Intracerebral propagation of interictal activity in partial epilepsy: implications for source localisation

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.

Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings

BACKGROUND

Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined.

NEW METHOD

We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared.

RESULTS

Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters.

COMPARISON WITH EXISTING METHODS

Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation.

CONCLUSIONS

Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.

A biophysically constrained brain connectivity model based on stimulation-evoked potentials

BACKGROUND

Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies.

NEW METHOD

Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features.

RESULTS

The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures.

COMPARISON WITH EXISTING METHOD

Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences.

CONCLUSIONS

These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.

Widespread slow oscillations support interictal epileptiform discharge networks in focal epilepsy

Interictal epileptiform discharges (IEDs) often co-occur across spatially-separated cortical regions, forming IED networks. However, the factors prompting IED propagation remain unelucidated. We hypothesized that slow oscillations (SOs) might facilitate IED propagation. Here, the amplitude and phase synchronization of SOs preceding propagating and non-propagating IEDs were compared in 22 patients with focal epilepsy undergoing intracranial electroencephalography (EEG) evaluation. Intracranial channels were categorized into the irritative zone (IZ) and normal zone (NOZ) regarding the presence of IEDs. During wakefulness, we found that pre-IED SOs within the IZ exhibited higher amplitudes for propagating IEDs than non-propagating IEDs (delta band: p = 0.001, theta band: p < 0.001). This increase in SOs was also concurrently observed in the NOZ (delta band: p = 0.04). Similarly, the inter-channel phase synchronization of SOs prior to propagating IEDs was higher than those preceding non-propagating IEDs in the IZ (delta band: p = 0.04). Through sliding window analysis, we observed that SOs preceding propagating IEDs progressively increased in amplitude and phase synchronization, while those preceding non-propagating IEDs remained relatively stable. Significant differences in amplitude occurred approximately 1150 ms before IEDs. During non-rapid eye movement (NREM) sleep, SOs on scalp recordings also showed higher amplitudes before intracranial propagating IEDs than before non-propagating IEDs (delta band: p = 0.006). Furthermore, the analysis of IED density around sleep SOs revealed that only high-amplitude sleep SOs demonstrated correlation with IED propagation. Overall, our study highlights that transient but widely distributed SOs are associated with IED propagation as well as generation in focal epilepsy during sleep and wakefulness, providing new insight into the EEG substrate supporting IED networks.

Electroencephalography-functional magnetic resonance imaging for clinical evaluation in focal epilepsy

OBJECTIVE

We aimed to evaluate the contribution of simultaneous recording of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) in the diagnosis of epilepsy syndrome, localization of the epileptogenic zone (EZ), and decision-making regarding surgical treatment.

METHODS

We performed a retrospective study to evaluate patients with focal epilepsy who underwent EEG-fMRI. Two evaluators assessed epilepsy syndrome, presumed focus, and surgical candidacy and defined confidence levels. They assessed these clinical characteristics first without EEG-fMRI and then including EEG-fMRI to assess how the results of EEG-fMRI changed the evaluations. We also determined how the clinical evaluation was affected by the concordance level between the blood oxygen level-dependent (BOLD) response and the presumed focus location, and by the confidence level of the BOLD response itself based on the t-value of the primary and secondary clusters.

RESULTS

Fifty-one scans from 48 patients were included. The BOLD map affected 66.7% of the evaluations by altering evaluation items (epilepsy syndrome, presumed focus, or surgical candidacy) or their confidence levels. EEG-fMRI results increased the confidence levels of epilepsy syndrome, presumed focus, or surgical candidacy in 47.1% of patients but reduced clinical confidence in these features in 11.8%. More specifically, the confidence levels increased for epilepsy syndrome in 28.5%, identification of presumed focus in 33.9%, and determination of surgical candidacy in 29.4%. The BOLD signal confidence level, whether high or low, did not influence these clinical factors.

SIGNIFICANCE

Previous studies have emphasized the utility of EEG-fMRI for the localization of the epileptogenic zone. This study demonstrated the potential of EEG-fMRI to influence clinical confidence when determining epilepsy syndrome, the presumed epileptic focus, and surgical candidacy.

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.

Identifying sources of human interictal discharges with travelling wave and white matter propagation

Interictal epileptiform discharges have been shown to propagate from focal epileptogenic sources as travelling waves or through more rapid white matter conduction. We hypothesize that both modes of propagation are necessary to explain interictal discharge timing delays. We propose a method that, for the first time, incorporates both propagation modes to identify unique potential sources of interictal activity. We retrospectively analysed 38 focal epilepsy patients who underwent intracranial EEG recordings and diffusion-weighted imaging for epilepsy surgery evaluation. Interictal discharges were detected and localized to the most likely source based on relative delays in time of arrival across electrodes, incorporating travelling waves and white matter propagation. We assessed the influence of white matter propagation on distance of spread, timing and clinical interpretation of interictal activity. To evaluate accuracy, we compared our source localization results to earliest spiking regions to predict seizure outcomes. White matter propagation helps to explain the timing delays observed in interictal discharge sequences, underlying rapid and distant propagation. Sources identified based on differences in time of receipt of interictal discharges are often distinct from the leading electrode location. Receipt of activity propagating rapidly via white matter can occur earlier than more local activity propagating via slower cortical travelling waves. In our cohort, our source localization approach was more accurate in predicting seizure outcomes than the leading electrode location. Inclusion of white matter in addition to travelling wave propagation in our model of discharge spread did not improve overall accuracy but allowed for identification of unique and at times distant potential sources of activity, particularly in patients with persistent postoperative seizures. Since distant white matter propagation can occur more rapidly than local travelling wave propagation, combined modes of propagation within an interictal discharge sequence can decouple the commonly assumed relationship between spike timing and distance from the source. Our findings thus highlight the clinical importance of recognizing the presence of dual modes of propagation during interictal discharges, as this may be a cause of clinical mislocalization.

