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Published in final edited form as: Semin Pediatr Neurol. 2021 Aug 20;39:100919. doi: 10.1016/j.spen.2021.100919

Localizing the Epileptogenic Zone with Novel Biomarkers

Christos Papadelis 1,2,3,4, M Scott Perry 1
PMCID: PMC8501232  NIHMSID: NIHMS1734832  PMID: 34620466

Abstract

Several noninvasive methods, such as high-density EEG or magnetoencephalography, are currently used to delineate the epileptogenic zone (EZ) during the presurgical evaluation of patients with drug resistant epilepsy (DRE). Yet, none of these methods can reliably identify the EZ by their own. In most cases a multimodal approach is needed. Challenging cases often require the implantation of intracranial electrodes, either through stereo-taxic EEG or electro-corticography. Recently, a growing body of literature introduces novel biomarkers of epilepsy that can be used for analyzing both invasive as well as noninvasive electrophysiological data. Some of these biomarkers are able to delineate the EZ with high precision, augment the presurgical evaluation, and predict the surgical outcome of patients with DRE undergoing surgery. However, the use of these epilepsy biomarkers in clinical practice is limited. Here, we summarize and discuss the latest technological advances in the presurgical neurophysiological evaluation of children with DRE with emphasis on electric and magnetic source imaging, high frequency oscillations, and functional connectivity.

Keywords: Electric Source Imaging, Functional Connectivity, High Frequency Oscillations, Magnetic Source Imaging

Introduction

Up to 30% of children with epilepsy are unable to control their seizures with antiseizure medications (ASM) and suffer from drug resistant epilepsy (DRE) [1]. Children with DRE are at increased risk for poor long-term intellectual and psychosocial outcomes, along with a poor health-related life quality [2]. For these children, epilepsy surgery is the best available treatment since it offers higher rate of seizure freedom and better outcome with respect to behavior and quality of life compared to drug therapy alone [3]. To be successful, epilepsy surgery requires the complete resection of the epileptogenic zone (EZ), the brain area that is indispensable for the generation of seizures [4]. Precise localization of the EZ is necessary to guide resection or focal ablation [5]. Eloquent brain areas for essential tasks, such as language and movements, should also be identified and preserved. Current practice uses the seizure onset zone (SOZ), the brain area where seizures initiate, the most approximate estimator of the EZ. However, the identification of this zone is challenging since it requires the recording of several spontaneous stereotyped seizures at the expense of human and financial resources. Moreover, these methods may not represent the full extent of the EZ. The noninvasive delineation of the EZ is currently performed through mapping several overlapping cortical zones using a variety of noninvasive diagnostic tests. The most common cortical zones to be considered are the irritative zone, the SOZ, the functional deficit zone, and the eloquent areas (Fig. 1).

Figure 1: Overlapping cortical zones for the delineation of the EZ.

Figure 1:

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Eloquent areas mainly consist of the primary motor (green), primary somatosensory (blue), primary visual (orange), and language areas (Broca’s and Wernicke’s areas; yellow); eloquent areas are responsible for basic physiological brain functions. Irritative Zone (dotted grey area): cortical area that generates interictal spikes in the EEG. Functional deficit zone (diagonal lined grey area): cortical area that is functionally abnormal during the interictal period as it can be identified by neurological examination or neuropsychological testing. Seizure Onset Zone (red area): the brain area where seizures initiate.

In approximately 20% of patients with DRE, the findings of noninvasive tests are either inconclusive or non-concordant. Thus, a clear hypothesis about the location of the EZ cannot be formulated and patients require intracranial EEG (iEEG), which is performed either through stereo-electroencephalography (sEEG) with depth electrodes or electro-corticography (ECoG) with grids and strips. These invasive procedures, particularly the sEEG, are generally well-tolerated and can correctly identify the EZ in ~60% of patients [6]. Yet, iEEG recordings have several limitations due to their invasiveness [7,8], and offer limited spatial sampling since iEEG electrodes record activity only in their direct vicinity and are blind to other areas. This makes it difficult to judge whether the activity at the seizure onset truly represents the seizure generator or is the result of propagation from other regions. Thus, the actual focus and extent of the SOZ may be missed, leading to surgical failure. Furthermore, the interpretation of invasive ictal studies requires the recording of several stereotyped clinical seizures to allow for a secure identification of the SOZ. Thus, in some patients, iEEG recordings from several days to weeks are necessary to acquire enough ictal information. As a result, there is an urgent need for better presurgical tools that can localize the EZ with high precision. This focused review aims to highlight the latest technological and neurophysiological advances in presurgical evaluation with specific emphasis on electric and magnetic source imaging (ESI and MSI), high frequency oscillations (HFOs), and functional connectivity (FC).

