Integration of High Resolution EEG and Functional Magnetic Resonance in the Study of Human Movement-Related Potentials

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.

Inferring Macroscale Brain Dynamics via Fusion of Simultaneous EEG-fMRI

Advances in the instrumentation and signal processing for simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) have enabled new ways to observe the spatiotemporal neural dynamics of the human brain. Central to the utility of EEG-fMRI neuroimaging systems are the methods for fusing the two data streams, with machine learning playing a key role. These methods can be dichotomized into those that are symmetric and asymmetric in terms of how the two modalities inform the fusion. Studies using these methods have shown that fusion yields new insights into brain function that are not possible when each modality is acquired separately. As technology improves and methods for fusion become more sophisticated, the future of EEG-fMRI for noninvasive measurement of brain dynamics includes mesoscale mapping at ultrahigh magnetic resonance fields, targeted perturbation-based neuroimaging, and using deep learning to uncover nonlinear representations that link the electrophysiological and hemodynamic measurements.

The first impression is what matters: a neuroaesthetic study of the cerebral perception and appreciation of paintings by Titian

In this paper we measured the neuroelectrical and the eye-movements activities in a group of 27 healthy subjects during their visit of a fine arts gallery in which a series of masterpieces of the Italian painter Tiziano Vecellio (also known as Titian, 1488-1576) were shown. The pictures chosen for the visit were 10 portraits and 10 of religious subjects. Each picture was observed for a minute. A mobile EEG device with an eye-tracker was used for this experiment. Evaluation of the appreciation of the pictures was performed by using the neuroelectrical approach-withdrawal index (AW). High value of AW means high appreciation of the picture. The number of eye fixations performed by the subjects during the observation of the pictures was also analyzed. Results showed that in the examined group the AW index was significant higher during the observation of portraits than during the observation of the religious subjects (as resulted from an ANOVA performed on AW index, with a p<;0,007). Interestingly, the average AW index estimated in the first 20 seconds of the observation of the pictures remains highly correlated with the AW index evaluated for the second part of the data (from 20 s to one minute) for all the 20 pictures examined (r = 0,82, p<;0,0001). In addition, the number of eye fixations performed by the subjects in the first 5 or 10 seconds of observation of the pictures that were most appreciated are significantly higher than the number of eye fixations performed on pictures that subjects did not like (p<;0,048 and p<;0,0018, respectively). Such difference vanishes if the entire period of observation of the pictures of one minute is used (p = 0,54). Taken together, such results seem to suggest that the neuroelectrical correlates of the perception of "good" or "bad" pictures are rapidly formed in our brain, within the first 10-20 seconds from the exposition to the picture.

A pilot study on the neurometric evaluation of "effective" and "ineffective" antismoking public service announcements

Tobacco use is the leading cause of preventable death and smoking-related illness worldwide. Research has shown that antismoking advertising may help reduce this habit. Nowadays, public service announcements (PSAs) are considered "Effective" or "Ineffective" on the base of official reports concerning behavioral/attitudinal changes toward healthier patterns and health-related savings following the exposure to the PSA. In this pilot study, we described the results of the use of three neurometric indexes for the evaluation of the efficacy of a couple of antismoking PSAs in a reduced sample of voluntary participants. The study applied the gathering of the electroencephalographic (EEG) rhythms variations, as well as the heart rate (HR) and galvanic skin response (GSR). The neurometric indicators here employed were the Approach-Withdrawal (AW), the Effort (EfI) and the Emotional (EI) indexes. Results suggest a significant higher values for AW, Effort and Emotional indexes (p=0,02; p= 0,03 and p= 0,01 respectively) related to the perception of the "Effective" antismoking PSAs against the perception of the "Ineffective" one. Since this is a pilot study, the results obtained need further investigation, in terms of enlarged stimuli sample and number of participants to provide indications concerning the relevant features to be included in the realization of effective anti-smoking PSAs.

Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment

Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.