High-rate leading spikes in propagating spike sequences predict seizure outcome in surgical patients with temporal lobe epilepsy

Inter-ictal spikes aid in the diagnosis of epilepsy and in planning surgery of medication-resistant epilepsy. However, the localizing information from spikes can be unreliable because spikes can propagate, and the burden of spikes, often assessed as a rate, does not always correlate with the seizure onset zone or seizure outcome. Recent work indicates identifying where spikes regularly emerge and spread could localize the seizure network. Thus, the current study sought to better understand where and how rates of single and coupled spikes, and especially brain regions with high-rate and leading spike of a propagating sequence, informs the extent of the seizure network. In 37 patients with medication-resistant temporal lobe seizures, who had surgery to treat their seizure disorder, an algorithm detected spikes in the pre-surgical depth inter-ictal EEG. A separate algorithm detected spike propagation sequences and identified the location of leading and downstream spikes in each sequence. We analysed the rate and power of single spikes on each electrode and coupled spikes between pairs of electrodes, and the proportion of sites with high-rate, leading spikes in relation to the seizure onset zone of patients seizure free (n = 19) and those with continuing seizures (n = 18). We found increased rates of single spikes in mesial temporal seizure onset zone (ANOVA, P < 0.001, η2 = 0.138), and increased rates of coupled spikes within, but not between, mesial-, lateral- and extra-temporal seizure onset zone of patients with continuing seizures (P < 0.001; η2 = 0.195, 0.113 and 0.102, respectively). In these same patients, there was a higher proportion of brain regions with high-rate leaders, and each sequence contained a greater number of spikes that propagated with a higher efficiency over a longer distance outside the seizure onset zone than patients seizure free (Wilcoxon, P = 0.0172). The proportion of high-rate leaders in and outside the seizure onset zone could predict seizure outcome with area under curve = 0.699, but not rates of single or coupled spikes (0.514 and 0.566). Rates of coupled spikes to a greater extent than single spikes localize the seizure onset zone and provide evidence for inter-ictal functional segregation, which could be an adaptation to avert seizures. Spike rates, however, have little value in predicting seizure outcome. High-rate spike sites leading propagation could represent sources of spikes that are important components of an efficient seizure network beyond the clinical seizure onset zone, and like the seizure onset zone these, too, need to be removed, disconnected or stimulated to increase the likelihood for seizure control.

Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy

Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.

Relative Contributions of Different Neural Sources to the EEG

Dogma dictates that the EEG signal is generated by postsynaptic currents (PSCs) because there are an enormous number of synapses in the brain, and PSCs have relatively long durations. However, PSCs are not the only potential source of electric fields in the brain. Action potentials, afterpolarizations, and presynaptic activity can also generate electric fields. Experimentally it is exceedingly difficult to delineate the contributions of different sources because they are casually linked. However, using computational modeling, we can interrogate the relative contributions of different neural elements to the EEG. We used a library of neuron models with morphologically realistic axonal arbors to quantify the relative contributions of PSCs, action potentials, and presynaptic activity to the EEG signal. Consistent with prior assertions, PSCs were the largest contributor to the EEG, but action potentials and afterpolarizations can also make appreciable contributions. For a population of neurons generating simultaneous PSCs and action potentials, we found that the action potentials accounted for up to 20% of the source strength while PSCs accounted for the other 80% and presynaptic activity negligibly contributed. Additionally, L5 PCs generated the largest PSC and action potential signals indicating that they the dominant EEG signal generator. Further, action potentials and afterpolarizations were sufficient to generate physiological oscillations, indicating that they are valid source contributors to the EEG. The EEG emerges from a combination of multiple different source, and, while PSCs are the largest contributor, other sources are non-negligible and should be included in modeling, analysis and interpretation of the EEG.

Interictal discharges in the human brain are travelling waves arising from an epileptogenic source

While seizure activity may be electrographically widespread, increasing evidence has suggested that ictal discharges may in fact represent travelling waves propagated from a focal seizure source. Interictal epileptiform discharges (IEDs) are an electrographic manifestation of excessive hypersynchronization of cortical activity that occur between seizures and are considered a marker of potentially epileptogenic tissue. The precise relationship between brain regions demonstrating IEDs and those involved in seizure onset, however, remains poorly understood. Here, we hypothesize that IEDs likewise reflect the receipt of travelling waves propagated from the same regions which give rise to seizures. Forty patients from our institution who underwent invasive monitoring for epilepsy, proceeded to surgery and had at least one year of follow-up were included in our study. Interictal epileptiform discharges were detected using custom software, validated by a clinical epileptologist. We show that IEDs reach electrodes in sequences with a consistent temporal ordering, and this ordering matches the timing of receipt of ictal discharges, suggesting that both types of discharges spread as travelling waves. We use a novel approach for localization of ictal discharges, in which time differences of discharge receipt at nearby electrodes are used to compute source location; similar algorithms have been used in acoustics and geophysics. We find that interictal discharges co-localize with ictal discharges. Moreover, interictal discharges tend to localize to the resection territory in patients with good surgical outcome and outside of the resection territory in patients with poor outcome. The seizure source may originate at, and also travel to, spatially distinct IED foci. Our data provide evidence that interictal discharges may represent travelling waves of pathological activity that are similar to their ictal counterparts, and that both ictal and interictal discharges emerge from common epileptogenic brain regions. Our findings have important clinical implications, as they suggest that seizure source localizations may be derived from interictal discharges, which are much more frequent than seizures.

Integration of white matter architecture to stereo-EEG better describes epileptic spike propagation

OBJECTIVE

Stereo-electroencephalography (SEEG)-derived epilepsy networks are used to better understand a patient's epilepsy; however, a unimodal approach provides an incomplete picture. We combine tractography and SEEG to determine the relationship between spike propagation and the white matter architecture and to improve our understanding of spike propagation mechanisms.

METHODS

Probablistic tractography from diffusion imaging (dMRI) of matched subjects from the Human Connectome Project (HCP) was combined with patient-specific SEEG-derived spike propagation networks. Two regions-of-interest (ROIs) with a significant spike propagation relationship constituted a Propagation Pair.

RESULTS

In 56 of 59 patients, Propagation Pairs were more often tract-connected as compared to all ROI pairs (p < 0.01; d = -1.91). The degree of spike propagation between tract-connected ROIs was greater (39 ± 21%) compared to tract-unconnected ROIs (31 ± 18%; p < 0.0001). Within the same network, ROIs receiving propagation earlier were more often tract-connected to the source (59.7%) as compared to late receivers (25.4%; p < 0.0001).

CONCLUSIONS

Brain regions involved in spike propagation are more likely to be connected by white matter tracts. Between nodes, presence of tracts suggests a direct course of propagation, whereas the absence of tracts suggests an indirect course of propagation.

SIGNIFICANCE

We demonstrate a logical and consistent relationship between spike propagation and the white matter architecture.

Ideal current dipoles are appropriate source representations for simulating neurons for intracranial recordings

OBJECTIVE

To determine whether dipoles are an appropriate simplified representation of neural sources for stereo-EEG (sEEG).

METHODS

We compared the distributions of voltages generated by a dipole, biophysically realistic cortical neuron models, and extended regions of cortex to determine how well a dipole represented neural sources at different spatial scales and at electrode to neuron distances relevant for sEEG. We also quantified errors introduced by the dipole approximation of neural sources in sEEG source localization using standardized low-resolution electrotomography (sLORETA).

