Electroencephalographic source imaging: a prospective study of 152 operated epileptic patients

Use, experience and perspectives of high-density EEG among Italian epilepsy centers: a national survey

INTRODUCTION

High-density EEG (hdEEG) is a validated tool in presurgical evaluation of people with epilepsy. The aim of this national survey is to estimate diffusion and knowledge of hdEEG to develop a network among Italian epilepsy centers.

METHODS

A survey of 16 items (and 15 additional items) was distributed nationwide by email to all members of the Italian League Against Epilepsy and the Italian Society of Clinical Neurophysiology. The data obtained were analyzed using descriptive statistics.

RESULTS

A total of 104 respondents were collected from 85 centers, 82% from the Centre-North of Italy; 27% of the respondents had a hdEEG. The main applications were for epileptogenic focus characterization in the pre-surgical evaluation (35%), biomarker research (35%) and scientific activity (30%). The greatest obstacles to hdEEG were economic resources (35%), acquisition of dedicated personnel (30%) and finding expertise (17%). Dissemination was limited by difficulties in finding expertise and dedicated personnel (74%) more than buying devices (9%); 43% of the respondents have already published hdEEG data, and 91% of centers were available to participate in multicenter hdEEG studies, helping in both pre-processing and analysis. Eighty-nine percent of respondents would be interested in referring patients to centers with established experience for clinical and research purposes.

CONCLUSIONS

In Italy, hdEEG is mainly used in third-level epilepsy centers for research and clinical purposes. HdEEG diffusion is limited not only by costs but also by lack of trained personnel. Italian centers demonstrated a high interest in educational initiatives on hdEEG as well as in clinical and research collaborations.

Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment

For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

OBJECTIVE

To investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy.

METHODS

Description of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR). The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation.

RESULTS

The proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients.

CONCLUSIONS

Numerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data.

SIGNIFICANCE

The proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states

OBJECTIVE

The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work-up of patients with drug-resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI.

METHODS

We studied overnight high-density electroencephalography recordings in focal drug-resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs.

RESULTS

The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal-to-noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid-eye-movement (REM) sleep. Clustering of amplitude-sensitive and -insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05).

INTERPRETATION

IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.

Ictal EEG Source Imaging

SUMMARY

Ictal EEG source imaging (ESI) is an advancing and growing application for presurgical epilepsy evaluation. For far too long, localization of seizures with scalp EEG has continued to rely on visual inspection of tracings arranged in a variety of montages allowing, at best, rough estimates of seizure onset regions. This most critical step is arguably the weakest point in epilepsy localization for surgical decision-making in clinical practice today. This review covers the methods and strategies that have been developed and tested for the performance of ictal ESI. It highlights practical issues and solutions toward sound implementation while covering differing methods to tackle the challenges specific to ictal ESI-noise and artifact reduction, component analysis, and other tools to increase seizure-specific signal for analysis. Further, validation studies to date-those with both high and low density numbers of electrodes-are summarized, providing a glimpse at the relative accuracy of ictal ESI in all types of focal epilepsy patients. Finally, given the added noninvasive information (greater degree of spatial resolution compared with standard ictal EEG review), the role of ictal ESI and its clinical utility in the presurgical evaluation is discussed.

Setting up EEG Source Imaging in Practice

SUMMARY

Adding EEG source imaging to a clinical practice has clear advantages over visual inspection of EEG. This article offers insight on incorporating EEG source imaging into an EEG laboratory and the best practices for producing optimal source analysis results.

Technical Considerations in EEG Source Imaging

SUMMARY

EEG source imaging is an established technique for identifying the origin of interictal and ictal epileptiform discharges in patients with epilepsy, and it is an important tool in neurophysiology research. Accurate and reliable EEG source imaging requires appropriate choices of how the head, skull, and scalp are modeled, and understanding of the different approaches to modeling is important to guide these choices. Similarly, numerous different approaches to modeling the electrical sources within the brain exist, and appropriate understanding of the strengths and limitations of each are essential to obtaining accurate, reliable, and interpretable solutions. This review aims to describe the essential theoretical basis for these head and source models while also discussing the practical implications of each in clinical or research applications.

EEG Source Imaging-Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution

SUMMARY

EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impact of electrode number and distribution across the scalp, the benefit of averaging raw data for signal analysis, and the relevance of modeling different phases of the interictal discharge as it evolves from take-off to peak. We emphasize the importance of recording high signal-to-noise ratio data for reliable source analysis. We argue that the accuracy of modeling cortical sources can be improved using higher electrode counts that include an inferior temporal array, by averaging interictal waveforms rather than limiting ESI to single spike analysis, and by careful interrogation of earlier phase components of these waveforms. No amount of postacquisition signal processing or source modeling sophistication, however, can make up for suboptimally recorded scalp EEG data in a poorly prepared patient.

Interictal Electrical Source Imaging

SUMMARY

Interictal electrical source imaging (ESI) determines the neuronal generators of epileptic activity in EEG occurring outside of seizures. It uses computational models to take anatomic and neuronal characteristics of the individual patient into account. The presented article provides an overview of application and clinical value of interictal ESI in patients with pharmacoresistant focal epilepsies undergoing evaluation for surgery. Neurophysiological constraints of interictal data are discussed and technical considerations are summarized. Typical indications are covered as well as issues of integration into clinical routine. Finally, an outlook on novel markers of epilepsy for interictal source analysis is presented. Interictal ESI provides diagnostic performance on par with other established methods, such as MRI, PET, or SPECT. Although its accuracy benefits from high-density recordings, it provides valuable information already when applied to EEG with only a limited number of electrodes with complete coverage. Novel oscillatory markers and the integration of frequency coupling and connectivity may further improve accuracy and efficiency.

EEG Source Imaging in Presurgical Evaluations

SUMMARY

Presurgical evaluations to plan intracranial EEG implantations or surgical therapies at most epilepsy centers in the United States currently depend on the visual inspection of EEG traces. Such analysis is inadequate and does not exploit all the localizing information contained in scalp EEG. Various types of EEG source modeling or imaging can provide sublobar localization of spike and seizure sources in the brain, and the software to do this with typical long-term monitoring EEG data are available to all epilepsy centers. This article reviews the fundamentals of EEG voltage fields that are used in EEG source imaging, the strengths and weakness of dipole and current density source models, the clinical situations where EEG source imaging is most useful, and the particular strengths of EEG source imaging for various cortical areas where spike/seizure sources are likely.