Gender and Age Related Effects While Watching TV Advertisements: An EEG Study

The aim of the present paper is to show how the variation of the EEG frontal cortical asymmetry is related to the general appreciation perceived during the observation of TV advertisements, in particular considering the influence of the gender and age on it. In particular, we investigated the influence of the gender on the perception of a car advertisement (Experiment 1) and the influence of the factor age on a chewing gum commercial (Experiment 2). Experiment 1 results showed statistically significant higher approach values for the men group throughout the commercial. Results from Experiment 2 showed significant lower values by older adults for the spot, containing scenes not very enjoyed by them. In both studies, there was no statistical significant difference in the scene relative to the product offering between the experimental populations, suggesting the absence in our study of a bias towards the specific product in the evaluated populations. These evidences state the importance of the creativity in advertising, in order to attract the target population.

Mental workload estimations in unilateral deafened children

Despite of technological innovations, noisy environments still constitute a challenging and stressful situation for words recognition by hearing impaired subjects. The evaluation of the mental workload imposed by the noisy environments for the recognition of the words in prelingually deaf children is then of paramount importance since it could affect the speed of the learning process during scholar period.The aim of the present study was to investigate different electroencephalographic (EEG) power spectral density (PSD) components (in theta 4-8 Hz - and alpha - 8-12 Hz - frequency bands) to estimate the mental workload index in different noise conditions during a word recognition task in prelingually deaf children, a population not yet investigated in relation to the workload index during auditory tasks. A pilot study involving a small group of prelingually deaf children was then subjected to EEG recordings during an auditory task composed by a listening and a successive recognition of words with different noise conditions. Results showed that in the pre-word listening phase frontal EEG PSD in theta band and the ratio of the frontal EEG PSD in theta band and the parietal EEG PSD in alpha band (workload index; IWL) reported highest values in the most demanding noise condition. In addition, in the phase preceding the word forced-choice task the highest parietal EEG PSD in alpha band and IWL values were reported at the presumably simplest condition (noise emitted in correspondence of the subject's deaf ear). These results could suggest the prominence of EEG PSD theta component activity in the pre-word listening phase. In addition, a more challenging noise situation in the pre-choice phase would be so "over-demanding" to fail to enhance both the alpha power and the IWL in comparison to the already demanding "simple" condition.

Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe.

Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness

This paper reviews published papers related to neurophysiological measurements (electroencephalography: EEG, electrooculography EOG; heart rate: HR) in pilots/drivers during their driving tasks. The aim is to summarise the main neurophysiological findings related to the measurements of pilot/driver's brain activity during drive performance and how particular aspects of this brain activity could be connected with the important concepts of "mental workload", "mental fatigue" or "situational awareness". Review of the literature suggests that exists a coherent sequence of changes for EEG, EOG and HR variables during the transition from normal drive, high mental workload and eventually mental fatigue and drowsiness. In particular, increased EEG power in theta band and a decrease in alpha band occurred in high mental workload. Successively, increased EEG power in theta as well as delta and alpha bands characterise the transition between mental workload and mental fatigue. Drowsiness is also characterised by increased blink rate and decreased HR values. The detection of such mental states is actually performed "offline" with accuracy around 90% but not online. A discussion on the possible future applications of findings provided by these neurophysiological measurements in order to improve the safety of the vehicles will be also presented.

High-resolution EEG analysis of power spectral density maps and coherence networks in a proportional reasoning task

Proportional reasoning is very important logical skill required in mathematics and science problem solving as well as in everyday life decisions. However, there is a lack of studies on neurophysiological correlates of proportional reasoning. To explore the brain activity of healthy adults while performing a balance scale task, we used high-resolution EEG techniques and graph-theory based connectivity analysis. After unskilled subjects learned how to properly solve the task, their cortical power spectral density (PSD) maps revealed an increased parietal activity in the beta band. This indicated that subjects started to perform calculations. In addition, the number of inter-hemispheric connections decreased after learning, implying a rearrangement of the brain activity. Repeated performance of the task led to the PSD decrease in the beta and gamma bands among parietal and frontal regions along with a synchronization of lower frequencies. These findings suggest that repetition led to a more automatic task performance. Subjects were also divided in two groups according to their scores on the test of logical thinking (TOLT). Although no group differences in the accuracy and reaction times were found, EEG data showed higher activity in the beta and gamma bands for the group that scored better on TOLT. Learning and repetition induced changes in the pattern of functional connectivity were evident for all frequency bands. Overall, the results indicated that higher frequency oscillations in frontal and parietal regions are particularly important for proportional reasoning.