RESULTS

For pyramidal neurons, the coefficient of correlation between voltages generated by a dipole and neuron model were > 0.9 for distances > 1 mm. For small regions of cortex (∼0.1 cm2), the error in voltages between a dipole and region was < 100 µV for all distances. However, larger regions of active cortex (>5 cm2) yielded > 50 µV errors within 1.5 cm of an electrode when compared to single dipoles. Finally, source localization errors were < 5 mm when using dipoles to represent realistic neural sources.

CONCLUSIONS

Single dipoles are an appropriate source model to represent both single neurons and small regions of active cortex, while multiple dipoles are required to represent large regions of cortex.

SIGNIFICANCE

Dipoles are computationally tractable and valid source models for sEEG.

Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset

Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 ± 2.8 s before seizure onset. Degree rose in all channels 8.2 ± 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.

Local neuronal excitation and global inhibition during epileptic fast ripples in humans

Understanding the neuronal basis of epileptic activity is a major challenge in neurology. Cellular integration into larger scale networks is all the more challenging. In the local field potential, interictal epileptic discharges can be associated with fast ripples (200-600 Hz), which are a promising marker of the epileptogenic zone. Yet, how neuronal populations in the epileptogenic zone and in healthy tissue are affected by fast ripples remain unclear. Here, we used a novel 'hybrid' macro-micro depth electrode in nine drug-resistant epileptic patients, combining classic depth recording of local field potentials (macro-contacts) and two or three tetrodes (four micro-wires bundled together) enabling up to 15 neurons in local circuits to be simultaneously recorded. We characterized neuronal responses (190 single units) with the timing of fast ripples (2233 fast ripples) on the same hybrid and other electrodes that target other brain regions. Micro-wire recordings reveal signals that are not visible on macro-contacts. While fast ripples detected on the closest macro-contact to the tetrodes were always associated with fast ripples on the tetrodes, 82% of fast ripples detected on tetrodes were associated with detectable fast ripples on the nearest macro-contact. Moreover, neuronal recordings were taken in and outside the epileptogenic zone of implanted epileptic subjects and they revealed an interlay of excitation and inhibition across anatomical scales. While fast ripples were associated with increased neuronal activity in very local circuits only, they were followed by inhibition in large-scale networks (beyond the epileptogenic zone, even in healthy cortex). Neuronal responses to fast ripples were homogeneous in local networks but differed across brain areas. Similarly, post-fast ripple inhibition varied across recording locations and subjects and was shorter than typical inter-fast ripple intervals, suggesting that this inhibition is a fundamental refractory process for the networks. These findings demonstrate that fast ripples engage local and global networks, including healthy tissue, and point to network features that pave the way for new diagnostic and therapeutic strategies. They also reveal how even localized pathological brain dynamics can affect a broad range of cognitive functions.

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

BACKGROUND AND OBJECTIVES

Accurate delineation of the seizure-onset zone (SOZ) in focal drug-resistant epilepsy often requires stereo-EEG (SEEG) recordings. Our aims were to propose a truly objective and quantitative comparison between EEG/magnetoencephalography (MEG) source imaging (EMSI), EEG/fMRI responses for similar spikes with primary irritative zone (PIZ) and SOZ defined by SEEG and to evaluate the value of EMSI and EEG/fMRI to predict postsurgical outcome.

METHODS

We identified patients with drug-resistant epilepsy who underwent EEG/MEG, EEG/fMRI, and subsequent SEEG at the Epilepsy Service from the Montreal Neurological Institute and Hospital. We quantified multimodal concordance within the SEEG channel space as spatial overlap with PIZ/SOZ and distances to the spike-onset, spike maximum amplitude and seizure core intracerebral channels by applying a new methodology consisting of converting EMSI results into SEEG electrical potentials (EMSIe-SEEG) and projecting the most significant fMRI response on the SEEG channels (fMRIp-SEEG). Spatial overlaps with PIZ/SOZ (AUCPIZ, AUCSOZ) were assessed by using the area under the receiver operating characteristic curve (AUC). Here, AUC represents the probability that a randomly picked active contact exhibited higher amplitude when located inside the spatial reference than outside.

RESULTS

Seventeen patients were included. Mean spatial overlaps with the PIZ and SOZ were 0.71 and 0.65 for EMSIe-SEEG and 0.57 and 0.62 for fMRIp-SEEG. Good EMSIe-SEEG spatial overlap with the PIZ was associated with smaller distance from the maximum EMSIe-SEEG contact to the spike maximum amplitude channel (median distance 14 mm). Conversely, good fMRIp-SEEG spatial overlap with the SOZ was associated with smaller distances from the maximum fMRIp-SEEG contact to the spike-onset and seizure core channels (median distances 10 and 5 mm, respectively). Surgical outcomes were correctly predicted by EEG/MEG in 12 of 15 (80%) patients and EEG/fMRI in 6 of 11(54%) patients.

DISCUSSION

With the use of a unique quantitative approach estimating EMSI and fMRI results in the reference SEEG channel space, EEG/MEG and EEG/fMRI accurately localized the SOZ and the PIZ. Precisely, EEG/MEG more accurately localized the PIZ, whereas EEG/fMRI was more sensitive to the SOZ. Both neuroimaging techniques provide complementary localization that can help guide SEEG implantation and select good candidates for surgery.

Temporally and functionally distinct large-scale brain network dynamics supporting task switching

Objective:

Our daily activities require frequent switches among competing responses at the millisecond time scale. We determined the spatiotemporal characteristics and functional significance of rapid, large-scale brain network dynamics during task switching.

Methods:

This cross-sectional study investigated patients with drug-resistant focal epilepsy who played a Lumosity cognitive flexibility training game during intracranial electroencephalography (iEEG) recording. According to a given task rule, unpredictably switching across trials, participants had to swipe the screen in the direction the stimulus was pointing or moving. Using this data, we described the spatiotemporal characteristics of iEEG high-gamma augmentation occurring more intensely during switch than repeat trials, unattributable to the effect of task rule (pointing or moving), within-stimulus congruence (the direction of stimulus pointing and moving was same or different in a given trial), or accuracy of an immediately preceding response. Diffusion-weighted imaging (DWI) tractography determined whether distant cortical regions showing enhanced activation during task switch trials were directly connected by white matter tracts. Trial-by-trial iEEG analysis deduced whether the intensity of task switch-related high-gamma augmentation was altered through practice and whether high-gamma amplitude predicted the accuracy of an upcoming response among switch trials.