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES)

Objective.Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to ESI, particularly in inpatient and intensive care settings.Approach.To address this challenge, we present a portable and affordable electrode digitization method using the 3D scanning feature in modern iPhone models. This technique combines iPhone scanning with semi-automated image processing using point-cloud electrode selection (PC-ES), a custom MATLAB desktop application. We compare iPhone electrode localization to state-of-the-art photogrammetry technology in a human study with over 6000 electrodes labeled using each method. We also characterize the performance of PC-ES with respect to head location and examine the relative impact of different algorithm parameters.Main Results.The median electrode position variation across reviewers was 1.50 mm for PC-ES scanning and 0.53 mm for photogrammetry, and the average median distance between PC-ES and photogrammetry electrodes was 3.4 mm. These metrics demonstrate comparable performance of iPhone/PC-ES scanning to currently available technology and sufficient accuracy for ESI.Significance.Low cost, portable electrode localization using iPhone scanning removes barriers to ESI in inpatient, outpatient, and remote care settings. While PC-ES has current limitations in user bias and processing time, we anticipate these will improve with software automation techniques as well as future developments in iPhone 3D scanning technology.

EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

Effects of inverse methods and spike phases on interictal high-density EEG source reconstruction

OBJECTIVE

To determine the effect of inverse methods and timepoints of interictal epileptic discharges (IEDs) used for high-density electric source imaging (hd-ESI) in pharmacoresistant focal epilepsies.

METHODS

We retrospectively evaluated the hd-ESI and [18F]fluorodeoxyglucose positron emission tomography (18FDG-PET) of 21 operated patients with pharmacoresistant focal epilepsy (Engel I). Volumetric hd-ESI was performed with three different inverse methods such as the inverse solution linearly constrained minimum variance (LCMV, a beamformer method), standardized low resolution electromagnetic tomography (sLORETA) and weighted minimum-norm estimation (wMNE) and at different IED phases. Hd-ESI accuracy was determined by volumetric overlap and distance between hd-ESI source maximum, as well as 18FDG-PET hypometabolic region relative to the resection zone (RZ).

RESULTS

In our cohort, the shortest distances and greatest volumetric overlaps to the RZ were found in the half-rise and peak-phase for all inverse methods. The distance to the RZ was not different between the centroid of the clinical hypothesis-based cluster and the source maximum in peak-phase. However, the distance of the hypothesis-based cluster was significantly shorter compared to the cluster selected by the smallest p-value.

CONCLUSIONS

Hd-ESI provides the greatest accuracy in determining the RZ at the IED half-rise and peak-phase for all applied inverse methods, whereby sLORETA and LCMV were equally accurate.

SIGNIFICANCE

Our results offer guidance in selecting inverse methods and IED phases for hd-ESI, compare the performance of hd-ESI and 18FDG-PET and encourage future studies in investigating the relationship between interictal ESI and 18FDG-PET hypometabolism.

Targeted density electrode placement achieves high concordance with traditional high-density EEG for electrical source imaging in epilepsy

OBJECTIVE

High-density (HD) electroencephalography (EEG) is increasingly used in presurgical epilepsy evaluation, but it is demanding in time and resources. To overcome these issues, we compared EEG source imaging (ESI) solutions with a targeted density and HD-EEG montage.

METHODS

HD-EEGs from patients undergoing presurgical evaluation were analyzed. A low-density recording was created by selecting the 25 electrodes of a standard montage from the 83 electrodes of the HD-EEG and adding 8-11 electrodes around the electrode with the highest amplitude interictal epileptiform discharges. The ESI solution from this "targeted" montage was compared to that from the HD-EEG using the distance between peak vertices, sublobar concordance and a qualitative similarity measure.

RESULTS

Fifty-eight foci of forty-three patients were included. The median distance between the peak vertices of the two montages was 13.2 mm, irrespective of focus' location. Tangential generators (n = 5/58) showed a higher distance than radial generators (p = 0.04). We found sublobar concordance in 54/58 of the foci (93%). Map similarity, assessed by an epileptologist, had a median score of 4/5.

CONCLUSIONS

ESI solutions obtained from a targeted density montage show high concordance with those calculated from HD-EEG.

SIGNIFICANCE

Requiring significantly fewer electrodes, targeted density EEG allows obtaining similar ESI solutions as traditional HD-EEG montage.

Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n=29), multiple sustained thermal stimuli were administered to the forearm, with the first, second, and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures, respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45 ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n=10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

Ictal EEG Source Imaging With Supplemental Electrodes

INTRODUCTION

Noninvasive brain imaging tests play a major role in guiding decision-making and the usage of invasive, costly intracranial electroencephalogram (ICEEG) in the presurgical epilepsy evaluation. This study prospectively examined the concordance in localization between ictal EEG source imaging (ESI) and ICEEG as a reference standard.

METHODS

Between August 2014 and April 2019, patients during video monitoring with scalp EEG were screened for those with intractable focal epilepsy believed to be amenable to surgical treatment. Additional 10-10 electrodes (total = 31-38 per patient, "31+") were placed over suspected regions of seizure onset in 104 patients. Of 42 patients requiring ICEEG, 30 (mean age 30, range 19-59) had sufficiently localized subsequent intracranial studies to allow comparison of localization between tests. ESI was performed using realistic forward boundary element models used in dipole and distributed source analyses.

RESULTS

At least partial sublobar concordance between ESI and ICEEG solutions was obtained in 97% of cases, with 73% achieving complete agreement. Median Euclidean distances between ESI and ICEEG solutions ranged from 25 to 30 mm (dipole) and 23 to 38 mm (distributed source). The latter was significantly more accurate with 31+ compared with 21 electrodes (P < 0.01). A difference of ≤25 mm was present in two thirds of the cases. No significant difference was found between dipole and distributed source analyses.

CONCLUSIONS

A practical method of ictal ESI (nonuniform placement of 31-38 electrodes) yields high accuracy for seizure localization in epilepsy surgery candidates. These results support routine clinical application of ESI in the presurgical evaluation.

Aging affects the phase coherence between spontaneous oscillations in brain oxygenation and neural activity

The risk of neurodegenerative disorders increases with age, due to reduced vascular nutrition and impaired neural function. However, the interactions between cardiovascular dynamics and neural activity, and how these interactions evolve in healthy aging, are not well understood. Here, the interactions are studied by assessment of the phase coherence between spontaneous oscillations in cerebral oxygenation measured by fNIRS, the electrical activity of the brain measured by EEG, and cardiovascular functions extracted from ECG and respiration effort, all simultaneously recorded. Signals measured at rest in 21 younger participants (31.1 ± 6.9 years) and 24 older participants (64.9 ± 6.9 years) were analysed by wavelet transform, wavelet phase coherence and ridge extraction for frequencies between 0.007 and 4 Hz. Coherence between the neural and oxygenation oscillations at ∼ 0.1 Hz is significantly reduced in the older adults in 46/176 fNIRS-EEG probe combinations. This reduction in coherence cannot be accounted for in terms of reduced power, thus indicating that neurovascular interactions change with age. The approach presented promises a noninvasive means of evaluating the efficiency of the neurovascular unit in aging and disease.

