Semiautomatic interictal electric source localization based on long-term electroencephalographic monitoring: A prospective study.

OBJECTIVE: Electric source imaging (ESI) of interictal epileptiform discharges (IEDs) has shown significant yield in numerous studies; however, its implementation at most centers is labor- and cost-intensive. Semiautomatic ESI analysis (SAEA) has been proposed as an alternative and has previously shown benefit. Computer-assisted automatic spike cluster retrieval, averaging, and source localization are carried out for each cluster and are then reviewed by an expert neurophysiologist, to determine their relevance for the individual case. Here, we examine its yield in a prospective single center study. METHOD: Between 2017 and 2022, 122 patients underwent SAEA. Inclusion criteria for the current study were unifocal epilepsy disorder, epilepsy surgery with curative purpose, and postoperative follow-up of 2 years or more. All patients (N=40) had continuous video-electroencephalographic (EEG) monitoring with 37 scalp electrodes, which underwent SAEA. Forty patients matched our inclusion criteria. RESULTS: Twenty patients required intracranial monitoring; 13 were magnetic resonance imaging (MRI)-negative. Mean duration of analyzed EEG was 4.3 days (±3.1 days), containing a mean of 12 749 detected IEDs (±22 324). The sensitivity, specificity, and accuracy of SAEA for localizing the epileptogenic focus of the entire group were 74.3%, 80%, and 75%, respectively, leading to an odds ratio (OR) of 11.5 to become seizure-free if the source was included in the resection volume (p < .05). In patients with extratemporal lobe epilepsy, our results indicated an accuracy of 68% (OR=11.7). For MRI-negative patients (n = 13) and patients requiring intracranial EEG (n = 20), we found a similarly high accuracy of 84.6% (OR=19) and 75% (OR = 15.9), respectively. SIGNIFICANCE: In this prospective study of SAEA of long-term video-EEG, spanning several days, we found excellent localizing information and a high yield, even in difficult patient groups. This compares favorably to high-density ESI, most likely due to marked improved signal-to-noise ratio of the averaged IEDs. We propose including ESI, or SAEA, in the workup of all patients who are referred for epilepsy surgery.

Detection of interictal epileptiform discharges in an extended scalp EEG array and high-density EEG-A prospective multicenter study.

OBJECTIVES: High counts of averaged interictal epileptiform discharges (IEDs) are key components of accurate interictal electric source imaging (ESI) in patients with focal epilepsy. Automated detections may be time-efficient, but they need to identify the correct IED types. Thus we compared semiautomated and automated detection of IED types in long-term video-EEG (electroencephalography) monitoring (LTM) using an extended scalp EEG array and short-term high-density EEG (hdEEG) with visual detection of IED types and the seizure-onset zone (SOZ). METHODS: We prospectively recruited consecutive patients from four epilepsy centers who underwent both LTM with 40-electrode scalp EEG and short-term hdEEG with 256 electrodes. Only patients with a single circumscribed SOZ in LTM were included. In LTM and hdEEG, IED types were identified visually, semiautomatically and automatically. Concordances of semiautomated and automated detections in LTM and hdEEG, as well as visual detections in hdEEG, were compared against visually detected IED types and the SOZ in LTM. RESULTS: Fifty-two of 62 patients with LTM and hdEEG were included. The most frequent IED types per patient, detected semiautomatically and automatically in LTM and visually in hdEEG, were significantly concordant with the most frequently visually identified IED type in LTM and the SOZ. Semiautomated and automated detections of IED types in hdEEG were significantly concordant with visually identified IED types in LTM, only when IED types with more than 50 detected single IEDs were selected. The threshold of 50 detected IED in hdEEG was reached in half of the patients. For all IED types per patient, agreement between visual and semiautomated detections in LTM was high. SIGNIFICANCE: Semiautomated and automated detections of IED types in LTM show significant agreement with visually detected IED types and the SOZ. In short-term hdEEG, semiautomated detections of IED types are concordant with visually detected IED types and the SOZ in LTM if high IED counts were detected.

Automated ictal EEG source imaging: A retrospective, blinded clinical validation study.