Results:

The average number of completed trials during five-minute gameplay was 221.4 per patient (range: 171–285). Task switch trials increased the response times, whereas later trials reduced them. Analysis of iEEG signals sampled from 860 brain sites effectively elucidated the distinct spatiotemporal characteristics of task switch, task rule, and post-error-specific high-gamma modulations. Post-cue, task switch-related high-gamma augmentation was initiated in the right calcarine cortex after 260 ms, right precuneus after 330 ms, right entorhinal after 420 ms, and bilateral anterior middle-frontal gyri after 450 ms. DWI tractography successfully showed the presence of direct white matter tracts connecting the right visual areas to the precuneus and anterior middle-frontal regions but not between the right precuneus and anterior middle-frontal regions. Task-related high-gamma amplitudes in later trials were reduced in the calcarine, entorhinal and anterior middle-frontal regions, but increased in the precuneus. Functionally, enhanced post-cue precuneus high-gamma augmentation improved the accuracy of subsequent responses among switch trials.

Conclusions:

Our multimodal analysis uncovered two temporally and functionally distinct network dynamics supporting task switching. High-gamma augmentation in the visual-precuneus pathway may reflect the neural process facilitating an attentional shift to a given updated task rule. High-gamma activity in the visual-dorsolateral prefrontal pathway, rapidly reduced through practice, may reflect the cost of executing appropriate stimulus-response translation.

Interictal Spikes as an EEG Biomarker of Cognitive Impairment

SUMMARY

Although interictal spikes (IISs) are a well-established EEG biomarker for epilepsy, whether they are also a biomarker of cognitive deficits is unclear. Interictal spikes are dynamic events consisting of a synchronous discharge of neurons producing high frequency oscillations and a succession of action potentials which disrupt the ongoing neural activity. There are robust data showing that IISs result in transitory cognitive impairment with the type of deficit specific to the cognitive task and anatomic location of the IIS. Interictal spike, particularly if frequent and widespread, can impair cognitive abilities, through interference with waking learning and memory and memory consolidation during sleep. Interictal spikes seem to be particularly concerning in the developing brain where animal data suggest that IISs can lead to adverse cognitive effects even after the disappearance of the spikes. Whether a similar phenomenon occurs in human beings is unclear. Thus, although IISs are a clear biomarker of transitory cognitive impairment, currently, they lack sensitivity and specificity as a biomarker for enduring cognitive impairment.

Higher-order tensor decomposition based scalp-to-intracranial EEG projection for detection of interictal epileptiform discharges

Objective.Interictal epileptiform discharges (IEDs) occur between two seizures onsets. IEDs are mainly captured by intracranial recordings and are often invisible over the scalp. This study proposes a model based on tensor factorization to map the time-frequency (TF) features of scalp EEG (sEEG) to the TF features of intracranial EEG (iEEG) in order to detect IEDs from over the scalp with high sensitivity.Approach.Continuous wavelet transform is employed to extract the TF features. Time, frequency, and channel modes of IED segments from iEEG recordings are concatenated into a four-way tensor. Tucker and CANDECOMP/PARAFAC decomposition techniques are employed to decompose the tensor into temporal, spectral, spatial, and segmental factors. Finally, TF features of both IED and non-IED segments from scalp recordings are projected onto the temporal components for classification.Main results.The model performance is obtained in two different approaches: within- and between-subject classification approaches. Our proposed method is compared with four other methods, namely a tensor-based spatial component analysis method, TF-based method, linear regression mapping model, and asymmetric-symmetric autoencoder mapping model followed by convolutional neural networks. Our proposed method outperforms all these methods in both within- and between-subject classification approaches by respectively achieving 84.2% and 72.6% accuracy values.Significance.The findings show that mapping sEEG to iEEG improves the performance of the scalp-based IED detection model. Furthermore, the tensor-based mapping model outperforms the autoencoder- and regression-based mapping models.

Automated electrical source imaging with scalp EEG to define the insular irritative zone: Comparison with simultaneous intracranial EEG

OBJECTIVE

To evaluate the accuracy of automatedinterictallow-density electrical source imaging (LD-ESI) to define the insular irritative zone (IZ) by comparing the simultaneous interictal ESI localization with the SEEG interictal activity.

METHODS

Long-term simultaneous scalp electroencephalography (EEG) and stereo-EEG (SEEG) with at least one depth electrode exploring the operculo-insular region(s) were analyzed. Automated interictal ESI was performed on the scalp EEG using standardized low-resolution brain electromagnetic tomography (sLORETA) and individual head models. A two-step analysis was performed: i) sublobar concordance betweencluster-based ESI localization and SEEG-based IZ; ii) time-locked ESI-/SEEG analysis. Diagnostic accuracy values were calculated using SEEG as reference standard. Subgroup analysis wascarried out, based onthe involvement of insular contacts in the seizure onset and patterns of insular interictal activity.

RESULTS

Thirty patients were included in the study. ESI showed an overall accuracy of 53% (C.I. 29-76%). Sensitivity and specificity were calculated as 53% (C.I. 29-76%), 55% (C.I. 23-83%) respectively. Higher accuracy was found in patients with frequent and dominant interictal insular spikes.

CONCLUSIONS

LD-ESI defines with good accuracy the insular implication in the IZ, which is not possible with classical interictalscalpEEG interpretation.

SIGNIFICANCE

Automated LD-ESI may be a valuable additional tool to characterize the epileptogenic zone in epilepsies with suspected insular involvement.

Dynamic tractography-based localization of spike sources and animation of spike propagations

OBJECTIVE

This study was undertaken to build and validate a novel dynamic tractography-based model for localizing interictal spike sources and visualizing monosynaptic spike propagations through the white matter.

METHODS

This cross-sectional study investigated 1900 spike events recorded in 19 patients with drug-resistant temporal lobe epilepsy (TLE) who underwent extraoperative intracranial electroencephalography (iEEG) and resective surgery. Twelve patients had mesial TLE (mTLE) without a magnetic resonance imaging-visible mass lesion. The remaining seven had a mass lesion in the temporal lobe neocortex. We identified the leading and lagging sites, defined as those initially and subsequently (but within ≤50 ms) showing spike-related augmentation of broadband iEEG activity. In each patient, we estimated the sources of 100 spike discharges using the latencies at given electrode sites and diffusion-weighted imaging-based streamline length measures. We determined whether the spatial relationship between the estimated spike sources and resection was associated with postoperative seizure outcomes. We generated videos presenting the spatiotemporal change of spike-related fiber activation sites by estimating the propagation velocity using the streamline length and spike latency measures.