Feasibility of high-density electric source imaging in the presurgical workflow: Effect of number of spikes and automated spike detection

OBJECTIVE

Presurgical high-density electric source imaging (hdESI) of interictal epileptic discharges (IEDs) is only used by few epilepsy centers. One obstacle is the time-consuming workflow both for recording as well as for visual review. Therefore, we analyzed the effect of (a) an automated IED detection and (b) the number of IEDs on the accuracy of hdESI and time-effectiveness.

METHODS

In 22 patients with pharmacoresistant focal epilepsy receiving epilepsy surgery (Engel 1) we retrospectively detected IEDs both visually and semi-automatically using the EEG analysis software Persyst in 256-channel EEGs. The amount of IEDs, the Euclidean distance between hdESI maximum and resection zone, and the operator time were compared. Additionally, we evaluated the intra-individual effect of IED quantity on the distance between hdESI maximum of all IEDs and hdESI maximum when only a reduced amount of IEDs were included.

RESULTS

There was no significant difference in the number of IEDs between visually versus semi-automatically marked IEDs (74 ± 56 IEDs/patient vs 116 ± 115 IEDs/patient). The detection method of the IEDs had no significant effect on the mean distances between resection zone and hdESI maximum (visual: 26.07 ± 31.12 mm vs semi-automated: 33.6 ± 34.75 mm). However, the mean time needed to review the full datasets semi-automatically was shorter by 275 ± 46 min (305 ± 72 min vs 30 ± 26 min, P < 0.001). The distance between hdESI of the full versus reduced amount of IEDs of the same patient was smaller than 1 cm when at least a mean of 33 IEDs were analyzed. There was a significantly shorter intraindividual distance between resection zone and hdESI maximum when 30 IEDs were analyzed as compared to the analysis of only 10 IEDs (P < 0.001).

SIGNIFICANCE

Semi-automatized processing and limiting the amount of IEDs analyzed (~30-40 IEDs per cluster) appear to be time-saving clinical tools to increase the practicability of hdESI in the presurgical work-up.

Integration of interictal EEG source localization in presurgical epilepsy evaluation - A single-center prospective study

OBJECTIVE

To investigate cost in working hours for initial integration of interictal EEG source localization (ESL) into clinical practice of a tertiary epilepsy center, and to examine concordance of results obtained with three different ESL pipelines.

METHODS

This prospective study covered the first year of using ESL in the Epilepsy-Center Berlin-Brandenburg. Patients aged ≥14 years with drug-resistant focal epilepsy referred for noninvasive presurgical evaluation were included. Interictal ESL was based on low-density EEG and individual head models. Source maxima were obtained from two freely available software packages and one commercial provider. One physician and computer scientist documented their working hours for setting up and processing ESL. Additionally, a survey was conducted among epilepsy centers in Germany to assess the current role of ESL in presurgical evaluation.

RESULTS

Of 40 patients included, 22 (55%) had enough interictal spikes for ESL. The physician's working times decreased from median 4.7 hours [interquartile range 3.9-6.4] in the first third of cases to 2.0 hours [1.9-2.4] in the remaining two thirds; P < 0.01. In addition, computer scientist and physician spent a total of 35.5 and 33.0 working hours on setting up the digital infrastructure, and on training and testing. Sublobar agreement between all three pipelines was 20%, mean measurement of agreement (kappa) 0.13. Finally, the survey revealed that 53% of epilepsy centers in Germany currently use ESL for presurgical evaluation.

SIGNIFICANCE

This study provides information regarding expected effort and costs for integration of ESL into an epilepsy surgery program. Low result agreement across different ESL pipelines calls for further standardization.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Alterations in cortical excitability during pain: A combined TMS-EEG Study

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n = 29), multiple sustained thermal stimuli were administered to the forearm, with the first, second and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n = 10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

Dynamic networks differentiate the language ability of children with cochlear implants

Background

Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill.

Methods

Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children.

Findings

Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy.

Interpretation

Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.

An in-vivo validation of ESI methods with focal sources

Electrophysiological source imaging (ESI) aims at reconstructing the precise origin of brain activity from measurements of the electric field on the scalp. Across laboratories/research centers/hospitals, ESI is performed with different methods, partly due to the ill-posedness of the underlying mathematical problem. However, it is difficult to find systematic comparisons involving a wide variety of methods. Further, existing comparisons rarely take into account the variability of the results with respect to the input parameters. Finally, comparisons are typically performed using either synthetic data, or in-vivo data where the ground-truth is only roughly known. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are precisely known. We compare ten different ESI methods, using their implementation in the MNE-Python package: MNE, dSPM, LORETA, sLORETA, eLORETA, LCMV beamformers, irMxNE, Gamma Map, SESAME and dipole fitting. We perform comparisons under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of such parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with most accurate methods hitting an average localization error of 1.2 cm and outperforming least accurate ones erring by 2.5 cm. As expected, dipolar and sparsity-promoting methods tend to outperform distributed methods. For several distributed methods, the best regularization parameter turned out to be the one in principle associated with low SNR, despite the high SNR of the available dataset. Depth weighting played no role for two out of the six methods implementing it. Sensitivity to input parameters varied widely between methods. While one would expect high variability being associated with low localization error at the best solution, this is not always the case, with some methods producing highly variable results and high localization error, and other methods producing stable results with low localization error. In particular, recent dipolar and sparsity-promoting methods provide significantly better results than older distributed methods. As we repeated the tests with "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings, we observed little impact of the number of channels on localization accuracy; however, for distributed methods denser montages provide smaller spatial dispersion. Overall findings confirm that EEG is a reliable technique for localization of point sources and therefore reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

Clinical added value of interictal automated electrical source imaging in the presurgical evaluation of MRI-negative epilepsy: A real-life experience in 29 consecutive patients

OBJECTIVE

During the presurgical evaluation, manual electrical source imaging (ESI) provides clinically useful information in one-third of the patients but it is time-consuming and requires specific expertise. This prospective study aims to assess the clinical added value of a fully automated ESI analysis in a cohort of patients with MRI-negative epilepsy and describe its diagnostic performance, by evaluating sublobar concordance with stereo-electroencephalography (SEEG) results and surgical resection and outcome.