OBJECTIVE: EEG source imaging (ESI) is a validated tool in the multimodal workup of patients with drug resistant focal epilepsy. However, it requires special expertise and it is underutilized. To circumvent this, automated analysis pipelines have been developed and validated for the interictal discharges. In this study, we present the clinical validation of an automated ESI for ictal EEG signals. METHODS: We have developed an automated analysis pipeline of ictal EEG activity, based on spectral analysis in source space, using an individual head model of six tissues. The analysis was done blinded to all other data. As reference standard, we used the concordance with the resected area and one-year postoperative outcome. RESULTS: We analyzed 50 consecutive patients undergoing epilepsy surgery (34 temporal and 16 extra-temporal). Thirty patients (60%) became seizure-free. The accuracy of the automated ESI was 74% (95% confidence interval: 59.66-85.37%). CONCLUSIONS: Automated ictal ESI has a high accuracy for localizing the seizure onset zone. SIGNIFICANCE: Automating the ESI of the ictal EEG signals will facilitate implementation of this tool in the presurgical evaluation.

Ictal EEG source imaging and connectivity to localize the seizure onset zone in extratemporal lobe epilepsy.

PURPOSE: To evaluate the yield of Functional Connectivity (FC) in addition to low-density ictal Electrical Source Imaging (ESI) in extratemporal lobe epilepsy (ETLE), using an automated algorithm for analysis. METHOD: Long-term EEG monitoring of consecutive ETLE patients who underwent surgery was reviewed by epileptologists, and seizure onsets characterized by rhythmical activity were identified. A spectrogram-based algorithm was developed to select objectively the parameters of ESI analysis. Two methods for SOZ localization were compared: i) ESI power, based on LORETA exclusively; ii) ESI + FC, including a Granger causality-based connectivity analysis. Results were determined at a sublobar level. The resection zone, in relation to 1-year follow-up surgical outcome, was considered as reference standard for diagnostic accuracy analyses. RESULTS: Ninety-four seizures from 24 patients were analyzed. At seizure-level, ESI power showed 36 % sensitivity and 72 % specificity (accuracy: 45 %). ESI + FC significantly improved the accuracy, with 52 % sensitivity and 84 % specificity (accuracy: 61 %, p = 0.04). Results of ESI + FC were equally valuable in patients with lateralized or bilateral/generalized visual interpretation of ictal EEG. In a patient level sub-analysis, upon blinded clinical interpretation, ESI + FC showed a correct localization in 67 % of patients and substantial inter-rater agreement (kappa = 0.64), against 27 % achieved by ESI power, with fair inter-rater agreement (kappa = 0.37). CONCLUSION: FC significantly improves SOZ localization compared to ESI solely in ETLE. Ictal ESI + FC could represent a novel option in the armamentarium of presurgical evaluation, aiding also in patients with visually non-localizable scalp ictal EEG. Prospective studies evaluating the clinical added value of automated low-density ictal ESI may be justified.

Automated EEG source imaging: A retrospective, blinded clinical validation study.

OBJECTIVE: To evaluate the accuracy of automated EEG source imaging (ESI) in localizing epileptogenic zone. METHODS: Long-term EEG, recorded with the standard 25-electrode array of the IFCN, from 41 consecutive patients with focal epilepsy who underwent resective surgery, were analyzed blinded to the surgical outcome. The automated analysis comprised spike-detection, clustering and source imaging at the half-rising time and at the peak of each spike-cluster, using individual head-models with six tissue-layers and a distributed source model (sLORETA). The fully automated approach presented ESI of the cluster with the highest number of spikes, at the half-rising time. In addition, a physician involved in the presurgical evaluation of the patients, evaluated the automated ESI results (up to four clusters per patient) in clinical context and selected the dominant cluster and the analysis time-point (semi-automated approach). The reference standard was location of the resected area and outcome one year after operation. RESULTS: Accuracy was 61% (95% CI: 45-76%) for the fully automated approach and 78% (95% CI: 62-89%) for the semi-automated approach. CONCLUSION: Automated ESI has an accuracy similar to previously reported neuroimaging methods. SIGNIFICANCE: Automated ESI will contribute to increased utilization of source imaging in the presurgical evaluation of patients with epilepsy.

EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy.

Electrical source imaging (ESI) from interictal scalp EEG is increasingly validated and used as a valuable tool in the presurgical evaluation of epilepsy as a reflection of the irritative zone. ESI of ictal scalp EEG to localize the seizure onset zone (SOZ) remains challenging. We investigated the value of an approach for ictal imaging using ESI and functional connectivity analysis (FC). Ictal scalp EEG from 111 seizures in 27 patients who had Engel class I outcome at least 1 year following resective surgery was analyzed. For every seizure, an artifact-free epoch close to the seizure onset was selected and ESI using LORETA was applied. In addition, the reconstructed sources underwent FC using the spectrum-weighted Adaptive Directed Transfer Function. This resulted in the estimation of the SOZ in two ways: (i) the source with maximal power after ESI, (ii) the source with the strongest outgoing connections after combined ESI and FC. Next, we calculated the distance between the estimated SOZ and the border of the resected zone (RZ) for both approaches and called this the localization error ((i) LEpow and (ii) LEconn respectively). By comparing LEpow and LEconn, we assessed the added value of FC. The source with maximal power after ESI was inside the RZ (LEpow = 0 mm) in 31% of the seizures and estimated within 10 mm from the border of the RZ (LEpow ≤ 10 mm) in 42%. Using ESI and FC, these numbers increased to 72% for LEconn = 0 mm and 94% for LEconn ≤ 10 mm. FC provided a significant added value to ESI alone (p < 0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy.