RESULTS

The spike propagation velocity from the source was 1.03 mm/ms on average (95% confidence interval = .91-1.15) across 133 tracts noted in the 19 patients. The estimated spike sources in mTLE patients with International League Against Epilepsy Class 1 outcome were more likely to be in the resected area (83.9% vs. 72.3%, φ = .137, p < .001) and in the medial temporal lobe region (80.5% vs. 72.5%, φ = .090, p = .002) than those associated with the Class ≥2 outcomes. The resulting video successfully animated spike propagations, which were confined within the temporal lobe in mTLE but involved extratemporal lobe areas in lesional TLE.

SIGNIFICANCE

We have, for the first time, provided dynamic tractography visualizing the spatiotemporal profiles of rapid propagations of interictal spikes through the white matter. Dynamic tractography has the potential to serve as a unique epilepsy biomarker.

Increased delta power as a scalp marker of epileptic activity: a simultaneous scalp and intracranial electroencephalography study

BACKGROUND AND PURPOSE

The purpose was to evaluate whether intracranial interictal epileptiform discharges (IEDs) that are not visible on the scalp are associated with changes in the frequency spectrum on scalp electroencephalograms (EEGs).

METHODS

Simultaneous scalp high-density EEG and intracranial EEG recordings were recorded in nine patients undergoing pre-surgical invasive recordings for pharmaco-resistant temporal lobe epilepsy. Epochs with hippocampal IED visible on intracranial EEG (ic-IED) but not on scalp EEG were selected, as well as control epochs without ic-IED. Welch's power spectral density was computed for each scalp electrode and for each subject; the power spectral density was further averaged across the canonical frequency bands and compared between the two conditions with and without ic-IED. For each patient the peak frequency in the delta band (the significantly strongest frequency band in all patients) was determined during periods of ic-IED. The five electrodes showing strongest power at the peak frequency were also determined.

RESULTS

It was found that intracranial IEDs are associated with an increase in delta power on scalp EEGs, in particular at a frequency ≥1.4 Hz. Electrodes showing slow frequency power changes associated with IEDs were consistent with the hemispheric lateralization of IEDs. Electrodes with maximum power of slow activity were not limited to temporal regions but also involved frontal (bilateral or unilateral) regions.

CONCLUSIONS

In patients with a clinical picture suggestive of temporal lobe epilepsy, the presence of delta slowing ≥1.4 Hz in anterior temporal regions can represent a scalp marker of hippocampal IEDs. To our best knowledge this is the first study that demonstrates the co-occurrence of ic-IED and increased delta power.

Validation of semi-automated anatomically labeled SEEG contacts in a brain atlas for mapping connectivity in focal epilepsy

OBJECTIVE

Stereotactic electroencephalography (SEEG) has been widely used to explore the epileptic network and localize the epileptic zone in patients with medically intractable epilepsy. Accurate anatomical labeling of SEEG electrode contacts is critically important for correctly interpreting epileptic activity. We present a method for automatically assigning anatomical labels to SEEG electrode contacts using a 3D-segmented cortex and coregistered postoperative CT images.

METHOD

Stereotactic electroencephalography electrode contacts were spatially localized relative to the brain volume using a standard clinical procedure. Each contact was then assigned an anatomical label by clinical epilepsy fellows. Separately, each contact was automatically labeled by coregistering the subject's MRI to the USCBrain atlas using the BrainSuite software and assigning labels from the atlas based on contact locations. The results of both labeling methods were then compared, and a subsequent vetting of the anatomical labels was performed by expert review.

RESULTS

Anatomical labeling agreement between the two methods for over 17 000 SEEG contacts was 82%. This agreement was consistent in patients with and without previous surgery (P = .852). Expert review of contacts in disagreement between the two methods resulted in agreement with the atlas based over manual labels in 48% of cases, agreement with manual over atlas-based labels in 36% of cases, and disagreement with both methods in 16% of cases. Labels deemed incorrect by the expert review were then categorized as either in a region directly adjacent to the correct label or as a gross error, revealing a lower likelihood of gross error from the automated method.

SIGNIFICANCE

The method for semi-automated atlas-based anatomical labeling we describe here demonstrates potential to assist clinical workflow by reducing both analysis time and the likelihood of gross anatomical error. Additionally, it provides a convenient means of intersubject analysis by standardizing the anatomical labels applied to SEEG contact locations across subjects.

How cerebral cortex protects itself from interictal spikes: The alpha/beta inhibition mechanism

Interactions between interictal epileptiform discharges (IEDs) and distant cortical regions subserve potential effects on cognition of patients with focal epilepsy. We hypothesize that "healthy" brain areas at a distance from the epileptic focus may respond to the interference of IEDs by generating inhibitory alpha and beta oscillations. We predict that more prominent alpha-beta oscillations can be found in patients with less impaired neurocognitive profile. We performed a source imaging magnetoencephalography study, including 41 focal epilepsy patients: 21 with frontal lobe epilepsy (FLE) and 20 with mesial temporal lobe epilepsy. We investigated the effect of anterior (i.e., frontal and temporal) IEDs on the oscillatory pattern over posterior head regions. We compared cortical oscillations (5-80 Hz) temporally linked to 3,749 IEDs (1,945 frontal and 1,803 temporal) versus an equal number of IED-free segments. We correlated results from IED triggered oscillations to global neurocognitive performance. Only frontal IEDs triggered alpha-beta oscillations over posterior head regions. IEDs with higher amplitude triggered alpha-beta oscillations of higher magnitude. The intensity of posterior head region alpha-beta oscillations significantly correlated with a better neuropsychological profile. Our study demonstrated that cerebral cortex protects itself from IEDs with generation of inhibitory alpha-beta oscillations at distant cortical regions. The association of more prominent oscillations with a better cognitive status suggests that this mechanism might play a role in determining the cognitive resilience in patients with FLE.

A review of magnetoencephalography use in pediatric epilepsy: an update on best practice

Introduction: Magnetoencephalography (MEG) is a noninvasive technique that is used for presurgical evaluation of children with drug-resistant epilepsy (DRE).Areas covered: The contributions of MEG for localizing the epileptogenic zone are discussed, in particular in extra-temporal lobe epilepsy and focal cortical dysplasia, which are common in children, as well as in difficult to localize epilepsy such as operculo-insular epilepsy. Further, the authors review current evidence on MEG for mapping eloquent cortex, its performance, application in clinical practice, and potential challenges.Expert opinion: MEG could change the clinical management of children with DRE by directing placement of intracranial electrodes thereby enhancing their yield. With improved identification of a circumscribed epileptogenic zone, MEG could render more patients as suitable candidates for epilepsy surgery and increase utilization of surgery.

