio, which is of significance for accurate brain source localization using piece-wise homogeneous head models.

Cortical Potential Imaging of Brain Electrical Activity by Means of Parametric Projection Filter

The objective of this study was to explore suitable spatial filters for inverse estimation of cortical potentials from the scalp electroencephalogram. The effect of incorporating noise covariance into inverse procedures was examined by computer simulations and tested in human experiment. The parametric projection filter, which allows inverse estimation with the presence of information on the noise, was applied to an inhomogeneous three-concentric-sphere model under various noise conditions in order to estimate the cortical potentials from the scalp potentials. The method for determining the optimum regularization parameter, which can be applied for parametric inverse techniques, is also discussed. Human visual evoked potential experiment was carried out to examine the performance of the proposed restoration method. The parametric projection filter gave more localized inverse solution of cortical potential distribution than the truncated SVD and Tikhonov regularization. The present simulation results suggest that incorporation of information on the noise covariance allows better estimation of cortical potentials, than inverse solutions without knowledge about the noise covariance, when the correlation between the signal and noise is low.

High-resolution EEG: Cortical potential imaging of interictal spikes

It is of clinical importance to localize pathologic brain tissue in epilepsy. Noninvasive localization of cortical areas associated with interictal epileptiform spikes may provide important information to facilitate presurgical planning for intractable epilepsy patients. A cortical potential imaging (CPI) technique was used to deconvolve the smeared scalp potentials into the cortical potentials. A 3-spheres inhomogeneous head model was used to approximately represent the head volume conductor. Five pediatric epilepsy patients were studied. The estimated cortical potential distributions of interictal spikes were compared with the subsequent surgical resections of these same patients. The areas of negativity in the reconstructed cortical potentials of interictal spikes in 5 patients were consistent with the areas of surgical resections for these patients. The CPI technique may become a useful alternative for noninvasive mapping of cortical regions displaying epileptiform activity from scalp electroencephalogram recordings.

A minimal product method and its application to cortical imaging.

In order to reduce the spatial blurring effect due to the head volume conductor, cortical imaging technique (CIT) can be used to reconstruct the cortical potential distribution from the scalp potential measurement with enhanced spatial resolution. To overcome the ill-posed nature of the inverse problem, Tikhonov regularization (TIK) and truncated Singular Value Decomposition (TSVD) are commonly used by choosing the appropriate regularization parameter and truncation parameter, respectively. We have developed a minimal product method (MINP) to determine the regularization and truncation parameters. The present computer simulation and experimental results indicate that the MINP can be easily implemented in both TIK and TSVD with satisfactory performance, and suggest the potential applications of the MINP method in determining the corner of the L-curve.

Cortical potential distributions and information processing.

The use of sets of spatiotemporal cortical potential distributions (CPDs) as the basis for cognitive information processing results in a very large space of cognitive elements with natural metrics. Results obtained from current source density (CSD) analysis suggest that in the CPD picture, action potentials may make only a relatively minor contribution to the brain's code. In order to establish if two CPDs are close, we consider standard metrics in spaces of continuous functions, and these may be employed to ascertain if two stimuli will be identified as the same. The correspondence between the electrical activity within brain regions, including not only action potentials but all postsynaptic potentials (PSPs), and CPDs is considered. We examine the possibility of using the CSD approach to find potential distributions using the descriptive approach in which precise sets of times are ascribed to the occurrence of action potentials and PSPs. Using metrics in the multidimensional space of paths of collections of point processes, we show that closeness of CPDs is implied by closeness of sets of spike times and PSP times if a certain metric is used but not others. We also set forth a dynamical model consisting of a system of reaction-dif fusion equations for ionic concentrations coupled with nerve membrane potential equations and active transport systems. Making the approximation of a descriptive approach, the correspondence between sets of spike times and PSP times and CPDs is obtained as with the CSD method. However, since it is not possible to ascribe precise times to the occurrence of PSPs and action potentials, the descriptive approach cannot be used to describe the configuration of electrical activity in cortical regions accurately. We also discuss how the CPD framework relates to the binding problem and submillisecond timing.

EEG brain mapping of phonological and semantic tasks in Italian and German languages

Objectives: Event-related potential correlates of phonological encoding – as compared with lexical access and semantic categorization – were measured in two studies involving two groups of 14 German and 14 Italian subjects. Methods: A two stimulus reaction time paradigm was used. Stimulus pairs presented one-by-one with 2 s inter-stimulus intervals (ISI) had to be matched with respect to lexical identity (word-picture) in a word comprehension task or with respect to the phonological representative of objects in a rhyming task. A semantic categorization task was added for the Italian sample. In both studies, the EEG was recorded from 26 scalp electrodes according to the 10–20 system. The slow negative potential during the ISI (CNV) was determined as the electrocortical correlate of preparation for and activation of the specific language-related task. Results: In both samples, phonological encoding (rhyming) evoked a more pronounced CNV over the left- compared with the right-frontal area, while less lateralized central dominance of the CNV was found in the word comprehension task. Semantic categorization was accompanied by the least asymmetry of activity. Conclusions: Results indicate that the different degree of asymmetry induced by phonological and semantic processing may be determined from the scalp distribution of slow cortical potentials with cross-lingual reliability.