Improved Localization of Seizure Onset Zones Using Spatiotemporal Constraints and Time-Varying Source Connectivity.

Presurgical evaluation of brain neural activity is commonly carried out in refractory epilepsy patients to delineate as accurately as possible the seizure onset zone (SOZ) before epilepsy surgery. In practice, any subjective interpretation of electroencephalographic (EEG) recordings is hindered mainly because of the highly stochastic behavior of the epileptic activity. We propose a new method for dynamic source connectivity analysis that aims to accurately localize the seizure onset zones by explicitly including temporal, spectral, and spatial information of the brain neural activity extracted from EEG recordings. In particular, we encode the source nonstationarities in three critical stages of processing: Inverse problem solution, estimation of the time courses extracted from the regions of interest, and connectivity assessment. With the aim to correctly encode all temporal dynamics of the seizure-related neural network, a directed functional connectivity measure is employed to quantify the information flow variations over the time window of interest. Obtained results on simulated and real EEG data confirm that the proposed approach improves the accuracy of SOZ localization.

Spatiotemporal differentiation in auditory and motor regions during auditory phoneme discrimination.

Auditory phoneme discrimination (APD) is supported by both auditory and motor regions through a sensorimotor interface embedded in a fronto-temporo-parietal cortical network. However, the specific spatiotemporal organization of this network during APD with respect to different types of phonemic contrasts is still unclear. Here, we use source reconstruction, applied to event-related potentials in a group of 47 participants, to uncover a potential spatiotemporal differentiation in these brain regions during a passive and active APD task with respect to place of articulation (PoA), voicing and manner of articulation (MoA). Results demonstrate that in an early stage (50-110 ms), auditory, motor and sensorimotor regions elicit more activation during the passive and active APD task with MoA and active APD task with voicing compared to PoA. In a later stage (130-175 ms), the same auditory and motor regions elicit more activation during the APD task with PoA compared to MoA and voicing, yet only in the active condition, implying important timing differences. Degree of attention influences a frontal network during the APD task with PoA, whereas auditory regions are more affected during the APD task with MoA and voicing. Based on these findings, it can be carefully suggested that APD is supported by the integration of early activation of auditory-acoustic properties in superior temporal regions, more perpetuated for MoA and voicing, and later auditory-to-motor integration in sensorimotor areas, more perpetuated for PoA.

Automated long-term EEG analysis to localize the epileptogenic zone.

Objective: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. Methods: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. Results: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. Significance: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus.

Seizure Onset Zone Localization from Ictal High-Density EEG in Refractory Focal Epilepsy.

Epilepsy surgery is the most efficient treatment option for patients with refractory epilepsy. Before surgery, it is of utmost importance to accurately delineate the seizure onset zone (SOZ). Non-invasive EEG is the most used neuroimaging technique to diagnose epilepsy, but it is hard to localize the SOZ from EEG due to its low spatial resolution and because epilepsy is a network disease, with several brain regions becoming active during a seizure. In this work, we propose and validate an approach based on EEG source imaging (ESI) combined with functional connectivity analysis to overcome these problems. We considered both simulations and real data of patients. Ictal epochs of 204-channel EEG and subsets down to 32 channels were analyzed. ESI was done using realistic head models and LORETA was used as inverse technique. The connectivity pattern between the reconstructed sources was calculated, and the source with the highest number of outgoing connections was selected as SOZ. We compared this algorithm with a more straightforward approach, i.e. selecting the source with the highest power after ESI as the SOZ. We found that functional connectivity analysis estimated the SOZ consistently closer to the simulated EZ/RZ than localization based on maximal power. Performance, however, decreased when 128 electrodes or less were used, especially in the realistic data. The results show the added value of functional connectivity analysis for SOZ localization, when the EEG is obtained with a high-density setup. Next to this, the method can potentially be used as objective tool in clinical settings.

EEG Derived Brain Activity Reflects Treatment Response from Vagus Nerve Stimulation in Patients with Epilepsy.