Simultaneous estimation of cortical activity during social interactions by using EEG hyperscannings

In this paper we show how the possibility of recording simultaneously the cerebral neuroelectric activity in different subjects (EEG hyperscanning) during the execution of different tasks could return useful information about the "internal" cerebral state of the subjects. We present the results obtained by EEG hyperscannings during ecological task (such as the execution of a card game) as well as that obtained in a series of couples of subjects during the performance of the Prisoner's Dilemma Game. The simultaneous recordings of couples of interacting subjects allows to observe and to model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. Results obtained in a study of different groups recorded during the card game revealed a larger activity in prefrontal and anterior cingulated cortex in different frequency bands for the player that leads the game when compared to other players. Results collected in a population of 10 subjects during the performance of the Prisoner's Dilemma suggested that the most consistently activated structure is the orbitofrontal region (roughly described by the Brodmann area 10) during the condition of competition in both the tasks. It could be speculated whether the pattern of cortical connectivity between different cortical areas in different subjects could be employed as a tool for assessing the outcome of the task in advance.

Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity—i.e., the tendency to form two separate subgraphs—than couples playing cooperative or tit-for-tat strategies. The decision to defect can be “read” in advance by evaluating the changes of connectivity pattern in the hyper-brain network.

The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: an application in a neuromarketing experiment

This paper presents some considerations about the use of adequate statistical techniques in the framework of the neuroelectromagnetic brain mapping. With the use of advanced EEG/MEG recording setup involving hundred of sensors, the issue of the protection against the type I errors that could occur during the execution of hundred of univariate statistical tests, has gained interest. In the present experiment, we investigated the EEG signals from a mannequin acting as an experimental subject. Data have been collected while performing a neuromarketing experiment and analyzed with state of the art computational tools adopted in specialized literature. Results showed that electric data from the mannequin's head presents statistical significant differences in power spectra during the visualization of a commercial advertising when compared to the power spectra gathered during a documentary, when no adjustments were made on the alpha level of the multiple univariate tests performed. The use of the Bonferroni or Bonferroni-Holm adjustments returned correctly no differences between the signals gathered from the mannequin in the two experimental conditions. An partial sample of recently published literature on different neuroscience journals suggested that at least the 30% of the papers do not use statistical protection for the type I errors. While the occurrence of type I errors could be easily managed with appropriate statistical techniques, the use of such techniques is still not so largely adopted in the literature.

Time-Varying Cortical Connectivity Estimation from Noninvasive, High-Resolution EEG Recordings

Objective: In this paper, we propose a body of techniques for the estimation of rapidly changing connectivity relationships between EEG signals estimated in cortical areas, based on the use of adaptive multivariate autoregressive modeling (AMVAR) for the estimation of a time-varying partial directed coherence (PDC). This approach allows the observation of rapidly changing influences between the cortical areas during the execution of a task, and does not require the stationarity of the signals. Methods: High resolution EEG data were recorded from a group of spinal cord injured (SCI) patients during the attempt to move a paralyzed limb. These data were compared with the time-varying connectivity patterns estimated in a control group during the real execution of the movement. Connectivity was estimated with the use of realistic head modeling and the linear inverse estimation of the cortical activity in a series of regions of interest by using time-varying PDC. Results: The SCI population involved a different cortical network than those generated by the healthy subjects during the task performance. Such a network differs for the involvement of the parietal cortices, which increases in strength near to the movement imagination onset for the SCI when compared to the normal population. Conclusions: The application of time-varying PDC allows tracking the evolution of the connectivity between cortical areas in the analyzed populations during the proposed tasks. Such details about the temporal evolution of the connectivity patterns estimated cannot be obtained with the application of the standard estimators of connectivity.

Ballistocardiogram artifact removal from EEG signals using adaptive filtering of EOG signals

We estimated ballistocardiogram (BCG) components in EEG signals recorded inside an MRI magnet using the electro-oculogram (EOG) signals recorded simultaneously with the EEG signals. Since the EOG signals are measured near the EEG measuring points, it is thought that the BCG components in the EOG signals resemble the BCG components in the EEG signals. To estimate the BCG components in the EEG signals, we applied the Kalman filter to the EOG and EEG signals recorded inside a 3.0 T MRI magnet. After removing the estimated BCG components from the EEG signals, we extracted the visual-evoked potentials (VEPs) from the BCG-removed EEG signals. To validate the efficacy of Kalman filtering in the BCG artifact removal, we have compared three types of VEPs of eight healthy subjects: one extracted from the raw EEG signals measured outside the magnet and the others extracted from the BCG-removed EEG signals measured inside the magnet. The BCG artifacts have been removed with Kalman filtering as well as with the conventional BCG template subtraction method for the sake of comparison. No significant difference in waveforms, latencies and amplitudes has been found between the two types of VEPs extracted from the two kinds of BCG-removed EEG signals.