Electric and Magnetic Source Imaging

ESI and MSI are currently used for the noninvasive localization of the irritative zone during the surgical work-up of patients with DRE. Several studies investigated the contribution of ESI/MSI in the presurgical evaluation process by assessing their localization accuracy and clinical utility. ESI and MSI can localize the irritative zone with an accuracy that ranges from ~7 to ~30 mm using a variety of source localization methods [9-11]. MSI seems to be more accurate than ESI though the difference in localization accuracy between the two modalities is on the order of mm [11]. Yet, this assessment is based on data of MEG and high-density EEG (HD-EEG) having unequal number of sensors (i.e., 306 sensors distributed in 102 locations for MEG versus 72 channels for HD-EEG). Further comparison studies are necessary that consider the same number of sensors for the two modalities. ESI and MSI have high sensitivity (up to 90%) and diagnostic odds ratio (up to 7.9) but low specificity (up to 54%) [12]. MSI has shown to change the surgical management plan in 21% to 33% of patients and was associated with long-term seizure control [13,14]. Proximity of ESI/MSI solutions to resection has also be linked to outcome [11,15,16]. Thus, ESI/MSI solutions can be interpreted as presurgical indicators of the EZ location. This contribution is not limited to the use of highly sophisticated techniques, such as HD-EEG or MEG, but also when the ESI is performed on conventional low-density EEG (i.e., 19 channels) interictal [11] or ictal recordings [17] (Fig. 2). The role of ESI/MSI is even more critical in patients with normal MRI or MRI with subtle or non-focal findings. Both ESI and MSI solutions can facilitate the surgical planning for these patients and limit the reliance on invasive monitoring, since they can estimate the SOZ and irritative zone with high precision when appropriate methods are used [18,19].

Figure 2: ESI and MSI solutions on ictal and interictal data.

Figure 2:

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A. Seizure onset (vertical red highlight area) on low-density conventional scalp EEG (selection of a −4 to 6 s time window) from a 9-year-old boy with DRE. (B) Source localization of the SOZ with Equivalent Current Dipole (ECD) of a single seizure and all seizures color-coded based on their goodness of fit (GOF) overlaid on resection (green area). C. Sensor locations and source localization of interictal spikes with ECD in a 13-year-old girl with DRE for conventional low-density (19 channels) scalp EEG, HD-EEG (72 channels), MEG (306 sensors), and iEEG (72 subdural electrodes) [11].

Despite numerous studies showing ESI and MSI provide reliable findings and yield clinical utility in the presurgical evaluation of patients with DRE, few epilepsy centers integrate these modalities in their standard clinical practice. A recent survey from a large consortium in Europe, comprising 25 epilepsy centers, showed that less than half of the centers used these methods for presurgical evaluation [20]. In most centers, the irritative zone and SOZ are approximated noninvasively through visual inspection of the EEG signal; the scalp region where the peak negativity of the discharges (phase reversal) is identified and regarded as the area of interest. Yet, this approach can lead to misleading findings since peak negativity can be recorded over a different lobe and even different side than the source due to volume conduction. Moreover, MSI/ESI solutions, if/when used, are rarely combined despite the complementary information they contain. A recent prospective study [21], showed that although the localization accuracy of the combined MSI/ESI was not significantly different from conventional neuroimaging, these two modalities complemented each other since their combined solution was able to localize the source in cases in which the conventional methods did not.

ESI can prove beneficial even when applied to iEEG data. Due to its limited spatial sampling, iEEG can result in misleading localization since the location of the recording contacts may differ from the actual source of the recorded activity [22, 23]. In a recent iEEG study, Alhilani and colleagues [24] determined the surgical utility of ESI on ictal and interictal iEEG data recorded from children with DRE. In a cohort of patients with focal cortical dysplasia, they showed that intracranial ESI of ictal and interictal activity can delineate the irritative zone and SOZ with a higher prognostic value for surgical outcome compared to the conventional approach for iEEG interpretation (Fig. 3).