METHODS

All consecutive patients referred to the Center for Refractory Epilepsy (CRE) of St-Luc University Hospital (Brussels, Belgium) for presurgical evaluation between 15/01/2019 and 31/12/2020 meeting the inclusion criteria, were recruited to the study. Interictal ESI was realized on low-density long-term EEG monitoring (LD-ESI) and, whenever available, high-density EEG (HD-ESI), using a fully automated analysis (Epilog PreOp, Epilog NV, Ghent, Belgium). The multidisciplinary team (MDT) was asked to formulate hypotheses about the epileptogenic zone (EZ) location at sublobar level and make a decision on further management for each patient at two distinct moments: i) blinded to ESI and ii) after the presentation and clinical interpretation of ESI. Results leading to a change in clinical management were considered contributive. Patients were followed up to assess whether these changes lead to concordant results on stereo-EEG (SEEG) or successful epilepsy surgery.

RESULTS

Data from all included 29 patients were analyzed. ESI led to a change in the management plan in 12/29 patients (41%). In 9/12 (75%), modifications were related to a change in the plan of the invasive recording. In 8/9 patients, invasive recording was performed. In 6/8 (75%), the intracranial EEG recording confirmed the localization of the ESI at a sublobar level. So far, 5/12 patients, for whom the management plan was changed after ESI, were operated on and have at least one-year postoperative follow-up. In all cases, the EZ identified by ESI was included in the resection zone. Among these patients, 4/5 (80%) are seizure-free (ILAE 1) and one patient experienced a seizure reduction of more than 50% (ILAE 4).

CONCLUSIONS

In this single-center prospective study, we demonstrated the added value of automated ESI in the presurgical evaluation of MRI-negative cases, especially in helping to plan the implantation of depth electrodes for SEEG, provided that ESI results are integrated into the whole multimodal evaluation and clinically interpreted.

Validating EEG source imaging using intracranial electrical stimulation

Electrical source imaging is used in presurgical epilepsy evaluation and in cognitive neurosciences to localize neuronal sources of brain potentials recorded on EEG. This study evaluates the spatial accuracy of electrical source imaging for known sources, using electrical stimulation potentials recorded on simultaneous stereo-EEG and 37-electrode scalp EEG, and identifies factors determining the localization error. In 11 patients undergoing simultaneous stereo-EEG and 37-electrode scalp EEG recordings, sequential series of 99-110 biphasic pulses (2 ms pulse width) were applied by bipolar electrical stimulation on adjacent contacts of implanted stereo-EEG electrodes. The scalp EEG correlates of stimulation potentials were recorded with a sampling rate of 30 kHz. Electrical source imaging of averaged stimulation potentials was calculated utilizing a dipole source model of peak stimulation potentials based on individual four-compartment finite element method head models with various skull conductivities (range from 0.0413 to 0.001 S/m). Fitted dipoles with a goodness of fit of ≥80% were included in the analysis. The localization error was calculated using the Euclidean distance between the estimated dipoles and the centre point of adjacent stimulating contacts. A total of 3619 stimulation locations, respectively, dipole localizations, were included in the evaluation. Mean localization errors ranged from 10.3 to 26 mm, depending on source depth and selected skull conductivity. The mean localization error increased with an increase in source depth (r(3617) = [0.19], P = 0.000) and decreased with an increase in skull conductivity (r(3617) = [-0.26], P = 0.000). High skull conductivities (0.0413-0.0118 S/m) yielded significantly lower localization errors for all source depths. For superficial sources (<20 mm from the inner skull), all skull conductivities yielded insignificantly different localization errors. However, for deeper sources, in particular >40 mm, high skull conductivities of 0.0413 and 0.0206 S/m yielded significantly lower localization errors. In relation to stimulation locations, the majority of estimated dipoles moved outward-forward-downward to inward-forward-downward with a decrease in source depth and an increase in skull conductivity. Multivariate analysis revealed that an increase in source depth, number of skull holes and white matter volume, while a decrease in skull conductivity independently led to higher localization error. This evaluation of electrical source imaging accuracy using artificial patterns with a high signal-to-noise ratio supports its application in presurgical epilepsy evaluation and cognitive neurosciences. In our artificial potential model, optimizing the selected skull conductivity minimized the localization error. Future studies should examine if this accounts for true neural signals.

MEG Node Degree for Focus Localization: Comparison with Invasive EEG

Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients. Resting state data were subjected to whole brain, all-to-all connectivity analysis using the imaginary part of coherence. Graphs were described using parcellated ND. SOZ localization was investigated on a lobar and sublobar level. On a lobar level, all frequency bands except alpha showed significantly higher maximal ND (mND) values inside the SOZ compared to outside (ratios 1.11-1.20, alpha 1.02). Area-under-the-curve (AUC) was 0.67-0.78 for all expected alpha (0.44, ns). On a sublobar level, mND inside the SOZ was higher for all frequency bands (1.13-1.38, AUC 0.58-0.78) except gamma (1.02). MEG ND is significantly related to SOZ in delta, theta and beta bands. ND may provide new localization tools for presurgical evaluation of epilepsy surgery.

Estimating neural activity from visual areas using functionally defined EEG templates

Electroencephalography (EEG) is a common and inexpensive method to record neural activity in humans. However, it lacks spatial resolution making it difficult to determine which areas of the brain are responsible for the observed EEG response. Here we present a new easy-to-use method that relies on EEG topographical templates. Using MRI and fMRI scans of 50 participants, we simulated how the activity in each visual area appears on the scalp and averaged this signal to produce functionally defined EEG templates. Once created, these templates can be used to estimate how much each visual area contributes to the observed EEG activity. We tested this method on extensive simulations and on real data. The proposed procedure is as good as bespoke individual source localization methods, robust to a wide range of factors, and has several strengths. First, because it does not rely on individual brain scans, it is inexpensive and can be used on any EEG data set, past or present. Second, the results are readily interpretable in terms of functional brain regions and can be compared across neuroimaging techniques. Finally, this method is easy to understand, simple to use and expandable to other brain sources.