Resistor mesh model of a spherical head: Part 2: A review of applications to cortical mapping

A resistor mesh model (RMM) has been validated with reference to the analytical model by consideration of a set of four dipoles close to the cortex. The application of the RMM to scalp potential interpolation was detailed in Part 1. Using the RMM and the same four dipoles, the different methods of cortical mapping were compared and have shown the potentiality of this RMM for obtaining current and potential cortical distributions. The lead-field matrices are well-adapted tools, but the use of a square matrix of high dimension does not permit the inverse solution to be improved in the presence of noise, as a regularisation technique is necessary with noisy data. With the RMM, the transfer matrix and the cortical imaging technique proved to be easy to implement. Further development of the RMM will include application to more realistic head models with more accurate conductivities.

Solving the neuroimaging puzzle: the multimodal integration of neuroelectromagnetic and functional magnetic resonance recordings

In this chapter, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are reviewed. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model, and regularized linear inverse source estimates. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) and event-related (coupling of activated voxels) fMRI. As an example, these methods were applied to EEG (128 electrodes) and fMRI data, which were recorded in separate sessions while normal subjects executed voluntary right one-digit movements.

Low resolution brain electromagnetic tomography in a realistic geometry head model: a simulation study

It is of importance to localize neural sources from scalp recorded EEG. Low resolution brain electromagnetic tomography (LORETA) has received considerable attention for localizing brain electrical sources. However, most such efforts have used spherical head models in representing the head volume conductor. Investigation of the performance of LORETA in a realistic geometry head model, as compared with the spherical model, will provide useful information guiding interpretation of data obtained by using the spherical head model. The performance of LORETA was evaluated by means of computer simulations. The boundary element method was used to solve the forward problem. A three-shell realistic geometry (RG) head model was constructed from MRI scans of a human subject. Dipole source configurations of a single dipole located at different regions of the brain with varying depth were used to assess the performance of LORETA in different regions of the brain. A three-sphere head model was also used to approximate the RG head model, and similar simulations performed, and results compared with the RG-LORETA with reference to the locations of the simulated sources. Multisource localizations were discussed and examples given in the RG head model. Localization errors employing the spherical LORETA, with reference to the source locations within the realistic geometry head, were about 20-30 mm, for four brain regions evaluated: frontal, parietal, temporal and occipital regions. Localization errors employing the RG head model were about 10 mm over the same four brain regions. The present simulation results suggest that the use of the RG head model reduces the localization error of LORETA, and that the RG head model based LORETA is desirable if high localization accuracy is needed.

Slow cortical potential shifts preceding sensorimotor interactions

It is well known that synchronization of cortical neurons is modulated ("gating") by the chronological interaction between somatosensory and sensorimotor events. This study tested the hypothesis that the anticipatory processes for this interaction increase the synchronization of cortical neurons as revealed by negative event-related potentials (contingent negative variation, CNV). High-resolution electroencephalographic data (128 electrodes) were recorded in 14 subjects. In the "sensorimotor interaction" condition, the subjects were waiting for a galvanic somatosensory stimulation at the left hand concomitant with a Go or NoGo stimulus (50% of Go trials triggering right hand movements). In the control condition, the Go/NoGo stimulus followed the somatosensory stimulation of 1.5s. The electroencephalographic data were spatially enhanced by surface Laplacian estimation. In the control condition, the CNV was observed only in the foreperiod between the somatosensory stimulation and Go/NoGo task (i.e. no CNV before the somatosensory stimuli). It was spatially localized in the primary sensorimotor area contralateral to the possible motor response. In the "sensorimotor interaction" condition, the CNV preceded the concomitant somatosensory stimulation and Go/NoGo task and was distributed to the frontocentral midline other than the contralateral sensorimotor area. These results suggest that the anticipatory processes for sensorimotor interactions increase the synchronization of cortical neurons in the frontocentral midline, possibly due to mechanisms sub-serving top-down attentional processes.