Incorporating Uncertainty in Data Labeling into Automatic Detection of Interictal Epileptiform Discharges from Concurrent Scalp-EEG via Multi-way Analysis

Interictal epileptiform discharges (IEDs) are elicited from an epileptic brain, whereas they can also be due to other neurological abnormalities. The diversity in their morphologies, their strengths, and their sources within the brain cause a great deal of uncertainty in their labeling by clinicians. The aim of this study is therefore to exploit and incorporate this uncertainty (the probability of the waveform being an IED) in the IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. For comparison, we also propose and study SCA-based method in which probability of the waveform being an IED is ignored. The proposed models are employed to detect IEDs in two different classification approaches: (1) subject-dependent and (2) subject-independent classification approaches. The proposed methods are compared with two other state-of-the-art methods namely, time-frequency features and tensor factorization methods. The proposed SCA-IEDP model has achieved superior performance in comparison with the traditional SCA and other competing methods. It achieved 79.9% and 63.4% accuracy values in subject-dependent and subject-independent classification approaches, respectively. This shows that considering the IED probabilities in designing an IED detection system can boost its performance.

Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

Clinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Contribution of Ictal Source Imaging for Localizing Seizure Onset Zone in Patients With Focal Epilepsy

OBJECTIVE

To determine whether seizure onset zone (SOZ) can be localized accurately prior to surgical planning in patients with focal epilepsy, we performed noninvasive EEG recordings and source localization analyses on 39 patients.

METHODS

In 39 patients with focal epilepsy, we recorded and extracted 138 seizures and 1,325 interictal epileptic discharges using high-density EEG. We investigated a novel approach for directly imaging sources of seizures and interictal spikes from high-density EEG recordings, and rigorously validated it for noninvasive localization of SOZ determined from intracranial EEG findings and surgical resection volume. Conventional source imaging analyses were also performed for comparison.

RESULTS

Ictal source imaging showed a concordance rate of 95% when compared to intracranial EEG or resection results. The average distance from estimation to seizure onset (intracranial) electrodes is 1.35 cm in patients with concordant results, and 0.74 cm to surgical resection boundary in patients with successful surgery. About 41% of the patients were found to have multiple types of interictal activities; coincidentally, a lower concordance rate and a significantly worse performance in localizing SOZ were observed in these patients.

CONCLUSION

Noninvasive ictal source imaging with high-density EEG recording can provide highly concordant results with clinical decisions obtained by invasive monitoring or confirmed by resective surgery. By means of direct seizure imaging using high-density scalp EEG recordings, the added value of ictal source imaging is particularly high in patients with complex interictal activity patterns, who may represent the most challenging cases with poor prognosis.

Detection of epileptic activity in presumably normal EEG

Monitoring epileptic activity in the absence of interictal discharges is a major need given the well-established lack of reliability of patients' reports of their seizures. Up to now, there are no other tools than reviewing the seizure diary; however, seizures may not be remembered or dismissed voluntarily. In the present study, we set out to determine if EEG voltage maps of epileptogenic activity in individual patients can help to identify disease activity, even if their scalp EEG appears normal. Twenty-five patients with pharmacoresistant focal epilepsy were included. For each patient, 6 min of EEG with spikes (yes-spike) and without visually detectable epileptogenic discharges (no-spike) were selected from long-term monitoring recordings (EEG 31-37 channels). For each patient, we identified typical discharges, calculated their average and the corresponding scalp voltage map ('spike-map'). We then fitted the spike-map for each patient on their (i) EEG epochs with visible spikes, (ii) epochs without any visible spike and (iii) EEGs of 48 controls. The global explained variance was used to estimate the presence of the spike-maps. The individual spike-map occurred more often in the spike-free EEGs of patients compared to EEGs of healthy controls (P = 0.001). Not surprisingly, this difference was higher if the EEGs contained spikes (P < 0.001). In patients, spike-maps were more frequent per second (P < 0.001) but with a shorter mean duration (P < 0.001) than in controls, for both no-spike and yes-spike EEGs. The amount of spike-maps was unrelated to clinical variables, like epilepsy severity, drug load or vigilance state. Voltage maps of spike activity are present very frequently in the scalp EEG of patients, even in presumably normal EEG. We conclude that spike-maps are a robust and potentially powerful marker to monitor subtle epileptogenic activity.

Spatial distribution of interictal spikes fluctuates over time and localizes seizure onset

The location of interictal spikes is used to aid surgical planning in patients with medically refractory epilepsy; however, their spatial and temporal dynamics are poorly understood. In this study, we analysed the spatial distribution of interictal spikes over time in 20 adult and paediatric patients (12 females, mean age = 34.5 years, range = 5-58) who underwent intracranial EEG evaluation for epilepsy surgery. Interictal spikes were detected in the 24 h surrounding each seizure and spikes were clustered based on spatial location. The temporal dynamics of spike spatial distribution were calculated for each patient and the effects of sleep and seizures on these dynamics were evaluated. Finally, spike location was assessed in relation to seizure onset location. We found that spike spatial distribution fluctuated significantly over time in 14/20 patients (with a significant aggregate effect across patients, Fisher's method: P < 0.001). A median of 12 sequential hours were required to capture 80% of the variability in spike spatial distribution. Sleep and postictal state affected the spike spatial distribution in 8/20 and 4/20 patients, respectively, with a significant aggregate effect (Fisher's method: P < 0.001 for each). There was no evidence of pre-ictal change in the spike spatial distribution for any patient or in aggregate (Fisher's method: P = 0.99). The electrode with the highest spike frequency and the electrode with the largest area of downstream spike propagation both localized the seizure onset zone better than predicted by chance (Wilcoxon signed-rank test: P = 0.005 and P = 0.002, respectively). In conclusion, spikes localize seizure onset. However, temporal fluctuations in spike spatial distribution, particularly in relation to sleep and post-ictal state, can confound localization. An adequate duration of intracranial recording-ideally at least 12 sequential hours-capturing both sleep and wakefulness should be obtained to sufficiently sample the interictal network.