Spike-microstates correlate with interictal epileptogenic discharges: a marker for hidden epileptic activity

Objectively estimating disease severity and treatment success is a main problem in outpatient managing of epilepsy. Self-reported seizures diaries are well-known to underestimate the actual seizure count, and repeated EEGs might not show interictal epileptiform discharges (IEDs), although patients suffer from seizures. In this prospective study, we investigate the potential of microstate analysis to monitor epilepsy patients independently of their IED count. From our databank of candidates for epilepsy surgery, we included 18 patients who underwent controlled resting EEG sessions (with eyes closed, 30 min), at around the same time of the day, during at least four days (range: 4-8 days; mean: 5). Nine patients with temporal foci, six with extratemporal foci, and three with generalized epilepsy were included. Each patient's IEDs were marked and the topographic voltage maps of the IED peaks were averaged, and an individual average spike topography (AST) was created. The AST was then backfitted to each timepoint of the whole EEG resulting in the Spike-Microstate (SMS). The presence of the SMS in the residual EEG outside of the short IEDs epochs was determined for each recording session in each patient and correlated with the occurrence of the IEDs across all recording session, as well as with the drug charge of each day. Overall, SMS was much more represented in the routine EEG than the IEDs: they were identified 262 times more often than IEDs. The SMS time coverage correlated significantly with the IED occurrence rate (rho = 0.56; P < 0.001). If only patients with focal epilepsy were considered, this correlation was even higher rho = 0.69 (P < 0.001). Drug charge per day did not correlate with SMS. In this proof-of-concept study, the time coverage of SMS correlated strongly with the occurrence rate of the IEDs, they can be retrieved in the scalp EEG at a much higher occurrence rate. We conclude that SMS, once obtained for a given patient, are a more abundant marker of hidden epileptic activity than IEDs, in particular in focal epilepsy, and can be used also in absence of IEDs. Future larger studies are needed to verify its potential as monitoring tool and to determine cut-off values when drug protection becomes imperfect.

Interictal localization of the epileptogenic zone: Utilizing the observed resonance behavior in the spectral band of surrounding inhibition

Introduction

The gold standard for identification of the epileptogenic zone (EZ) continues to be the visual inspection of electrographic changes around seizures' onset by experienced electroencephalography (EEG) readers. Development of an epileptogenic focus localization tool that can delineate the EZ from analysis of interictal (seizure-free) periods is still an open question of great significance for improved diagnosis (e.g., presurgical evaluation) and treatment of epilepsy (e.g., surgical outcome).

Methods

We developed an EZ interictal localization algorithm (EZILA) based on novel analysis of intracranial EEG (iEEG) using a univariate periodogram-type power measure, a straight-forward ranking approach, a robust dimensional reduction method and a clustering technique. Ten patients with temporal and extra temporal lobe epilepsies, and matching the inclusion criteria of having iEEG recordings at the epilepsy monitoring unit (EMU) and being Engel Class I ≥12 months post-surgery, were recruited in this study.

Results

In a nested k-fold cross validation statistical framework, EZILA assigned the highest score to iEEG channels within the EZ in all patients (10/10) during the first hour of the iEEG recordings and up to their first typical clinical seizure in the EMU (i.e., early interictal period). To further validate EZILA's performance, data from two new (Engel Class I) patients were analyzed in a double-blinded fashion; the EZILA successfully localized iEEG channels within the EZ from interictal iEEG in both patients.

Discussion

Out of the sampled brain regions, iEEG channels in the EZ were most frequently and maximally active in seizure-free (interictal) periods across patients in specific narrow gamma frequency band (∼60-80 Hz), which we have termed focal frequency band (FFB). These findings are consistent with the hypothesis that the EZ may interictally be regulated (controlled) by surrounding inhibitory neurons with resonance characteristics within this narrow gamma band.

A review of the holy Quran listening and its neural correlation for its potential as a psycho-spiritual therapy

Since its revelation over 14 centuries ago, the Holy Quran is considered as scriptural divine words of Islam, and it is believed to promote psycho-spiritual therapeutic benefits to its reciter and/or listener. In this context, the listening of rhythmic Quranic verses among Muslims is often viewed as a form of unconventional melodic vocals, with accompanied anecdotal claims of the 'Quranic chills' pleasing effect. However, compared to music, rhythm, and meditation therapy, information on the neural basis of the anecdotal healing effects of the Quran remain largely unexplored. Current studies in this area took the leads from the low-frequency neuronal oscillations (i.e., alpha and theta) as the neural correlates, mainly using electroencephalography (EEG) and/or magnetoencephalography (MEG). In this narrative review, we present and discuss recent work related to these neural correlates and highlight several methodical issues and propose recommendations to progress this emerging transdisciplinary research. Collectively, evidence suggests that listening to rhythmic Quranic verses activates similar brain regions and elicits comparable therapeutic effects reported in music and rhythmic therapy. Notwithstanding, further research are warranted with more concise and standardized study designs to substantiate these findings, and opens avenue for the listening to Quranic verses as an effective complementary psycho-spiritual therapy.

Source localization of ictal SEEG to predict postoperative seizure outcome

OBJECTIVE

Stereo-electroencephalography (SEEG) is inherently-three-dimensional and can be modeled using source localization. This study aimed to assess the validity of ictal SEEG source localization.

METHODS

The dominant frequency at ictal onset was used for source localization in the time and frequency domains using rotating dipoles and current density maps. Validity was assessed by concordance with the epileptologist-defined seizure onset zone (conventional SOZ) and the surgical treatment volume (TV) of seizure-free versus non-seizure-free patients.

RESULTS

Source localization was performed on 68 seizures from 27 patients. Median distance to nearest contact in the conventional SOZ was 7 (IQR 6-12) mm for time-domain dipoles. Current density predicted ictal activity with up to 86 % (60-87 %) accuracy. Distance from time-domain dipoles to the TV was smaller (P = 0.045) in seizure-free (2 [0-4] mm) versus non-seizure-free (12 [2-17] mm) patients, and predicted surgical outcome with 91 % sensitivity and 63 % specificity. Removing near-field data from contacts within the TV negated outcome prediction (P = 0.51).

CONCLUSIONS

Source localization of SEEG accurately mapped ictal onset compared with conventional interpretation. Proximity of dipoles to the TV predicted seizure outcome when near-field recordings were analyzed.

SIGNIFICANCE

Ictal SEEG source localization is useful in corroborating the epileptogenic zone, assuming near-field recordings are obtained.

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.