On the realization of an analytic high-resolution EEG

The analytic solution of the harmonic downward continuation of the scalp potential field in an N-shell heterogeneous, but isotropic, spherical volume conductor model has been derived. The objective of this paper was to investigate the realization of a so-called "high-resolution electroencephalogram (EEG)": by enhancing the poor spatial resolution of EEG recordings. To this end, the forward problem for a dipolar source arbitrarily located at the source point Q = Q(rs, phi s, theta s) has been determined in a compact matrix notation. It is possible to transfer the potential field given on the outer surface of a spherically shaped volume conductor to an arbitrary inner surface (e.g., to the cortical surface) under consideration of the electrical and geometrical properties of the model. For the application of the proposed method to real-world problems, the coefficients of the series expansion describing the cortical potential distribution are determined by minimizing the squared curvature of the scalp potential field integrated over the scalp surface. Simulation results for distributed sources show that the proposed method is superior to the surface Laplacian method for interelectrode distances below 2.5 cm.

Mapping EEG-potentials on the surface of the brain: a strategy for uncovering cortical sources.

This paper describes a uniform method for calculating the interpolation of scalp EEG potential distribution, the current source density (CSD), the cortical potential distribution (cortical mapping) and the CSD of the cortical potential distribution. It will be shown that interpolation and deblurring methods such as CSD or cortical mapping are not independent of the inverse problem in potential theory. Not only the resolution but also the accuracy of these techniques, especially those of deblurring, depend greatly on the spatial sampling rate (i.e., the number of electrodes). Using examples from simulated and real (64 channels) data it can be shown that the application of more than 100 EEG channels is not only favourable but necessary to guarantee a reasonable accuracy in the calculations of CSD or cortical mapping. Likewise, it can be shown that using more than 250 electrodes does not improve the resolution.

Spatial sampling and filtering of EEG with spline laplacians to estimate cortical potentials.

The electroencephalogram (EEG) is recorded by sensors physically separated from the cortex by resistive skull tissue that smooths the potential field recorded at the scalp. This smoothing acts as a low-pass spatial filter that determines the spatial bandwidth, and thus the required spatial sampling density, of the scalp EEG. Although it is better appreciated in the time domain, the Nyquist frequency for adequate discrete sampling is evident in the spatial domain as well. A mathematical model of the low-pass spatial filtering of scalp potentials is developed, using a four concentric spheres (brain, CSF, skull, and scalp) model of the head and plausible estimates of the conductivity of each tissue layer. The surface Laplacian estimate of radial skull current density or cortical surface potential counteracts the low-pass filtering of scalp potentials by shifting the spatial spectrum of the EEG, producing a band-passed spatial signal that emphasizes local current sources. Simulations with the four spheres model and dense sensor arrays demonstrate that progressively more detail about cortical potential distribution is obtained as sampling is increased beyond 128 channels.

The surface Laplacian, high resolution EEG and controversies.

The surface Laplacian estimate (i.e., current source density) as obtained with spline functions is evaluated in the context of some recent controversies concerning high resolution EEG and source localization. In simulation studies, the spline-Laplacian provides much better estimates of cortical surface potential than is obtained from raw scalp potential, provided dense electrode arrays (e.g., 64 or more electrodes) are used. Spline-Laplacians (which are relatively independent of volume conductor model) provide estimates of cortical potential distribution which are quite similar to those obtained with a cortical imaging algorithm based on a four sphere model.

A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging

Two different methods to improve the spatial resolution of EEG are discussed: the surface Laplacian (e.g., current source density) and cortical imaging (e.g., spatial deconvolution). The former methods tend to be independent of head volume conductor model, whereas the latter methods are more model-dependent. Computer simulations of scalp potentials due to either a few isolated sources or 4200 distributed cortical sources and studies of actual EEG data both indicate that the two methods provide similar estimates of cortical potential distribution. Typical correlation coefficients between either spline-Laplacian or cortical image and simulated (calculated) cortical potential are in the 0.8–0.95 range, depending partly on CSF thickness. By contrast, correlation coefficients between simulated scalp and cortical potential are in the 0.4–0.5 range, suggesting that high resolution methods provide much better estimates of cortical potential than is obtained with conventional EEG. The two methods are also applied to steady-state visually evoked potentials and spontaneous EEG. Correlation coefficients obtained from real EEG data are in the same general ranges as correlations obtained from simulations. The new high resolution methods can provide a dramatic increase in the information content of EEG and appear to have widespread application in both clinical and cognitive studies.