Figure 3: Delineation of the irritative zone (IZ) and SOZ using ESI on iEEG data.

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(A) Delineation of the IZ for a 18-year old boy underwent surgery (good outcome). Top panels show the irritative zone defined with the conventional approach by using the coordinates of most interictally active contacts (red) overlaid on patients pre-operative MRI overlaid on resection (green). Bottom panels show the irritative zone defined by ESI (cyan dipoles). (B) Delineation of the SOZ for the same patient. The figure has been obtained from [24] after permission.

High Frequency Oscillations (HFOs)

For the past two decades, HFOs have attracted the attention of the research community developing electrophysiological epilepsy biomarkers. Interictal HFOs are defined as brief spontaneous low-amplitude oscillations between 80 and 500 Hz standing out of the background iEEG signal. HFOs are strongly bound to the SOZ and their resection correlates with surgical outcome [25-27]; they seem to be more specific biomarkers of the EZ than spikes though recent findings challenged this notion [28]. HFOs are classified into ripples (80-250 Hz) and fast ripples (250-500 Hz) [29]. Very-HFOs (>1,000 Hz) have been also seen in patients with epilepsy [30] but their clinical utility is unclear. Ripples are observed in most patients but have low specificity since they can also occur in non-epileptogenic regions (physiological ripples). Fast ripples are more specific biomarkers than ripples [31,32] since they are more focal but may not be recorded with conventional macroelectrodes [33].

The superiority of HFOs compared to other biomarkers was challenged by two recent iEEG studies. A multicenter study in 53 patients showed that HFOs, on a patient level, were useful for the identification of the EZ in only 67% of patients [34]. Another study showed the absence of statistical evidence to support that HFOs or its variants (combinations of HFOs overlapping with spikes) are globally better biomarkers than spikes [28]. The absence of statistical evidence was attributed to the observation that HFOs were better biomarkers than spikes in some patients but spikes were better in the others. This variable performance of HFOs and spikes can most likely be explained by the presence of physiological ripples and the large distribution of spikes. In an attempt to differentiate pathological from physiological ripples, two independent research groups recently showed that ripples propagate across iEEG electrodes and that surgical resection of the onset of this propagation (ripples onset) is more predictive of the surgical outcome compared to areas of spread [35,36]. In a cohort of pediatric patients with DRE undergoing surgery, Tamilia and colleagues [35] identified propagation sequences of ripples across multiple iEEG contacts which were different from the propagation of spikes. This previously undescribed phenomenon may reflect a hierarchical epileptogenic brain organization: onset-ripples are more epileptogenic than spread-ripples or isolated ripples which occur sporadically (Fig. 4). Similar findings were seen in a cohort of seizure-free adults with focal epilepsy after neurosurgery [36]; ripples and fast-ripples did not occur as isolated events, but rather as a pathophysiological activity that was organized in networks. In line with Tamilia and colleagues [35], onset areas of the ripple and fast ripple network were frequently seen in resected tissue. However, contrarily to Tamilia et al. [35], surgical resection of these areas was not superior compared to iEEG channels with the highest rates of HFOs. Such contradictory findings may be explained by the fact that the two studies examined different population cohorts (i.e., adults vs. children). While in adults the onset of HFOs may also correspond to the highest rate contacts, in children other areas (e.g., non-epileptogenic and non-onset) may also present high rates of HFOs (thus physiological HFOs). Considering the limitations of ripples, research groups recently examined the role of fast ripples as accurate biomarkers of the EZ. Nevalainen and colleagues [37] analyzed overnight stereo-EEG recordings from adult patients with DRE and hypothesized that different measures of fast ripples (i.e., resection ratio, maximal rate, and distribution) can predict outcome. Fast ripples were accurate in predicting outcome at the individual level, while absence of channels with high rates of fast ripples or absence of one dominant fast ripples area was a poor prognostic factor. In line with these findings, van't Klooster [38] examined whether HFOs and spikes in combined pre- and post-resection ECoG can predict outcome in different tailoring approaches. They found that (for different tailoring approaches) fast ripples that persist before and after resection predict poor outcome.

Figure 4: Spatiotemporal propagation of HFOs (ripples).