The mechanism of action of vagus nerve stimulation (VNS) is yet to be elucidated. To that end, the effects of VNS on the brain of epileptic patients were studied. Both when VNS was switched "On" and "Off", the brain activity of responders (R, seizure frequency reduction of over 50%) was compared to the brain activity of nonresponders (NR, seizure frequency reduction of less than 50%). Using EEG recordings, a significant increase in P300 amplitude for R and a significant decrease in P300 amplitude for NR were found. We found biomarkers for checking the efficacy of VNS with accuracy up to 94%. The results show that P300 features recorded in nonmidline electrodes are better P300 biomarkers for VNS efficacy than P300 features recorded in midline electrodes. Using source localization and connectivity analyses, the activity of the limbic system, insula and orbitofrontal cortex was found to be dependent on VNS switched "On" versus "Off" or patient group (R versus NR). The results suggest an important role for these areas in the mechanism of action of VNS, although a larger patient study should be done to confirm the findings.

Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization.

Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP) approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i) an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii) an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii) an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time epochs were in the same range as the LORETA and ECD techniques. We found distances smaller than 23 mm, with robust results for all the patients. For the finite difference models, we found that the distances to the resection border for the MSVP inversions of the full spike time epochs were generally smaller compared to the MSVP inversions of the time epochs before the spike peak. The results also suggest that the inversions using the finite difference models resulted in slightly smaller distances to the resection border compared to the spherical models. The results we obtained are promising because the MSVP approach allows to study the network of the estimated source-intensities and allows to characterize the spatial extent of the underlying sources.

The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy.

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.

Multiple sparse volumetric priors for distributed EEG source reconstruction.

We revisit the multiple sparse priors (MSP) algorithm implemented in the statistical parametric mapping software (SPM) for distributed EEG source reconstruction (Friston et al., 2008). In the present implementation, multiple cortical patches are introduced as source priors based on a dipole source space restricted to a cortical surface mesh. In this note, we present a technique to construct volumetric cortical regions to introduce as source priors by restricting the dipole source space to a segmented gray matter layer and using a region growing approach. This extension allows to reconstruct brain structures besides the cortical surface and facilitates the use of more realistic volumetric head models including more layers, such as cerebrospinal fluid (CSF), compared to the standard 3-layered scalp-skull-brain head models. We illustrated the technique with ERP data and anatomical MR images in 12 subjects. Based on the segmented gray matter for each of the subjects, cortical regions were created and introduced as source priors for MSP-inversion assuming two types of head models. The standard 3-layered scalp-skull-brain head models and extended 4-layered head models including CSF. We compared these models with the current implementation by assessing the free energy corresponding with each of the reconstructions using Bayesian model selection for group studies. Strong evidence was found in favor of the volumetric MSP approach compared to the MSP approach based on cortical patches for both types of head models. Overall, the strongest evidence was found in favor of the volumetric MSP reconstructions based on the extended head models including CSF. These results were verified by comparing the reconstructed activity. The use of volumetric cortical regions as source priors is a useful complement to the present implementation as it allows to introduce more complex head models and volumetric source priors in future studies.

Bayesian model selection of template forward models for EEG source reconstruction.

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction.

Influence of skull modeling approaches on EEG source localization.

Electroencephalographic source localization (ESL) relies on an accurate model representing the human head for the computation of the forward solution. In this head model, the skull is of utmost importance due to its complex geometry and low conductivity compared to the other tissues inside the head. We investigated the influence of using different skull modeling approaches on ESL. These approaches, consisting in skull conductivity and geometry modeling simplifications, make use of X-ray computed tomography (CT) and magnetic resonance (MR) images to generate seven different head models. A head model with an accurately segmented skull from CT images, including spongy and compact bone compartments as well as some air-filled cavities, was used as the reference model. EEG simulations were performed for a configuration of 32 and 128 electrodes, and for both noiseless and noisy data. The results show that skull geometry simplifications have a larger effect on ESL than those of the conductivity modeling. This suggests that accurate skull modeling is important in order to achieve reliable results for ESL that are useful in a clinical environment. We recommend the following guidelines to be taken into account for skull modeling in the generation of subject-specific head models: (i) If CT images are available, i.e., if the geometry of the skull and its different tissue types can be accurately segmented, the conductivity should be modeled as isotropic heterogeneous. The spongy bone might be segmented as an erosion of the compact bone; (ii) when only MR images are available, the skull base should be represented as accurately as possible and the conductivity can be modeled as isotropic heterogeneous, segmenting the spongy bone directly from the MR image; (iii) a large number of EEG electrodes should be used to obtain high spatial sampling, which reduces the localization errors at realistic noise levels.