From theory to practical fundamentals of electroencephalographic source imaging in localizing the epileptogenic zone

With continued advancement in computational technologies, the analysis of electroencephalography (EEG) has shifted from pure visual analysis to a noninvasive computational technique called EEG source imaging (ESI), which involves mathematical modeling of dipolar and distributed sources of a given scalp EEG pattern. ESI is a noninvasive phase I test for presurgical localization of the seizure onset zone in focal epilepsy. It is a relatively inexpensive modality, as it leverages scalp EEG and magnetic resonance imaging (MRI) data already collected typically during presurgical evaluation. With an adequate number of electrodes and combined with patient-specific MRI-based head models, ESI has proven to be a valuable and accurate clinical diagnostic tool for localizing the epileptogenic zone. Despite its advantages, however, ESI is routinely used at only a minority of epilepsy centers. This paper reviews the current evidence and practical fundamentals for using ESI of interictal and ictal epileptic activity during the presurgical evaluation of drug-resistant patients. We identify common errors in processing and interpreting ESI studies, describe the differences in approach needed for localizing interictal and ictal EEG discharges through practical examples, and describe best practices for optimizing the diagnostic information available from these studies.

Patient-specific solution of the electrocorticography forward problem in deforming brain

Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.

Electric Source Imaging in Presurgical Evaluation of Epilepsy: An Inter-Analyser Agreement Study

Electric source imaging (ESI) estimates the cortical generator of the electroencephalography (EEG) signals recorded with scalp electrodes. ESI has gained increasing interest for the presurgical evaluation of patients with drug-resistant focal epilepsy. In spite of a standardised analysis pipeline, several aspects tailored to the individual patient involve subjective decisions of the expert performing the analysis, such as the selection of the analysed signals (interictal epileptiform discharges and seizures, identification of the onset epoch and time-point of the analysis). Our goal was to investigate the inter-analyser agreement of ESI in presurgical evaluations of epilepsy, using the same software and analysis pipeline. Six experts, of whom five had no previous experience in ESI, independently performed interictal and ictal ESI of 25 consecutive patients (17 temporal, 8 extratemporal) who underwent presurgical evaluation. The overall agreement among experts for the ESI methods was substantial (AC1 = 0.65; 95% CI: 0.59–0.71), and there was no significant difference between the methods. Our results suggest that using a standardised analysis pipeline, newly trained experts reach similar ESI solutions, calling for more standardisation in this emerging clinical application in neuroimaging.

Presurgical accuracy of dipole clustering in MRI-negative pediatric patients with epilepsy: Validation against intracranial EEG and resection

OBJECTIVE

To assess the utility of interictal magnetic and electric source imaging (MSI and ESI) using dipole clustering in magnetic resonance imaging (MRI)-negative patients with drug resistant epilepsy (DRE).

METHODS

We localized spikes in low-density (LD-EEG) and high-density (HD-EEG) electroencephalography as well as magnetoencephalography (MEG) recordings using dipoles from 11 pediatric patients. We computed each dipole's level of clustering and used it to discriminate between clustered and scattered dipoles. For each dipole, we computed the distance from seizure onset zone (SOZ) and irritative zone (IZ) defined by intracranial EEG. Finally, we assessed whether dipoles proximity to resection was predictive of outcome.

RESULTS

LD-EEG had lower clusterness compared to HD-EEG and MEG (p < 0.05). For all modalities, clustered dipoles showed higher proximity to SOZ and IZ than scattered (p < 0.001). Resection percentage was higher in optimal vs. suboptimal outcome patients (p < 0.001); their proximity to resection was correlated to outcome (p < 0.001). No difference in resection percentage was seen for scattered dipoles between groups.

CONCLUSION

MSI and ESI dipole clustering helps to localize the SOZ and IZ and facilitate the prognostic assessment of MRI-negative patients with DRE.

SIGNIFICANCE

Assessing the MSI and ESI clustering allows recognizing epileptogenic areas whose removal is associated with optimal outcome.

Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics

Electrophysiological source imaging (ESI) is an indispensable tool for noninvasively studying brain function and dysfunction. The moderate to low spatial resolution of electroencephalography/magnetoencephalography recordings has been a major hindrance to their wide utility in the field. Although this challenge is mitigated significantly by ESI techniques, it is difficult for individuals without relevant training to select and optimize hyperparameters for these ESI solvers. We propose a deep learning–based source imaging methodology that incorporates current advances in biophysical computational models and neural networks into the ESI framework, and it requires minimal user intervention after the model is trained. Our work promises to enable precise and robust high-resolution spatiotemporal functional brain imaging for a variety of neuroscience research studies and clinical applications.

Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning–based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF’s capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.

Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization

High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on source localization for specific sources and specific electrode configurations. The electrodes for these configurations are often manually selected to uniformly cover the entire head, going from 32 to 128 electrodes, but electrode configurations are not often selected according to their contribution to estimation accuracy. In this work, an optimization-based study is proposed to determine the minimum number of electrodes that can be used and to identify the optimal combinations of electrodes that can retain the localization accuracy of HD-EEG reconstructions. This optimization approach incorporates scalp landmark positions of widely used EEG montages. In this way, a systematic search for the minimum electrode subset is performed for single- and multiple-source localization problems. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with source reconstruction methods is used to formulate a multi-objective optimization problem that concurrently minimizes (1) the localization error for each source and (2) the number of required EEG electrodes. The method can be used for evaluating the source localization quality of low-density EEG systems (e.g. consumer-grade wearable EEG). We performed an evaluation over synthetic and real EEG datasets with known ground-truth. The experimental results show that optimal subsets with 6 electrodes can attain an equal or better accuracy than HD-EEG (with more than 200 channels) for a single source case. This happened when reconstructing a particular brain activity in more than 88% of the cases in synthetic signals and 63% in real signals, and in more than 88% and 73% of cases when considering optimal combinations with 8 channels. For a multiple-source case of three sources (only with synthetic signals), it was found that optimized combinations of 8, 12 and 16 electrodes attained an equal or better accuracy than HD-EEG with 231 electrodes in at least 58%, 76%, and 82% of cases respectively. Additionally, for such electrode numbers, lower mean errors and standard deviations than with 231 electrodes were obtained.

High density electric source imaging in childhood-onset epilepsy due to focal cortical dysplasia

•

ESI with 64 electrodes allows accurate localization of the epileptogenic area in patients with FCD.

•

Concordant results with other imaging techniques is associated with excellent outcome.

•

ESI should become a standard imaging tool in pediatric surgical candidates with FCD.

Objective

The goal of this study was to investigate the diagnostic utility of electric source imaging (ESI) in the presurgical evaluation of children with focal cortical dysplasia (FCD) and to compare it with other imaging techniques.

