Electromagnetic Source Imaging Research Articles

Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network.

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.

Deep learning based source imaging provides strong sublobar localization of epileptogenic zone from MEG interictal spikes

Electromagnetic source imaging (ESI) offers unique capability of imaging brain dynamics for studying brain functions and aiding the clinical management of brain disorders. Challenges exist in ESI due to the ill-posedness of the inverse problem and thus the need of modeling the underlying brain dynamics for regularizations. Advances in generative models provide opportunities for more accurate and realistic source modeling that could offer an alternative approach to ESI for modeling the underlying brain dynamics beyond equivalent physical source models. However, it is not straightforward to explicitly formulate the knowledge arising from these generative models within the conventional ESI framework. Here we investigate a novel source imaging framework based on mesoscale neuronal modeling and deep learning (DL) that can learn the sensor-source mapping relationship directly from MEG data for ESI. Two DL-based ESI models were trained based on data generated by neural mass models and either generic or personalized head models. The robustness of the two DL models was evaluated by systematic computer simulations and clinical validation in a cohort of 29 drug-resistant focal epilepsy patients who underwent intracranial EEG (iEEG) evaluation or surgical resection. Results estimated from pre-operative MEG interictal spikes were quantified using the overlap with resection regions and the distance to the seizure-onset zone (SOZ) defined by iEEG recordings. The DL-based ESI provided robust results when no personalized head geometry is considered, reaching a spatial dispersion of 21.90 ± 19.03 mm, sublobar concordance of 83 %, and sublobar sensitivity and specificity of 66 and 97 % respectively. When using personalized head geometry derived from individual patients’ MRI in the training data, personalized DL-based ESI model can further improve the performance and reached a spatial dispersion of 8.19 ± 8.14 mm, sublobar concordance of 93 %, and sublobar sensitivity and specificity of 77 and 99 % respectively. When compared to the SOZ, the localization error of the personalized approach is 15.78 ± 5.54 mm, outperforming the conventional benchmarks. This work demonstrates that combining generative models and deep learning enables an accurate and robust imaging of epileptogenic zone from MEG recordings with strong sublobar precision, suggesting its added value to enhancing MEG source localization and imaging, and to epilepsy source localization and other clinical applications.

Electromagnetic source imaging predicts surgical outcome in children with focal cortical dysplasia

ObjectiveTo evaluate the diagnostic accuracy of electromagnetic source imaging (EMSI) in localizing spikes and predict surgical outcome in children with drug resistant epilepsy (DRE) due to focal cortical dysplasia (FCD). MethodsWe retrospectively analyzed magnetoencephalography (MEG) and high-density (HD-EEG) data from 23 children with FCD-associated DRE who underwent intracranial EEG and surgery. We localized spikes using equivalent current dipole (ECD) fitting, dipole clustering, and dynamical statistical parametric mapping (dSPM) on EMSI, electric source imaging (ESI), and magnetic source imaging (MSI). We calculated the distance from the seizure onset zone (DSOZ) and resection (DRES). We estimated receiver operating characteristic (ROC) curves with Youden’s index (J) to predict outcome. ResultsEMSI presented shorter DSOZ (15.18 ± 9.06 mm) and DRES (8.56 ± 6.24 mm) compared to ESI (DSOZ: 25.04 ± 16.20 mm, p < 0.009; DRES: 18.88 ± 17.30 mm, p < 0.03) and MSI (DSOZ: 23.37 ± 8.98 mm, p < 0.03; DRES: 15.51 ± 10.11 mm, p < 0.02) for clustering in patients with good outcome. Clustering showed shorter DSOZ and DRES compared to ECD fitting and dSPM (p < 0.05). EMSI had higher performance as outcome predictor (J = 70.63%) compared to ESI (J = 41.27%) and MSI (J = 33.33%) for clustering. ConclusionsEMSI provides superior localization and improved predictive performance than individual modalities. SignificanceEMSI can help the surgical planning and facilitate the localization of epileptogenic foci.

Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6[Formula: see text]h and reduced the objective function by an average of 13.2% on 3.75[Formula: see text]min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.

Electromagnetic Source Imaging With a Combination of Sparse Bayesian Learning and Deep Neural Network.

Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. The network is trained with synthesized data derived from the probabilistic graphical model embedded in the conventional algorithm. We achieved a realization of this framework with the algorithm, source imaging based on spatio-temporal basis function (SI-STBF), as backbone. In numerical simulations, the proposed algorithm validated its availability for different head models and robustness against distinct intensities of the noise. Meanwhile, it acquired superior performance compared to SI-STBF and several benchmarks in a variety of source configurations. Additionally, in real data experiments, it obtained the concordant results with the prior studies.

Research on Top Archer’s EEG Microstates and Source Analysis in Different States

The electroencephalograph (EEG) microstate is a method used to describe the characteristics of the EEG signal through the brain scalp electrode potential’s spatial distribution; as such, it reflects the changes in the brain’s functional state. The EEGs of 13 elite archers from China’s national archery team and 13 expert archers from China’s provincial archery team were recorded under the alpha rhythm during the resting state (with closed eyes) and during archery aiming. By analyzing the differences between the EEG microstate parameters and the correlation between these parameters with archery performance, as well as by combining our findings through standardized low-resolution brain electromagnetic tomography source analysis (sLORETA), we explored the changes in the neural activity of professional archers of different levels, under different states. The results of the resting state study demonstrated that the duration, occurrence, and coverage in microstate D of elite archers were significantly higher than those of expert archers and that their other microstates had the greatest probability of transferring to microstate D. During the archery aiming state, the average transition probability of the other microstates transferring to microstate in the left temporal region was the highest observed in the two groups of archers. Moreover, there was a significant negative correlation between the duration and coverage of microstates in the frontal region of elite archers and their archery performance. Our findings indicate that elite archers are more active in the dorsal attention system and demonstrate a higher neural efficiency during the resting state. When aiming, professional archers experience an activation of brain regions associated with archery by suppressing brain regions unrelated to archery tasks. These findings provide a novel theoretical basis for the study of EEG microstate dynamics in archery and related cognitive motor tasks, particularly from the perspective of the subject’s mental state.

Clinical Yield of Electromagnetic Source Imaging and Hemodynamic Responses in Epilepsy: Validation With Intracerebral Data.

Accurate delineation of the seizure-onset zone (SOZ) in focal drug-resistant epilepsy often requires stereo-EEG (SEEG) recordings. Our aims were to propose a truly objective and quantitative comparison between EEG/magnetoencephalography (MEG) source imaging (EMSI), EEG/fMRI responses for similar spikes with primary irritative zone (PIZ) and SOZ defined by SEEG and to evaluate the value of EMSI and EEG/fMRI to predict postsurgical outcome. We identified patients with drug-resistant epilepsy who underwent EEG/MEG, EEG/fMRI, and subsequent SEEG at the Epilepsy Service from the Montreal Neurological Institute and Hospital. We quantified multimodal concordance within the SEEG channel space as spatial overlap with PIZ/SOZ and distances to the spike-onset, spike maximum amplitude and seizure core intracerebral channels by applying a new methodology consisting of converting EMSI results into SEEG electrical potentials (EMSIe-SEEG) and projecting the most significant fMRI response on the SEEG channels (fMRIp-SEEG). Spatial overlaps with PIZ/SOZ (AUCPIZ, AUCSOZ) were assessed by using the area under the receiver operating characteristic curve (AUC). Here, AUC represents the probability that a randomly picked active contact exhibited higher amplitude when located inside the spatial reference than outside. Seventeen patients were included. Mean spatial overlaps with the PIZ and SOZ were 0.71 and 0.65 for EMSIe-SEEG and 0.57 and 0.62 for fMRIp-SEEG. Good EMSIe-SEEG spatial overlap with the PIZ was associated with smaller distance from the maximum EMSIe-SEEG contact to the spike maximum amplitude channel (median distance 14 mm). Conversely, good fMRIp-SEEG spatial overlap with the SOZ was associated with smaller distances from the maximum fMRIp-SEEG contact to the spike-onset and seizure core channels (median distances 10 and 5 mm, respectively). Surgical outcomes were correctly predicted by EEG/MEG in 12 of 15 (80%) patients and EEG/fMRI in 6 of 11(54%) patients. With the use of a unique quantitative approach estimating EMSI and fMRI results in the reference SEEG channel space, EEG/MEG and EEG/fMRI accurately localized the SOZ and the PIZ. Precisely, EEG/MEG more accurately localized the PIZ, whereas EEG/fMRI was more sensitive to the SOZ. Both neuroimaging techniques provide complementary localization that can help guide SEEG implantation and select good candidates for surgery.