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(A) Propagating ripples on filtered iEEG data (80-250 Hz) from a 10-year-old-boy with DRE. (B) Spatiotemporal propagation of ripples displayed on intraoperative photo of implanted grid of patient’s cortex and on the 3D cortical reconstruction of patient’s preoperative MRI (C) [color-coded for latency from onset (ms)]. (D) Isolated ripples (occurred either in a single channel or multi-channel) not participating on propagations [35].

So far, HFOs have been mostly investigated using iEEG due to their low amplitude. But iEEG is not always performed prior to surgery. An emerging body of literature has provided strong evidence that HFOs can be recorded non-invasively from ictal and non-ictal recordings with high-density EEG or MEG (for a review see [39]). HFOs were first reported ictally on scalp EEG at the onset of epileptic spasms in children with epilepsy [40,41] and tonic seizures in patients suffering from Lennox-Gastaut syndrome [42]. Interictally, HFOs were first reported on EEG in children with sleep-induced electrical status epilepticus [43] and patients with focal epilepsy [44]. Since these early studies, several groups have shown that HFOs can be detected non-invasively with either EEG [45-52], MEG [53-59], or simultaneous recordings [52,60,61]. Despite these findings the clinical utility of noninvasively recorded HFOs is limited in clinical practice. This may be attributed to the lack of studies that: (i) elucidate the spatial relationship between the gold standard (typically defined by the iEEG HFOs) and the generators of scalp recorded HFOs; and (ii) differentiate scalp recorded pathological from physiological HFOs. To this aim, Tamilia and colleagues [52] recently showed that noninvasive source imaging (via HD-EEG or MEG) localizes ripples with high precision to the intracranial gold standard in children with DRE. Scalp-recorded ripples that co-occur with spikes were prognostic biomarkers of epileptogenicity contrarily to scalp ripples-alone, which more likely reflect physiological events. In another recent study from the same group, Tamilia and colleagues [61] showed the noninvasive mapping of interictal ripple propagation in a cohort of children with DRE using ESI and MSI with HD-EEG and MEG, respectively. In line with previous iEEG studies [10,36], this study showed that ripple propagations, captured by non-invasively estimated virtual sensors, reflect a hierarchical epileptogenic organization where the ripple onset generator estimates the EZ better than mapping the area of spread as well as all ripple generators independently from propagation (Fig. 5). Although these findings indicate that scalp recorded ripples (and particularly the onset of ripple propagations) may be a presurgical asset with significant clinical utility, their low detectability represents the prime challenge, which demands further research with longer recordings, optimized protocols, and the development of instrumentation for high-frequency recordings.

Figure 5: Virtual sensor implantation with MEG and HD-EEG for mapping noninvasively propagation of HFOs across large brain areas.

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(A) Example of iEEG implantation with both subdural and depth electrodes on the left temporal and medial temporal lobe of a 12-year-old boy with DRE. (B) Virtual channel placement based on the coordinates of iEEG electrodes (matched locations). Ripple propagation on MEG (C) and HD-EEG (D) virtual sensors across time [61].

Functional Connectivity

Over the last decade, there is an increasing interest in embedding connectivity analysis into the preoperative evaluation of patients with DRE. This trend has led to a transition from the concept of epilepsy as a “focal” to a “network” brain disorder [62]. Several studies have reported altered networks reflecting neuropathology in patients with focal epilepsy [63-65] as well as generalized epilepsy [66-69]. A meta-analysis that examined the most commonly used metrics in whole-brain networks showed increased average path length and mean average clustering coefficient for patients with epilepsy compared to healthy controls [65]. These findings indicate that -at a group level- focal epilepsy shows widespread detrimental effects, that is, reduced integration and increased segregation, on whole brain interictal network. Such connectivity changes may relate to the co-morbid cognitive and behavioral impairments often reported in patients with focal epilepsy. Several recent iEEG studies have also showed that functional connectivity (FC) is a potentially useful marker for the localization of the EZ [70,71]. FC is a statistical concept, which is defined as the temporal dependency of neuronal activation patterns from anatomically segregated brain regions (Fig. 6). Therefore, the extent to which different brain areas are functionally connected depends on the level of synchronous temporal activity [62]. Most studies report increased functional connectivity in the EZ compared to the rest areas of the brain [72-74], though few others report the opposite effect [75-77]. In a recent study, Shah and colleagues [78] showed higher connectivity for iEEG electrodes localized within the resection area for good outcome patients compared to patients with poorer postoperative seizure control (Fig. 7). Importantly, these measures are derived from interictal data; thus, they offer the opportunity to delineate the EZ without having to provoke a seizure (e.g., by medication withdraw and sleep deprivation) or wait for a seizure to occur.