Methods

Twenty patients with epilepsy onset before 18 years, surgically treated focal epilepsy with a minimal follow-up of 2 years, and histologically proven FCD were retrospectively selected. All patients underwent MRI, positron emission tomography (PET), and 16 patients also had ictal single-photon emission computed tomography (iSPECT). ESI, using EEG with 64 electrodes or more (HD-ESI), was performed in all 20 patients. We determined sensitivity, specificity and accuracy of ESI, and compared its yield to that of other imaging techniques.

Results

Twelve patients were seizure-free post-operatively (60%). Among all patients, highest localization accuracy (80%) was obtained with ESI, followed by PET and iSPECT (75%). When results from ESI and SPECT were concordant 100% of patients achieved Engel I outcome. If ESI and PET showed concordant localization, 90% of patients achieved postoperative seizure freedom.

Conclusions

Our findings demonstrate that HD-ESI allows accurate localization of the epileptogenic zone in patients with FCD.

Significance

In combination with other imaging modalities, ESI helps with planning a more accurate surgery and therefore, the chances of postoperative seizure control are higher. Since it is based on EEG recordings, it does not require sedation, which is particularly interesting in pediatric patients. ESI represents an important imaging tool in focal epilepsies due to cortical dysplasia, which might be difficult to detect on standard imaging.

Limited utility of structural MRI to identify the epileptogenic zone in young children with tuberous sclerosis

BACKGROUND AND PURPOSE

The success of epilepsy surgery in children with tuberous sclerosis complex (TSC) hinges on identification of the epileptogenic zone (EZ). We studied structural MRI markers of epileptogenic lesions in young children with TSC.

METHODS

We included 26 children with TSC who underwent epilepsy surgery before the age of 3 years at five sites, with 12 months or more follow-up. Two neuroradiologists, blinded to surgical outcome data, reviewed 10 candidate lesions on preoperative MRI for characteristics of the tuber (large affected area, calcification, cyst-like properties) and of focal cortical dysplasia (FCD) features (cortical malformation, gray-white matter junction blurring, transmantle sign). They selected lesions suspect for the EZ based on structural MRI, and reselected after unblinding to seizure onset location on electroencephalography (EEG).

RESULTS

None of the tuber characteristics and FCD features were distinctive for the EZ, indicated by resected lesions in seizure-free children. With structural MRI alone, the EZ was identified out of 10 lesions in 31%, and with addition of EEG data, this increased to 48%. However, rates of identification of resected lesions in non-seizure-free children were similar. Across 251 lesions, interrater agreement was moderate for large size (κ = .60), and fair (κ = .24) for all other features.

CONCLUSIONS

In young children with TSC, the utility of structural MRI features is limited in the identification of the epileptogenic tuber, but improves when combined with EEG data.

Sleep-wake states change the interictal localization of candidate epileptic source generators

STUDY OBJECTIVES

To compare estimated epileptic source localizations from 5 sleep-wake states (SWS): wakefulness (W), rapid eye movement sleep (REM), and non-REM 1-3.

METHODS

Electrical source localization (sLORETA) of interictal spikes from different SWS on surface EEG from the epilepsy monitoring unit at spike peak and take-off, with results mapped to individual brain models for 75% of patients. Concordance was defined as source localization voxels shared between 2 and 5 SWS, and discordance as those unique to 1 SWS against 1-4 other SWS.

RESULTS

563 spikes from 16 prospectively recruited focal epilepsy patients across 161 day-nights. SWS exerted significant differences at spike peak but not take-off. Source localization size did not vary between SWS. REM localizations were smaller in multifocal than unifocal patients (28.8% vs. 54.4%, p = .0091). All five SWS contributed about 45% of their localizations to converge onto 17.0 ± 15.5% voxels. Against any one other SWS, REM was least concordant (54.4% vs. 66.9%, p = .0006) and most discordant (39.3% vs. 29.6%, p = .0008). REM also yielded the most unique localizations (20.0% vs. 8.6%, p = .0059).

CONCLUSIONS

REM was best suited to identify candidate epileptic sources. sLORETA proposes a model in which an "omni-concordant core" of source localizations shared by all five SWS is surrounded by a "penumbra" of source localizations shared by some but not all SWS. Uniquely, REM spares this core to "move" source voxels from the penumbra to unique cortex not localized by other SWS. This may reflect differential intra-spike propagation in REM, which may account for its reported superior localizing abilities.

Impact of high-density EEG in presurgical evaluation for refractory epilepsy patients

OBJECTIVE

Electrical source localization (ESI) can help to identify the seizure onset zone or propagation zone, but it is unclear how dipole localization techniques influence surgical planning.

METHODS

Patients who received a high density (HD)-EEG from 7/2014-7/2019 at Stanford were included if they met the following inclusion criteria: (1) adequate epileptiform discharges were recorded for source localization analysis, (2) underwent surgical treatment, which was at least 6 months before the survey. Interictal ESI was performed with the LORETA method on age matched MRIs. Six neurophysiologists from the Stanford Epilepsy Program independently reviewed each case through an HIPPA-protected online survey. The same cases were presented again with additional data from the HD-EEG study. Ratings of how much the HD-EEG findings added value and in what way were recorded.

RESULTS

Fifty out of 202 patients met the inclusion criteria, providing a total of 276 h of HDEEG recordings. All patients had video EEG recordings and at least one brain MRI, 88 % had neuropsychological testing, 78 % had either a PET or SPECT scan. Additional HD-EEG information was rated as helpful in 83.8 %, not useful in 14.4 % and misleading in 1.8 % of cases. In 20.4 % of cases the HD-EEG information altered decision-making in a major way, such as choosing a different surgical procedure, avoidance of invasive recording or suggesting placement of invasive electrodes in a lobe not previously planned. In 21.5 % of cases, HD-EEG changed the plan in a minor way, e.g., extra invasive electrodes near the previously planned sites in the same sub-lobar region. In 42.3 % cases, HD-EEG did not change their plan but provided confirmation. In cases with normal MRI, additional HD-EEG information was more likely to change physicians' decision making during presurgical process when compared to the cases with MRI-visible lesions (53.3 % vs. 34.3 %, p = 0.002). Among patients achieving Engel class I/II outcome, the concordance rate of HD-EEG and resection zone was 64.7 % versus 35.3 % with class III/IV (p = 0.028).