Electromagnetic Source Imaging via Bayesian Modeling With Smoothness in Spatial and Temporal Domains.

Accurate reconstruction of cortical activation from electroencephalography and magnetoencephalography (E/MEG) is a long-standing challenge because of the inherently ill-posed inverse problem. In this paper, a novel algorithm under the empirical Bayesian framework, source imaging with smoothness in spatial and temporal domains (SI-SST), is proposed to address this issue. In SI-SST, current sources are decomposed into the product of spatial smoothing kernel, sparseness encoding coefficients, and temporal basis functions (TBFs). Further smoothness is integrated in the temporal domain with the employment of an underlying autoregressive model. Because sparseness encoding coefficients are constructed depending on overlapped clusters over cortex in this model, we derived a novel update rule based on fixed-point criterion instead of the convexity based approach which becomes invalid in this scenario. Entire variables and hyper parameters are updated alternatively in the variational inference procedure. SI-SST was assessed by multiple metrics with both simulated and experimental datasets. In practice, SI-SST had the superior reconstruction performance in both spatial extents and temporal profiles compared to the benchmarks.

Electromagnetic evidence that benign epileptiform transients of sleep are traveling, rotating hippocampal spikes

ObjectiveBenign epileptiform transients of sleep (BETS) have a unique voltage topography and a posteriorly propagating, inferiorly rotating diphasic EEG pattern. The source generators of BETS have not been definitively identified. We aimed to clarify the cerebral localization of BETS using MEG and electromagnetic source imaging (EMSI). MethodsWe analyzed BETS recorded with simultaneous MEG and EEG in four patients with epilepsy. Magnetic source imaging (MSI) and EMSI using equivalent current, single moving and rotating dipole inverse models was performed on averaged BETS potentials. MEG beamforming was performed in one case with abundant BETS. ResultsMSI and EMSI revealed hippocampal dipole source maxima in all cases, with current flow direction rotating from inferomedial to superomedial or superolateral between the first and second BETS peaks. Moving dipole analyses revealed spatiotemporal propagation along the anterior-posterior hippocampal axis and concomitant electromagnetic field rotation. Beamformer source reconstruction revealed an identical hippocampal localization. ConclusionsConverging evidence from different electromagnetic inverse modeling methods indicates that BETS are traveling, rotating hippocampal spikes, whose diphasic waveform is due to back and forth propagation along the anterior-posterior axis of the hippocampus. SignificanceThe hippocampal localization and longitudinal, rotating propagation pattern of BETS raises the possibility of a sleep-related functional role for these hippocampal spikes.

Influence of Patient-Specific Head Modeling on EEG Source Imaging.

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject's head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.

The analytical subtraction approach for solving the forward problem in EEG

Objective. The subtraction approach is known for being a theoretically-rigorous and accurate technique for solving the forward problem in electroencephalography by means of the finite element method. One key aspect of this approach consists of computing integrals of singular kernels over the discretised domain, usually referred to as potential integrals. Several techniques have been proposed for dealing with such integrals, all of them approximating the results at the expense of reducing the accuracy of the solution. In this paper, we derive analytic formulas for the potential integrals, reducing approximation errors to a minimum. Approach. Based on volume coordinates and Gauss theorems, we obtained parametric expressions for all the element matrices needed in the formulation assuming first order basis functions defined on a tetrahedral mesh. This included solving potential integrals over triangles and tetrahedra, for which we found compact and efficient formulas. Main results. Comparison with numerical quadrature schemes allowed us to test the advantages of the methodology proposed, which were found of great relevance for highly-eccentric sources, as those found in the somatosensory and visual cortices. Moreover, the availability of compact formulas allowed for an efficient implementation of the technique, which resulted in similar computational cost than the simplest numerical scheme. Significance. The analytical subtraction approach is the optimal subtraction-based methodology with regard to accuracy. The computational cost is similar to that obtained with the lowest order numerical integration scheme, making it a competitive option in the field. The technique is highly relevant for improving electromagnetic source imaging results utilising individualised head models and anisotropic electric conductivity fields without imposing impractical mesh requirements.