Fast periodic visual stimulation to highlight the relationship between human intracerebral recordings and scalp electroencephalography

Despite being of primary importance for fundamental research and clinical studies, the relationship between local neural population activity and scalp electroencephalography (EEG) in humans remains largely unknown. Here we report simultaneous scalp and intracerebral EEG responses to face stimuli in a unique epileptic patient implanted with 27 intracerebral recording contacts in the right occipitotemporal cortex. The patient was shown images of faces appearing at a frequency of 6 Hz, which elicits neural responses at this exact frequency. Response quantification at this frequency allowed to objectively relate the neural activity measured inside and outside the brain. The patient exhibited typical 6 Hz responses on the scalp at the right occipitotemporal sites. Moreover, there was a clear spatial correspondence between these scalp responses and intracerebral signals in the right lateral inferior occipital gyrus, both in amplitude and in phase. Nevertheless, the signal measured on the scalp and inside the brain at nearby locations showed a 10-fold difference in amplitude due to electrical insulation from the head. To further quantify the relationship between the scalp and intracerebral recordings, we used an approach correlating time-varying signals at the stimulation frequency across scalp and intracerebral channels. This analysis revealed a focused and right-lateralized correspondence between the scalp and intracerebral recordings that were specific to the face stimulation is more broadly distributed in various control situations. These results demonstrate the interest of a frequency tagging approach in characterizing the electrical propagation from brain sources to scalp EEG sensors and in identifying the cortical sources of brain functions from these recordings.

Reorganization of Large-Scale Functional Networks During Low-Frequency Electrical Stimulation of the Cortical Surface

We investigated the functional network reorganization caused by low-frequency electrical stimulation (LFES) of human brain cortical surface. Intracranial EEG data from subdural grid positions were analyzed in 16 pre-surgery epileptic patients. LFES was performed by injecting current pulses (10[Formula: see text]mA, 0.2[Formula: see text]ms pulse width, 0.5[Formula: see text]Hz, 25 trials) into all adjacent electrode contacts. Dynamic functional connectivity analysis was carried out on two frequency bands (low: 1–4[Formula: see text]Hz; high: 10–40[Formula: see text]Hz) to investigate the early, high frequency and late, low frequency responses elicited by the stimulation. The centralization increased in early compared to late responses, suggesting a more prominent role of direct neural links between primarily activated areas and distant brain regions. Injecting the current into the seizure onset zone (SOZ) evoked a more integrated functional topology during the early (N1) period of the response, whereas during the late (N2) period — regardless of the stimulation site — the connectedness of the SOZ was elevated compared to the non-SOZ tissue. The abnormal behavior of the epileptic sub-network during both part of the responses supports the idea of the pathogenic role of impaired inhibition and excitation mechanisms in epilepsy.

The Influence of EEG References on the Analysis of Spatio-Temporal Interrelation Patterns

The characterization of the functional network of the brain dynamics has become a prominent tool to illuminate novel aspects of brain functioning. Due to its excellent time resolution, such research is oftentimes based on electroencephalographic recordings (EEG). However, a particular EEG-reference might cause crucial distortions of the spatiotemporal interrelation pattern and may induce spurious correlations as well as diminish genuine interrelations originally present in the dataset. Here we investigate in which manner correlation patterns are affected by a chosen EEG reference. To this end we evaluate the influence of 7 popular reference schemes on artificial recordings derived from well controlled numerical test frameworks. In this respect we are not only interested in the deformation of spatial interrelations, but we test additionally in which way the time evolution of the functional network, estimated via some bi-variate interrelation measures, gets distorted. It turns out that the median reference as well as the global average show the best performance in most situations considered in the present study. However, if a collective brain dynamics is present, where most of the signals get correlated, these schemes may also cause crucial deformations of the functional network, such that the parallel use of different reference schemes seems advisable.

The seizure onset zone drives state-dependent epileptiform activity in susceptible brain regions

OBJECTIVE

Due to variability in the patterns of propagation of interictal epileptiform discharges (IEDs), qualitative definition of the irritative zone has been challenging. Here, we introduce a quantitative approach toward exploration of the dynamics of IED propagation within the irritative zone.

METHODS

We examined intracranial EEG (iEEG) in nine participants undergoing invasive monitoring for seizure localization. We used an automated IED detector and a community detection algorithm to identify populations of electrodes exhibiting IED activity that co-occur in time, and to group these electrodes into communities.

RESULTS

Within our algorithmically-identified communities, IED activity in the seizure onset zone (SOZ) tended to lead IED activity in other functionally coupled brain regions. The tendency of pathological activity to arise in the SOZ, and to spread to non-SOZ tissues, was greater in the asleep state.

CONCLUSIONS

IED activity, and, by extension, the variability observed between the asleep and awake states, is propagated from a core seizure focus to nearby less pathological brain regions.

SIGNIFICANCE

Using an unsupervised, computational approach, we show that the spread of IED activity through the epilepsy network varies with physiologic state.

Electrophysiological Brain Connectivity: Theory and Implementation

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Reproducibility of interictal spike propagation in children with refractory epilepsy

OBJECTIVE

Interictal spikes are a characteristic feature of invasive electroencephalography (EEG) recordings in children with refractory epilepsy. Spikes frequently co-occur across multiple brain regions with discernable latencies, suggesting that spikes can propagate through distributed neural networks. The purpose of this study was to examine the long-term reproducibility of spike propagation patterns over hours to days of interictal recording.

METHODS

Twelve children (mean age 13.1 years) were retrospectively studied. A mean ± standard deviation (SD) of 47.2 ± 40.1 hours of interictal EEG recordings were examined per patient (range 17.5-166.5 hours). Interictal recordings were divided into 30-minute segments. Networks were extracted based on the frequency of spike coactivation between pairs of electrodes. For each 30-minute segment, electrodes were assigned a "Degree Preference (DP)" based on the tendency to appear upstream or downstream within propagation sequences. The consistency of DPs across segments ("DP-Stability") was quantified using the Spearman rank correlation.

RESULTS

Regions exhibited highly stable preferences to appear upstream, intermediate, or downstream in spike propagation sequences. Across networks, the mean ± SD DP-Stability was 0.88 ± 0.07, indicating that propagation patterns observed in 30-minute segments were representative of the patterns observed in the full interictal window. At the group level, regions involved in seizure generation appeared more upstream in spike propagation sequences.

SIGNIFICANCE

Interictal spike propagation is a highly reproducible output of epileptic networks. These findings shed new light on the spatiotemporal dynamics that may constrain the network mechanisms of refractory epilepsy.

Diagnostic accuracy of interictal source imaging in presurgical epilepsy evaluation: A systematic review from the E-PILEPSY consortium

OBJECTIVE

Interictal high resolution (HR-) electric source imaging (ESI) and magnetic source imaging (MSI) are non-invasive tools to aid epileptogenic zone localization in epilepsy surgery candidates. We carried out a systematic review on the diagnostic accuracy and quality of evidence of these modalities.

METHODS

Embase, Pubmed and the Cochrane database were searched on 13 February 2017. Diagnostic accuracy studies taking post-surgical seizure outcome as reference standard were selected. Quality appraisal was based on the QUADAS-2 framework.