Alpha event-related desynchronization preceding a go/no-go task: a high-resolution EEG study

The authors delineated the time evolution of alpha event-related desynchronization over human frontal, parietal, and primary sensorimotor areas during the expectancy of a go/no-go task. The main issue under investigation was whether anticipatory processes impinged upon cortical areas in sequential or parallel mode. Compared with the control condition, in the experimental condition there was an Alpha 1 desynchronization over the central midline, an Alpha 2 desynchronization increasing over primary sensorimotor areas, and an Alpha 3 desynchronization increasing in parallel over bilateral primary sensorimotor areas. These processes had different temporal features. Results disclose an anticipatory activity of central midline areas and primary sensorimotor areas in both parallel and sequential modes. This reflects an adaptive, energy-consuming strategy rather than an economic waiting for the go stimulus.

ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner

Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for removing artifacts that are linearly and independently mixed with signals of interest. Here, we demonstrate and validate the usefulness of ICA in removing BCG artifacts from EEG data acquired in the MRI scanner. In accordance with our hypothesis that BCG artifacts are physiologically independent from EEG, it was found that ICA consistently resulted in five to six independent components representing the BCG artifact. Following removal of these components, a significant reduction in spectral power at frequencies associated with the BCG artifact was observed. We also show that our ICA-based procedures perform significantly better than noise-cancellation methods that rely on estimation and subtraction of averaged artifact waveforms from the recorded EEG. Additionally, the proposed ICA-based method has the advantage that it is useful in situations where ECG reference signals are corrupted or not available.

Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle

In this paper, advanced methods for the modeling of human cortical activity from combined high-resolution electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from magnetic resonance images, multidipole source model and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. Examples of the application of these methods to the estimation of the time varying cortical current density activity in selected region of interest (ROI) are presented for movement-related high-resolution EEG data.

High-resolution EEG: cortical potential imaging of interictal spikes

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

The CPI technique may become a useful alternative for noninvasive mapping of cortical regions displaying epileptiform activity from scalp electroencephalogram recordings.

Estimation of Active Cortical Current Source Regions Using a Vector Representation Scanning Approach

The objective of this article is to present a framework for cortical current source reconstruction that extracts a center and magnitude of electrical brain activity from EEG signals. High-resolution EEG recordings, a subject-specific MRI-based electromagnetic boundary element method (BEM) model, and a channel reduction technique are used. This new geometric measure combines the magnitude and spatial location of electrical brain activity of each of the identified subsets of channels into a three-dimensional resultant vector. The combination of the two approaches constitutes a source reconstruction scanning technique that provides a real-time estimation of cortical centers that can be tracked over time. Simulations demonstrate that the ability of this method to find the best-fit cortical location is more robust both in terms of accuracy and precision than traditional approaches for single-source conditions. Experimental validation demonstrates its ability to localize and separate cortical activity in plausible sites for two different motor tasks. Finally, this method provides a statistical measure to compare electrical brain activity associated with different motor tasks.

High-resolution EEG mapping: an equivalent charge-layer approach

Brain electrical signal is one of the windows to understanding neural activities. Various high-resolution imaging techniques have been developed to reveal the electrical activities underneath the cortical surface from scalp electroencephalographic recordings, such as scalp Laplacian, cortical surface potential, equivalent charge layer (ECL) and equivalent dipole layer (EDL). In this work, we develop forward density formulae for the ECL and the EDL of neural electric sources in a 4-concentric-sphere head model, and compare ECL with EDL in theory, simulation and real evoked data tests. The results confirm that the ECL map may be of higher spatial resolution than the EDL map.

