Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach

OBJECTIVE

To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data.

METHODS

We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization.

RESULTS

FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes.

SIGNIFICANCE

We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

High-density electroencephalographic functional networks in genetic generalized epilepsy: Preserved whole-brain topology hides local reorganization

OBJECTIVE

Genetic generalized epilepsy (GGE) accounts for approximately 20% of adult epilepsy cases and is considered a disorder of large brain networks, involving both hemispheres. Most studies have not shown any difference in functional whole-brain network topology when compared to healthy controls. Our objective was to examine whether this preserved global network topology could hide local reorganizations that balance out at the global network level.

METHODS

We recorded high-density electroencephalograms from 20 patients and 20 controls, and reconstructed the activity of 118 regions. We computed functional connectivity in windows free of interictal epileptiform discharges in broad, delta, theta, alpha, and beta frequency bands, characterized the network topology, and used the Hub Disruption Index (HDI) to quantify the topological reorganization. We examined the generalizability of our results by reproducing a 25-electrode clinical system.

RESULTS

Our study did not reveal any significant change in whole-brain network topology among GGE patients. However, the HDI was significantly different between patients and controls in all frequency bands except alpha (p < .01, false discovery rate [FDR] corrected, d < -1), and accompanied by an increase in connectivity in the prefrontal regions and default mode network. This reorganization suggests that regions that are important in transferring the information in controls were less so in patients. Inversely, the crucial regions in patients are less so in controls. These findings were also found in delta and theta frequency bands when using 25 electrodes (p < .001, FDR corrected, d < -1).

SIGNIFICANCE

In GGE patients, the overall network topology is similar to that of healthy controls but presents a balanced local topological reorganization. This reorganization causes the prefrontal areas and default mode network to be more integrated and segregated, which may explain executive impairment associated with GGE. Additionally, the reorganization distinguishes patients from controls even when using 25 electrodes, suggesting its potential use as a diagnostic tool.

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

High-density electric source imaging patterns and outcomes following temporal lobectomy in patients with hippocampal sclerosis

OBJECTIVE

The objective of this study was to ascertain specific patterns of electrical source imaging (ESI) that are associated with a good surgical outcome (no seizure recurrence) using 256-channel high-density (HD) electroencephalography (EEG) in patients with temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) who underwent temporal lobectomy.

METHODS

Adult patients (age ≥ 18 years) were prospectively recruited from September 2016 to May 2020 at the authors' center. All patients underwent phase I presurgical evaluation and were subsequently advised to proceed with surgery based on consensus from a multidisciplinary epilepsy conference, without knowing HD-ESI results. All recruited patients were followed for at least 12 months after surgery. The outcome of interest was a status of no seizure recurrence, which was assessed at the end of the study. The association between ESI patterns and outcome was assessed using the chi-square or Fisher exact test. Associated p values as well as odds ratios and 95% CIs were reported. The diagnostic performance of the significant pattern associated with the outcome was also evaluated.

RESULTS

Fifty-eight patients with known predictors for either good or worse surgical outcomes were recruited. The mean postoperative follow-up period was 33.34 (SD 13.88) months. Forty-seven patients had sufficient interictal epileptiform discharges for HD-ESI analysis. Thirteen of these 47 patients experienced seizure recurrence. The most common source localizations were at Brodmann area (BA) 20 (inferior temporal area) and BA 21 (middle temporal area). A specific ESI pattern of BA 21 without extratemporal sources was significantly associated with no seizure recurrence (p = 0.047). This pattern had a high positive predictive value of 100% and false-positive rate of 0% associated with no seizure recurrence following the surgery.

CONCLUSIONS

A specific ESI pattern that was highly associated with no seizure recurrence following surgery was demonstrated by a 256-channel HD-EEG. If this pattern can be reproducibly proven in further studies, some TLE-HS patients may be able to proceed with surgery without further investigations.

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.

ConvDip: A Convolutional Neural Network for Better EEG Source Imaging

The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture, that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs <40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.