RESULTS

Eleven studies were included: eight MSI (n = 267), three HR-ESI (n = 127) studies. None was free from bias. This mostly involved: selection of operated patients only, interference of source imaging with surgical decision, and exclusion of indeterminate results. Summary sensitivity and specificity estimates were 82% (95% CI: 75-88%) and 53% (95% CI: 37-68%) for overall source imaging, with no statistical difference between MSI and HR-ESI. Specificity is higher when partially concordant results were included as non-concordant (p < 0.05). Inclusion of indeterminate test results as non-concordant lowered sensitivity (p < 0.05).

CONCLUSIONS

Source imaging has a relatively high sensitivity but low specificity for identification of the epileptogenic zone.

SIGNIFICANCE

We need higher quality studies allowing unbiased test evaluation to determine the added value and diagnostic accuracy of source imaging in the presurgical workup of refractory focal epilepsy.

Simultaneous scalp EEG improves seizure lateralization during unilateral intracranial EEG evaluation in temporal lobe epilepsy

PURPOSE

To determine if simultaneous bilateral scalp EEG (scEEG) can accurately detect a contralateral seizure onset in patients with unilateral intracranial EEG (IEEG) implantation.

METHODS

We evaluated 39 seizures from 9 patients with bitemporal epilepsy who underwent simultaneous scEEG and IEEG (SSIEEG). To simulate conditions of unilateral IEEG implantation with a missed contralateral seizure onset, we analyzed the IEEG recording contralateral to the seizure onset (CL- IEEG), in conjunction with simultaneous scEEG. The following criteria were evaluated between scEEG and CL- IEEG (1) latency: the time to onset of EEG seizure (2) location: concordance of ictal onset zones and (3) pattern: congruence of EEG morphology and frequency.

RESULTS

SSIEEG correctly lateralized 36/39 (92.3%) seizures compared to 13/39 (33.3%) seizures using CL- IEEG alone (OR = 24.0, p < 0.01), 33 (84.6%) seizures using scEEG alone (OR = 2.2, p = 0.29) and 26 (66.9%) seizures using time of clinical onset alone (OR = 6.0, p = 0.01). For the three criteria evaluated, (1) 22/39 (56.4%) seizures had an earlier onset on the scEEG, compared to CL- IEEG; (2) lack of congruence of location of seizure onset was noted in 33/39 (84.6%) of the seizures; and (3) 22/39 (56.4%) seizures did not have a congruent ictal pattern.

CONCLUSIONS

The chronological, topographic and morphologic features of SSIEEG can accurately detect the hemisphere of seizure onset in most cases with unilateral IEEG implantation. SSIEEG is significantly better than, IEEG, scEEG or clinical onset alone in this scenario. We propose that SSIEEG should be considered in all cases of intractable focal epilepsy undergoing unilateral IEEG evaluation.

Surgical resection of ripple onset predicts outcome in pediatric epilepsy

OBJECTIVE

In patients with medically refractory epilepsy (MRE), interictal ripples (80-250Hz) are observed in large brain areas whose resection may be unnecessary for seizure freedom. This limits their utility as epilepsy biomarkers for surgery. We assessed the spatiotemporal propagation of interictal ripples on intracranial electroencephalography (iEEG) in children with MRE, compared it with the propagation of spikes, identified ripples that initiated propagation (onset-ripples), and evaluated their clinical value as epilepsy biomarkers.

METHODS

Twenty-seven children who underwent epilepsy surgery were studied. We identified propagation sequences of ripples and spikes across multiple iEEG contacts and calculated each ripple or spike latency from the propagation onset. We classified ripples and spikes into categories (ie, onset, spread, and isolated) based on their spatiotemporal characteristics and correlated their mean rate inside and outside resection with outcome (good outcome, Engel 1 versus poor outcome, Engel≥2). We determined, as onset-zone, spread-zone, and isolated-zone, the areas generating the corresponding ripple or spike category and evaluated the predictive value of their resection.

RESULTS

We observed ripple propagation in all patients and spike propagation in 25 patients. Mean rate of onset-ripples inside resection predicted the outcome (odds ratio = 5.37; p = 0.02) and correlated with Engel class (rho = -0.55; p = 0.003). Resection of the onset-ripple-zone was associated with good outcome (p = 0.047). No association was found for the spread-ripple-zone, isolated-ripple-zone, or any spike-zone.

INTERPRETATION

Interictal ripples propagate across iEEG contacts in children with MRE. The association between the onset-ripple-zone resection and good outcome indicates that onset-ripples are promising epilepsy biomarkers, which estimate the epileptogenic tissue better than spread-ripples or onset-spikes. Ann Neurol 2018;84:331-346.

Deep Neural Architectures for Mapping Scalp to Intracranial EEG

Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

Characterizing EEG Cortical Dynamics and Connectivity with Responses to Single Pulse Electrical Stimulation (SPES)

Objectives: To model cortical connections in order to characterize their oscillatory behavior and role in the generation of spontaneous electroencephalogram (EEG). Methods: We studied averaged responses to single pulse electrical stimulation (SPES) from the non-epileptogenic hemisphere of five patients assessed with intracranial EEG who became seizure free after contralateral temporal lobectomy. Second-order control system equations were modified to characterize the systems generating a given response. SPES responses were modeled as responses to a unit step input. EEG power spectrum was calculated on the 20[Formula: see text]s preceding SPES. Results: 121 channels showed responses to 32 stimulation sites. A single system could model the response in 41.3% and two systems were required in 58.7%. Peaks in the frequency response of the models tended to occur within the frequency range of most activity on the spontaneous EEG. Discrepancies were noted between activity predicted by models and activity recorded in the spontaneous EEG. These discrepancies could be explained by the existence of alpha rhythm or interictal epileptiform discharges. Conclusions: Cortical interactions shown by SPES can be described as control systems which can predict cortical oscillatory behavior. The method is unique as it describes connectivity as well as dynamic interactions.

Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions

OBJECTIVE

The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions.

METHODS

To identify the synchronized neuronal activity underlying the IEDs, the variation in correlation over time of the SEEG signals is related to the occurrence of IEDs using the general linear model. The interdependency is assessed of the brain areas that reflect highly synchronized neural activity by applying independent component analysis, followed by cluster analysis of the spatial distributions of the independent components. The spatiotemporal interactions of the spike clusters reveal the leading or lagging of brain areas.

RESULTS

The analysis framework was evaluated for five successfully operated patients, showing that the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone. The additional value of the framework was demonstrated for two more patients, who were MRI-negative and for whom surgery was not successful.

CONCLUSIONS

A network approach is promising in case of complex epilepsies.

SIGNIFICANCE

Analysis of IEDs is considered a valuable addition to routine review of SEEG recordings, with the potential to increase the success rate of epilepsy surgery.