Presurgical Functional Cortical Mapping Using Electromagnetic Source Imaging.

Preoperative localization of functionally eloquent cortex (functional cortical mapping) is common clinical practice in order to avoid or reduce postoperative morbidity. This review aims at providing a general overview of magnetoencephalography (MEG) and high-density electroencephalography (hdEEG) based methods and their clinical role as compared to common alternatives for functional cortical mapping of (1) verbal language function, (2) sensorimotor cortex, (3) memory, (4) visual, and (5) auditory cortex. We highlight strengths, weaknesses and limitations of these functional cortical mapping modalities based on findings in the recent literature. We also compare their performance relative to other non-invasive functional cortical mapping methods, such as functional Magnetic Resonance Imaging (fMRI), Transcranial Magnetic Stimulation (TMS), and to invasive methods like the intracarotid Amobarbital Test (WADA-Test) or intracranial investigations.

P08-S Combined electromagnetic source imaging in presurgical evaluation

Background To date, source imaging (SI) has been performed using epileptiform discharges (ED) detected by magnetoencephalography (MEG) or electroencephalography (EEG). A few case studies has combined MEG and EEG recordings and performed electromagnetic source imaging (cEMSI). This study tries to elucidate the role of cEMSI in presurgical evaluation. Material and methods A MEG whole-head 306-channel Elekta Neuromag® system, and simultaneous high density EEG (70 electrodes, range 58–80) using a non-magnetic cap (EASYCAP) were recorded in 141 consecutive patients. Fifty patients were operated and had one year follow up. MEG-EEG was inspected for EDs. Signals were analyzed using CURRY 7 Neuroimaging Suite. For each cluster, using the visually detected EDs as templates, automated algorithms scanned the recordings, and detected EDs were visually checked. To improve the signal-to-noise ratio, EDs with similar topography were averaged. Two different inverse solutions were applied: equivalent current dipole (ECD) and a distributed source model (DSM): sLORETA. We performed electric SI, magnetic SI, and cEMSI. All analyses were performed using individual head models from the patients’ MRIs. We calculated the odds ratio (OR) of becoming seizure-free when operation was concordant vs discordant with the localization of the SI. Results Combining both EEG and MEG signals (cEMSI) gave an OR of 5.8 for ECD and 19.2 for DSM. OR of cEMSI using DSM was significantly higher than both ESI (p = 0.02) and MSI (p = 0.03). Conclusion Combined EMSI achieved significantly higher odds ratio for becoming seizure-free compared to electric SI and magnetic SI.

The individuality index: a measure to quantify the degree of inter-individual, spatial variability in intra-cerebral brain electric and metabolic activity.

Contemporary neuroscience research primarily focuses on the identification of brain activation patterns commonly deviant across participant groups or experimental conditions. This approach inherently underestimates potentially meaningful intra- and inter-individual variability present in brain physiological measures. We propose a parameter referred to as 'individuality index (II)' that takes individual variability into account. It quantifies the degree of individual variance of brain activation patterns for different brain regions and participants. IIs can be computed based on intra-cerebral source strength values such as the ones derived from the exact low resolution electromagnetic tomography source localization software. We exemplary estimated IIs for simulated datasets. Our results illustrate how IIs are affected by different spatial activation patterns across participants and quantify their distributional properties. They suggest that the proposed indices can meaningfully quantify inter- and intra-individuality of brain activation patterns. Their application to realistic datasets will allow the identification of (1) those brain regions that show particularly heterogeneous activation patterns, the contribution of which is particularly likely to be underestimated by conventional group statistics, (2) those brain regions that can alternatively be recruited by different participants for the same tasks, and (3) their associations with potentially decisive behavioral variables such as individually applied mental strategy.

Electromagnetic source imaging in presurgical workup of patients with epilepsy: A prospective study.