High-Density EEG in Current Clinical Practice and Opportunities for the Future

SUMMARY

High-density EEG (HD-EEG) recordings use a higher spatial sampling of scalp electrodes than a standard 10-20 low-density EEG montage. Although several studies have demonstrated improved localization of the epileptogenic cortex using HD-EEG, widespread implementation is impeded by cost, setup and interpretation time, and lack of specific or sufficient procedural billing codes. Despite these barriers, HD-EEG has been in use at several institutions for years. These centers have noted utility in a variety of clinical scenarios where increased spatial resolution from HD-EEG has been required, justifying the extra time and cost. We share select scenarios from several centers, using different recording techniques and software, where HD-EEG provided information above and beyond the standard low-density EEG. We include seven cases where HD-EEG contributed directly to current clinical care of epilepsy patients and highlight two novel techniques which suggest potential opportunities to improve future clinical care. Cases illustrate how HD-EEG allows clinicians to: case 1-lateralize falsely generalized interictal epileptiform discharges; case 2-improve localization of falsely generalized epileptic spasms; cases 3 and 4-improve localization of interictal epileptiform discharges in anatomic regions below the circumferential limit of standard low-density EEG coverage; case 5-improve noninvasive localization of the seizure onset zone in lesional epilepsy; cases 6 and 7-improve localization of the seizure onset zone to guide invasive investigation near eloquent cortex; case 8-identify epileptic fast oscillations; and case 9-map language cortex. Together, these nine cases illustrate that using both visual analysis and advanced techniques, HD-EEG can play an important role in clinical management.

Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System

This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.

Temporal and Spatial Dynamics of Different Interictal Epileptic Discharges: A Time-Frequency EEG Approach in Pediatric Focal Refractory Epilepsy

Objective: Characterization of the spatial and temporal dynamics of interictal epileptic discharges (IED) using time-frequency analysis (TFA) and electrical-source localization (ESL).

Methods: TFA was performed on IED (spikes, spike waves, and polyspike waves) recorded by high-density-EEG (HD-EEG) in 19 refractory focal epileptic children. Temporal modulations related to IEDs were analyzed in a time window around the IED peaks [−1,000 to 1,000 ms]. Spatial modulations were analyzed by ESL in the time-frequency and time domains.

Results: IED were associated with complex power spectral modulations. We observed increases in power spectrum (IPS) patterns specific to IED type. For spikes, the TFA pattern consisted of an IPS (−100 to +100 ms, 4–50 Hz). For spike waves, the IPS was followed by a second IPS (+100 to +400 ms, 4–10 Hz), corresponding to the slow wave. IPS patterns were preceded (−400 to −100 ms, 4–40 Hz), and followed (+100 to +400 ms) by a decrease in the power spectrum (DPS) (n = 8). For 14 out of 19 patients, at least one ESL method was concordant with the epileptogenic area. For the remaining five patients, all of them had temporal epilepsies. ESL in the time-frequency domain (DPS/IPS) provided concordant (n = 6) or complementary (n = 4) information to the ESL in the time domain concerning the epileptogenic zone. ESL in time-frequency domain (DPS/IPS) was the only method to provide concordant information concerning the epileptogenic zone in three patients.

Significance: TFA demonstrates complex time-frequency modulations of the neuronal networks around IED, suggesting that the pathological mechanisms are initiated well before onset of the classical hyper-synchronization of the IED. Combining time and time-frequency analysis of the ESL provides complementary information to define the epileptogenic zone in refractory focal epilepsy.

Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low-density EEG

OBJECTIVE

Electrical source imaging (ESI) is used increasingly to estimate the epileptogenic zone (EZ) in patients with epilepsy. Directed functional connectivity (DFC) coupled to ESI helps to better characterize epileptic networks, but studies on interictal activity have relied on high-density recordings. We investigated the accuracy of ESI and DFC for localizing the EZ, based on low-density clinical electroencephalography (EEG).

METHODS

We selected patients with the following: (a) focal epilepsy, (b) interictal spikes on standard EEG, (c) either a focal structural lesion concordant with the electroclinical semiology or good postoperative outcome. In 34 patients (20 temporal lobe epilepsy [TLE], 14 extra-TLE [ETLE]), we marked interictal spikes and estimated the cortical activity during each spike in 82 cortical regions using a patient-specific head model and distributed linear inverse solution. DFC between brain regions was computed using Granger-causal modeling followed by network topologic measures. The concordance with the presumed EZ at the sublobar level was computed using the epileptogenic lesion or the resected area in postoperative seizure-free patients.

RESULTS

ESI, summed outflow, and efficiency were concordant with the presumed EZ in 76% of the patients, whereas the clustering coefficient and betweenness centrality were concordant in 70% of patients. There was no significant difference between ESI and connectivity measures. In all measures, patients with TLE had a significantly higher (P < 0.05) concordance with the presumed EZ than patients with with ETLE. The brain volume accepted for concordance was significantly larger in TLE.

SIGNIFICANCE

ESI and DFC derived from low-density EEG can reliably estimate the EZ from interictal spikes. Connectivity measures were not superior to ESI for EZ localization during interictal spikes, but the current validation of the localization of connectivity measure is promising for other applications.