Figure 6: FC estimates from iEEG.

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Pairwise FC matrices (right) are estimated from iEEG. The connectivity matrix for a brain network comprising N channels is a N×N matrix: diagonal elements of the matrix represent the connectivity of each node with itself (therefore we will assume that they contain only zero values); off-diagonal elements of the connectivity matrix represent the connectivity between pairs of distinct channels.

Figure 7: Patient-level strength selectivity analysis based on iEEG data.

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Spatial maps of FC (nodal strength in beta band) (left) along with corresponding 2D heat maps of nodal strength in all frequency maps (right) for a patient with good (a) and poor (b) surgical outcome. Resection zones are highlighted in green. The figure has been obtained from [71] after permission.

Yet, all these studies were performed on iEEG which has limited field of view and may thus miss the EZ. Measures of FC using noninvasive techniques (i.e., MEG or HD-EEG) have been recently proposed by several groups using virtual sensors that allow an extensive coverage of the entire brain (for a review see [79]). Nissen and colleagues [80] showed that pathological hubs, localized using an atlas-based beamforming on MEG data, had a 100% specificity and 73% accuracy to delineate the EZ. A similar MEG study from the same group reinforced these findings by showing that FC measures estimated noninvasively with virtual sensors correlate with the ones estimated invasively through iEEG [81]. These recent findings open up the possibility of using noninvasive methods, such as MEG, to “predict” later iEEG findings at various cerebrocortical locations, and to use this information for optimal placement of iEEG electrodes in order to sufficiently cover the EZ.

Discussion

The main challenge in the presurgical evaluation of patients with DRE is the successful localization of the EZ while preserving eloquent areas. Such a task requires the precise mapping of different cortical areas, such as the irritative zone and SOZ, ideally by using noninvasive techniques that can delineate these areas with high precision. Emerging technological developments in neuroimaging currently allows the simultaneous recording of MEG and HD-EEG from >550 sensors (when combined together) covering the entire head (Fig. 8), which offer complementary information about the underlying EZ [82]. These developments, particularly when accompanied by advanced source localization methods, can offer critical information about the precise localization of these epileptogenic areas. Yet, the use of these methods is limited in clinical practice, mostly due to: (i) their complexity; (ii) lack of experts knowledgeable about these technologies and practices; (iii) lack of multicenter large prospective studies examining their effectiveness in improving presurgical evaluation and predicting surgical outcome for patients with DRE; (iv) skepticism of epileptologists to rely on their findings; and (v) “disconnection” of software and hardware developers from clinical practice that will allow the simplified use of these methods in a timely manner. Recent developments give hope that novel and more effective biomarkers of epilepsy, such as interictal pathological HFOs, can be localized noninvasively using these tools. These biomarkers would allow augmenting the presurgical evaluation by applying it to an earlier stage in the workup, guiding the implantation of iEEG electrodes, and facilitating patients’ surgical prognosis. Recent FC studies also give hope that these measures can map network hubs in the EZ vicinity having a pathological role in the propagation of epileptic activity to the rest of the brain. Removal of these pathological hubs (instead of the entire EZ), or their disconnection from the EZ, might lead to good surgical outcome. This would be particularly beneficial in patients who were previously not eligible for surgery due to overlap of the EZ with the eloquent cortex.

Figure 8: Novel technological improvements in mapping the EZ.

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Pediatric HD-EEG recordings (a) that can be performed simultaneously with MEG (b) at Cook Children’s Medical Center (Fort Worth, TX, USA). Permission has been obtained from the parents of the child to use his image for the purposes and dissemination of knowledge.

Conclusions

Emerging technological developments are on the horizon for precise delineation of the EZ. Such developments may result in more efficient, less invasive, and less time-demanding presurgical evaluation. Yet, more work is needed to translate these developments into effective clinical tools used in a regular basis in clinical practice.

Funding:

This work was supported by the National Institute of Neurological Disorders and Stroke [1R01NS104116, 2019, and 1R21NS101373, 2018].

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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