Boundary element method-based cortical potential imaging of somatosensory evoked potentials using subjects' magnetic resonance images

A boundary element method-based cortical potential imaging technique has been developed to directly link the scalp potentials with the cortical potentials with the aid of magnetic resonance images of the subjects. First, computer simulations were conducted to evaluate the new approach in a concentric three-sphere inhomogeneous head model. Second, the corresponding cortical potentials were estimated from the patients' preoperative scalp somatosensory evoked potentials (SEPs) based on the boundary element models constructed from subjects' magnetic resonance images and compared to the postoperative direct cortical potential recordings in the same patients. Simulation results demonstrated that the cortical potentials can be estimated from the scalp potentials using different scalp electrode configurations and are robust against measurement noise. The cortical imaging analysis of the preoperative scalp SEPs recorded from patients using the present approach showed high consistency in spatial pattern with the postoperative direct cortical potential recordings. Quantitative comparison between the estimated and the directly recorded subdural grid potentials resulted in reasonably high correlation coefficients in cases studied. Amplitude difference between the estimated and the recorded potentials was also observed as indexed by the relative error, and the possible underlying reasons are discussed. The present numerical and experimental results validate the boundary element method-based cortical potential imaging approach and demonstrate the feasibility of the new approach in noninvasive high-resolution imaging of brain electric activities from scalp potential measurement and magnetic resonance images.

High-resolution EEG: on the cortical equivalent dipole layer imaging

BACKGROUND

Brain electrical activity is a spatio-temporally distributed process. Cortical imaging techniques have been developed to reconstruct cortical activity from the scalp electroencephalographic or magnetoencephalographic measurements. Several cortical imaging approaches, such as the epicortical potentials and a dipole layer accounting for the cortical activity, have been used to represent brain electrical activity.

METHODS

A closed cortical dipole layer source model is used to equivalently represent brain electrical activity. The relationship between the primary brain electrical sources and the cortical equivalent dipole layer is derived from the theory of electromagnetics. Computer simulation studies were conducted using a 3-concentric-sphere head model to validate the proposed theory. The cortical equivalent dipole layer imaging approach was tested in both computer simulation and human visual evoked potential (VEP) experiments.

RESULTS

The strength of the cortical equivalent dipole layer is shown to be proportional to the electrical potential over the same surface generated by primary electrical sources, had the outer medium been replaced by air. The proposed theory was validated by computer simulation in a discrete system. Simulation and VEP experimental studies suggest the feasibility of applying the cortical equivalent dipole layer imaging approach for brain imaging.

CONCLUSIONS

The cortical equivalent dipole layer model can equivalently represent the primary brain electrical sources throughout the entire brain surrounded by the dipole layer. The strength of the cortical equivalent dipole layer due to primary sources can be directly calculated according to the theory developed in the present study.

Electric source imaging of human brain functions

We review recent methodological advances in electromagnetic source imaging and present EEG data from our laboratory obtained by application of these methods. There are two principal steps in our analysis of multichannel electromagnetic recordings: (i) the determination of functionally relevant time periods in the ongoing electric activity and (ii) the localization of the sources in the brain that generate these activities recorded on the scalp. We propose a temporal segmentation of the time-varying activity, which is based on determination of changes in the topography of the electric fields, as an approach to the first step, and a distributed linear inverse solution based on realistic head models as an approach to the second step. Data from studies of visual motion perception, visuo-motor transfer, mental imagery, semantic decision, and cognitive interference illustrate that this analysis allows us to define the patterns of electric activity that are present at given time periods after stimulus presentation, as well as those time periods where significantly different patterns appear between different stimuli and tasks. The presented data show rapid and parallel activation of different areas within complex neuronal networks, including early activity of brain regions remote from the primary sensory areas. In addition, the data indicate information exchange between homologous areas of the two hemispheres in cases where unilateral stimulus presentation requires interhemispheric transfer.

The effect of the skull on event-related P300

OBJECTIVES

Event-related potentials (EP) indicate neuronal processes with a high temporal resolution, while functional magnetic resonance imaging (fMRI) has a high spatial distribution. Information from both techniques may complement each other. However, this combination is fraught with difficulty because of a possible interference of the skull or the scalp with scalp-recorded EP. The aim of the present study was to investigate this influence of skull and scalp thicknesses on event-related P300 potentials.

METHODS

Thirty healthy controls were examined using an auditory evoked P300 elicited by a standard oddball paradigm. Skull and scalp thicknesses were determined using coronal T2-weighted magnetic resonance imaging (MRI).

RESULTS

P3b-amplitudes were significantly correlated with temporo-parietal skull thickness (r=-0.42; P=0.021; regression slope of -1.14 microV/mm skull thickness), whereas scalp had no influence on P300. The amplitude of the more frontal subcomponent P3a was not related to frontal skull thickness.

CONCLUSIONS

Therefore, the utility of P300 as a research tool can be enhanced when adjustment for skull thickness is made.