Electrical Neuroimaging of Music Processing in Pianists With and Without True Absolute Pitch

True absolute pitch (AP), labeling of pitches with semitone precision without a reference, is classically studied using isolated tones. However, AP is acquired and has its function within complex dynamic musical contexts. Here we examined event-related brain responses and underlying cerebral sources to endings of short expressive string quartets, investigating a homogeneous population of young highly trained pianists with half of them possessing true-AP. The pieces ended regularly or contained harmonic transgressions at closure that participants appraised. Given the millisecond precision of ERP analyses, this experimental plan allowed examining whether AP alters music processing at an early perceptual, or later cognitive level, or both, and which cerebral sources underlie differences with non-AP musicians. We also investigated the impact of AP on general auditory cognition. Remarkably, harmonic transgression sensitivity did not differ between AP and non-AP participants, and differences for auditory cognition were only marginal. The key finding of this study is the involvement of a microstate peaking around 60 ms after musical closure, characterizing AP participants. Concurring sources were estimated in secondary auditory areas, comprising the planum temporale, all transgression conditions collapsed. These results suggest that AP is not a panacea to become a proficient musician, but a rare perceptual feature.

Beta oscillations in the sensorimotor cortex correlate with disease and remission in benign epilepsy with centrotemporal spikes

INTRODUCTION

Benign epilepsy with centrotemporal spikes (BECTS) is a common form of childhood epilepsy with the majority of those afflicted remitting during their early teenage years. Seizures arise from the lower half of the sensorimotor cortex of the brain (e.g. seizure onset zone) and the abnormal epileptiform discharges observed increase during NREM sleep. To date no clinical factors reliably predict disease course, making determination of ongoing seizure risk a significant challenge. Prior work in BECTS have shown abnormalities in beta band (14.9-30 Hz) oscillations during movement and rest. Oscillations in this frequency band are modulated by state of consciousness and thought to reflect intrinsic inhibitory mechanisms.

METHODS

We used high density EEG and source localization techniques to examine beta band activity in the seizure onset zone (sensorimotor cortex) in a prospective cohort of children with BECTS and healthy controls during sleep. We hypothesized that beta power in the sensorimotor cortex would be different between patients and healthy controls, and that beta abnormalities would improve with resolution of disease in this self-limited epilepsy syndrome. We further explored the specificity of our findings and correlation with clinical features. Statistical testing was performed using logistic and standard linear regression models.

RESULTS

We found that beta band power in the seizure onset zone is different between healthy controls and BECTS patients. We also found that a longer duration of time spent seizure-free (corresponding to disease remission) correlates with lower beta power in the seizure onset zone. Exploratory spatial analysis suggests this effect is not restricted to the sensorimotor cortex. Exploratory frequency analysis suggests that this phenomenon is also observed in alpha and gamma range activity. We found no relationship between beta power and the presence or rate of epileptiform discharges in the sensorimotor cortex or a test of sensorimotor performance.

CONCLUSION

These results provide evidence that cortical beta power in the seizure onset zone may provide a dynamic physiological biomarker of disease in BECTS.

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.

Functionally aberrant electrophysiological cortical connectivities in first episode medication-naive schizophrenics from three psychiatry centers

Functional dissociation between brain processes is widely hypothesized to account for aberrations of thought and emotions in schizophrenic patients. The typically small groups of analyzed schizophrenic patients yielded different neurophysiological findings, probably because small patient groups are likely to comprise different schizophrenia subtypes. We analyzed multichannel eyes-closed resting EEG from three small groups of acutely ill, first episode productive schizophrenic patients before start of medication (from three centers: Bern N = 9; Osaka N = 9; Berlin N = 12) and their controls. Low resolution brain electromagnetic tomography (LORETA) was used to compute intracortical source model-based lagged functional connectivity not biased by volume conduction effects between 19 cortical regions of interest (ROIs). The connectivities were compared between controls and patients of each group. Conjunction analysis determined six aberrant cortical functional connectivities that were the same in the three patient groups. Four of these six concerned the facilitating EEG alpha-1 frequency activity; they were decreased in the patients. Another two of these six connectivities concerned the inhibiting EEG delta frequency activity; they were increased in the patients. The principal orientation of the six aberrant cortical functional connectivities was sagittal; five of them involved both hemispheres. In sum, activity in the posterior brain areas of preprocessing functions and the anterior brain areas of evaluation and behavior control functions were compromised by either decreased coupled activation or increased coupled inhibition, common across schizophrenia subtypes in the three patient groups. These results of the analyzed three independent groups of schizophrenics support the concept of functional dissociation.