CONCLUSION

HD-EEG assists presurgical planning for refractory epilepsy patients, with a higher yield in patients with non-lesional MRIs. Concordance of HD-EEG dipole analysis localization and resection site is a favorable outcome indicator.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Neocortical localization and thalamocortical modulation of neuronal hyperexcitability contribute to Fragile X Syndrome

Fragile X Syndrome (FXS) is a monogenetic form of intellectual disability and autism in which well-established knockout (KO) animal models point to neuronal hyperexcitability and abnormal gamma-frequency physiology as a basis for key disorder features. Translating these findings into patients may identify tractable treatment targets. Using source modeling of resting-state electroencephalography data, we report findings in FXS, including 1) increases in localized gamma activity, 2) pervasive changes of theta/alpha activity, indicative of disrupted thalamocortical modulation coupled with elevated gamma power, 3) stepwise moderation of low and high-frequency abnormalities based on female sex, and 4) relationship of this physiology to intellectual disability and neuropsychiatric symptoms. Our observations extend findings in Fmr1-/- KO mice to patients with FXS and raise a key role for disrupted thalamocortical modulation in local hyperexcitability. This systems-level mechanism has received limited preclinical attention but has implications for understanding fundamental disease mechanisms.

Accuracy of high-density EEG electrode position measurement using an optical scanner compared with the photogrammetry method

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We measured EEG electrode positions in high-density array.

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We compared an optical scanner with the conventional photogrammetry method.

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The optical scanner was more accurate than photogrammetry.

The real-time scanning was feasible – it took 5–10 min per patient.

Objective

To determine the feasibility and accuracy of a handheld optical scanner to measure the three-dimensional (3D) EEG electrode coordinates in a high-density array of 256 electrodes.

Methods

We compared the optical scanning with a previously validated method, based on photogrammetry. Electrode coordinates were co-registered with the MRI of the patients, and mean distance error relative to the three-dimensional MRI reconstruction was determined for each patient. We included 60 patients: 30 were measured using the photogrammetry method, and 30 age and gender matched patients were measured with the optical scanner.

Results

Using the optical scanner, the mean distance error was 1.78 mm (95% confidence interval: 1.59–1.98 mm) which was significantly lower (p < 0.001) compared with the photogrammetry method (mean distance error: 2.43 mm; 95% confidence interval: 2.28–2.57 mm). The real-time scanning took 5–10 min per patient.

Conclusions

The handheld optical scanner is more accurate and feasible, compared to the photogrammetry method.

Significance

Measuring EEG electrode positions in high-density array, using the optical scanner is suitable for clinical implementation in EEG source imaging for presurgical evaluation.

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 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 electrodes 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 out 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.

SEEG in 3D: Interictal Source Localization From Intracerebral Recordings

BACKGROUND

Stereo-electroencephalography (SEEG) uses a three-dimensional configuration of depth electrodes to localize epileptiform activity, but traditional analysis of SEEG is spatially restricted to the point locations of the electrode contacts. Interpolation of brain activity between contacts might allow for three-dimensional representation of epileptiform activity and avoid pitfalls of SEEG interpretation.

OBJECTIVE

The goal of this study was to validate SEEG-based interictal source localization and assess the ability of this technique to monitor far-field activity in non-implanted brain regions.

METHODS

Interictal epileptiform discharges were identified on SEEG in 26 patients who underwent resection, ablation, or disconnection of the suspected epileptogenic zone. Dipoles without (free) and with (scan) gray matter restriction, and current density (sLORETA and SWARM methods), were calculated using a finite element head model. Source localization results were compared to the conventional irritative zone (IZ) and the surgical treatment volumes (TV) of seizure-free vs. non-seizure-free patients.

RESULTS

The median distance from dipole solutions to the nearest contact in the conventional IZ was 7 mm (interquartile range 4-15 mm for free dipoles and 4-14 mm for scan dipoles). The IZ modeled with SWARM predicted contacts within the conventional IZ with 83% (75-100%) sensitivity and 94% (88-100%) specificity. The proportion of current within the TV was greater in seizure-free patients (P = 0.04) and predicted surgical outcome with 45% sensitivity and 93% specificity. Dipole solutions and sLORETA results did not correlate with seizure outcome. Addition of scalp EEG led to more superficial modeled sources (P = 0.03) and negated the ability to predict seizure outcome (P = 0.23). Removal of near-field data from contacts within the TV resulted in smearing of the current distribution (P = 0.007) and precluded prediction of seizure freedom (P = 0.20).

CONCLUSIONS

Source localization accurately represented interictal discharges from SEEG. The proportion of current within the TV distinguished between seizure-free and non-seizure-free patients when near-field recordings were obtained from the surgical target. The high prevalence of deep sources in this cohort likely obscured any benefit of concurrent scalp EEG. SEEG-based interictal source localization is useful in illustrating and corroborating the epileptogenic zone. Additional techniques are needed to localize far-field epileptiform activity from non-implanted brain regions.

Case Report: Laser Ablation Guided by State of the Art Source Imaging Ends an Adolescent's 16-Year Quest for Seizure Freedom

Epilepsy surgery is the most effective therapeutic approach for children with drug resistant epilepsy (DRE). Recent advances in neurosurgery, such as the Laser Interstitial Thermal Therapy (LITT), improved the safety and non-invasiveness of this method. Electric and magnetic source imaging (ESI/MSI) plays critical role in the delineation of the epileptogenic focus during the presurgical evaluation of children with DRE. Yet, they are currently underutilized even in tertiary epilepsy centers. Here, we present a case of an adolescent who suffered from DRE for 16 years and underwent surgery at Cook Children's Medical Center (CCMC). The patient was previously evaluated in a level 4 epilepsy center and treated with multiple antiseizure medications for several years. Presurgical evaluation at CCMC included long-term video electroencephalography (EEG), magnetoencephalography (MEG) with simultaneous conventional EEG (19 channels) and high-density EEG (256 channels) in two consecutive sessions, MRI, and fluorodeoxyglucose - positron emission tomography (FDG-PET). Video long-term EEG captured nine focal-onset clinical seizures with a maximal evolution over the right frontal/frontal midline areas. MRI was initially interpreted as non-lesional. FDG-PET revealed a small region of hypometabolism at the anterior right superior temporal gyrus. ESI and MSI performed with dipole clustering showed a tight cluster of dipoles in the right anterior insula. The patient underwent intracranial EEG which indicated the right anterior insular as seizure onset zone. Eventually LITT rendered the patient seizure free (Engel 1; 12 months after surgery). Retrospective analysis of ESI and MSI clustered dipoles found a mean distance of dipoles from the ablated volume ranging from 10 to 25 mm. Our findings highlight the importance of recent technological advances in the presurgical evaluation and surgical treatment of children with DRE, and the underutilization of epilepsy surgery in children with DRE.