ObjectiveTo determine the diagnostic accuracy and clinical utility of electromagnetic source imaging (EMSI) in presurgical evaluation of patients with epilepsy.MethodsWe prospectively recorded magnetoencephalography (MEG) simultaneously with EEG and performed EMSI, comprising electric source imaging, magnetic source imaging, and analysis of combined MEG-EEG datasets, using 2 different software packages. As reference standard for irritative zone (IZ) and seizure onset zone (SOZ), we used intracranial recordings and for localization accuracy, outcome 1 year after operation.ResultsWe included 141 consecutive patients. EMSI showed localized epileptiform discharges in 94 patients (67%). Most of the epileptiform discharge clusters (72%) were identified by both modalities, 15% only by EEG, and 14% only by MEG. Agreement was substantial between inverse solutions and moderate between software packages. EMSI provided new information that changed the management plan in 34% of the patients, and these changes were useful in 80%. Depending on the method, EMSI had a concordance of 53% to 89% with IZ and 35% to 73% with SOZ. Localization accuracy of EMSI was between 44% and 57%, which was not significantly different from MRI (49%–76%) and PET (54%–85%). Combined EMSI achieved significantly higher odds ratio compared to electric source imaging and magnetic source imaging.ConclusionEMSI has accuracy similar to established imaging methods and provides clinically useful, new information in 34% of the patients.Classification of evidenceThis study provides Class IV evidence that EMSI had a concordance of 53%–89% and 35%–73% (depending on analysis) for the localization of epileptic focus as compared with intracranial recordings—IZ and SOZ, respectively.

Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer.

Adaptive beamformer methods that have been extensively used for functional brain imaging using EEG/MEG (magnetoencephalography) signals are sensitive to model mismatches. We propose a robust minimum variance beamformer (RMVB) technique, which explicitly incorporates the uncertainty of the lead field matrix into the estimation of spatial-filter weights that are subsequently used to perform the imaging. The uncertainty of the lead field is modeled by ellipsoids in the RMVB method; these hyperellipsoids (ellipsoids in higher dimensions) define regions of uncertainty for a given nominal lead field vector. These ellipsoids are estimated empirically by sampling lead field vectors surrounding each point of the source space, or more generally by building several forward models for the source space. Once these uncertainty regions (ellipsoids) are estimated, they are used to perform the source-imaging task. Computer simulations are conducted to evaluate the performance of the proposed RMVB technique. Our results show that robust beamformers can outperform conventional beamformers in terms of localization error, recovering source dynamics, and estimation of the underlying source extents when uncertainty in the lead field matrix is properly determined and modeled. The RMVB can be substituted for conventional beamformers, especially in applications where source imaging is performed off-line, and computational speed and complexity are not of major concern. A high-quality source imaging can be utilized in various applications, such as determining the epileptogenic zone in medically intractable epilepsy patients or estimating the time course of activity, which is a required step for computing the functional connectivity of brain networks.

T04. Clinical utility of electromagnetic source imaging in the Danish presurgical epilepsy evaluation

Introduction The greatest challenge in epilepsy surgery lies in the presurgical evaluation. On their own, none of the diagnostic methods can identify the epileptogenic zone. Therefore, a multimodal approach is used. Over the last decades, advances in recording techniques and in signal analysis made it possible to estimate the source of the epileptiform discharges recorded by electroencephalography (EEG source imaging: ESI) and magnetoencephalography (MEG source imaging: MSI). This prospective study investigates the role of electromagnetic source imaging (EMSI) as a non-invasive tool to guide the multidisciplinary epilepsy surgery team. Methods MEG (306 channels) and simultaneous high-density EEG was recorded in 85 consecutive patients with refractory focal epilepsy, referred for conventional non-invasive presurgical evaluation. EMSI, comprising of electric, magnetic source imaging and analysis of combined MEG-EEG datasets, using commercially available software (BESA and CURRY) was used. The Danish epilepsy surgery team evaluated the patients first blinded to EMSI and then including the data from EMSI. At both sessions the multidisciplinary team (MDT) determined the presumed localisation of the epileptogenic zone and decided on location of surgery, intracranial registration (ICR) placement, or not offering epilepsy surgery. The clinical utility of EMSI was defined as the proportion of patients in whom EMSI changed the decision of the MDT. A change was defined useful as follows: (a) change from stop to ICR: the ICR localized the source; (b) change in implantation strategy: the electrode(s) implanted based on the EMSI identified the source; and (c) change from implantation to operation: the patient became seizure-free. Results The impact of EMSI on patient management-plan was assessed in 85 patients (50 men) in whom EMSI was part of the decision-making process. The age of these patients was between 10 and 70 years (median: 32 years). Thirty-eight patients were MRI negative; in the remaining patients, there were discordance between MRI and data from long-term video-EEG monitoring (semiology and EEG). EMSI changed the management plan in 28/85 patients (33%). For 16.5% (14/85) of the patients the ICR plan changed (additional structures were implanted). For 7% (6/85) of the patients, in whom neither operation nor ICR was suggested, after discussing the EMSI results, ICR was offered. For 9.4% (8/85) of the patients ICR was suggested prior to EMSI; after EMSI these patients skipped ICR and went directly to operation. Finally, for one patient it was decided not to offer operation, after discussing the EMSI. At one-year follow-up 80% (16/20) of these changes proved to be useful, meaning that the EMSI changes located the seizure onset zone, irritative zone or the patient became seizure free. Conclusion Electromagnetic source imagening provides clinically relevant information that supplements the decision-making process in presurgery evaluation for epilepsy surgery.

Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks

ObjectiveThe goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks. MethodsSystematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis. ResultsSource imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly. ConclusionsWhile iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes. SignificanceThe present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.

Characterization of cortical source generators based on electroencephalography during tonic pain.

ObjectiveThe aim of the present study was to characterize the cortical source generators evoked by experimental tonic pain.MethodsElectroencephalography (EEG) was recorded on two separate days during rest and with immersion of the hand in ice water for 2 minutes (cold pressor test). Exact low-resolution brain electromagnetic tomography source localization was performed in 31 healthy volunteers to characterize the cortical source generators.ResultsReliability was high in all eight frequency bands during rest and cold pressor conditions (intraclass coefficients =0.47–0.83 in the cingulate and insula). Tonic pain increased cortical activities in the delta (1–4 Hz), theta (4–8 Hz), beta1 (12–18 Hz), beta2 (18–24 Hz), beta3 (24–32 Hz), and gamma (32–60 Hz) bands (all P<0.011) in widespread areas mainly in the limbic system, whereas decreased cortical activities were found in cingulate and pre- and postcentral gyri in the alpha2 (10–12 Hz) band (P=0.007). The pain intensity was correlated with cingulate activity in the beta2, beta3, and gamma bands (all P<0.04).ConclusionSource localization of EEG is a reliable method to estimate cortical source generators. Activities in different brain regions, mainly in the limbic system, showed fluctuations in various frequency bands. Cingulate changes were correlated with pain intensity.SignificanceThis method might add information to the objective assessment of the cortical pain response in future experimental pain studies.

Inhibition of retrieval in hypnotic amnesia: dissociation by upper-alpha gating.

Hypnotic amnesia is a functional dissociation from awareness during which information from specific neural processes is unavailable to consciousness. We test the proposal that changes in topographic patterns of cortical oscillations in upper-alpha (10–12 Hz) band selectively inhibit the recall of memories during hypnotic amnesia by blocking availability of locally processed information at specific points in retrieval. Participants were prescreened for high or low hypnotic susceptibility. Following hypnotic induction, participants were presented with a series of 60 face stimuli and were required to identify affective expressions. Participants received a suggestion for amnesia for these faces. They were then presented with a set of 30 old and 30 new faces and identified each as old or new. Amnesia suggestion was lifted and recall tested using the remaining 30 old faces and another 30 new faces. Exact Low Resolution Brain Electromagnetic Tomography source analyses are reported for 64 channel event-related electroencephalogram recorded from highs showing reversible amnesia to old faces. For high-susceptible participants, the amnesia suggestion significantly increased old faces wrongly identified while for low-susceptible participants amnesia suggestion increased the new faces wrongly identified. There were no differences between high- and low-susceptible participants following reversal of the suggestion. For previously seen faces which were wrongly identified, compared to new faces correctly identified, (late) evoked upper-alpha is significantly higher in right BA7 in a region implicated in top-down executive control to assist recall of visual information. Lagged nonlinear connectivity between cortical sources in upper-alpha in the same condition showed significantly increased connectivity between right BA34 (parahippocampal gyrus) and right BAs 7, 20 and 22. Integration between these regions is essential for recall of recent faces. During amnesia, spatial and temporal coordination of upper-alpha appears to suppress integrated functioning of these regions (hence recall). These patterns were absent after reversal of amnesia suggestion.