Identification of Focal Epileptogenic Networks in Generalized Epilepsy Using Brain Functional Connectivity Analysis of Bilateral Intracranial EEG Signals

Case Report: Focal, generalized, or both: does generalized network involvement preclude successful epilepsy surgery?

We present two cases with focal seizures where scalp electroencephalography (EEG) had prominent features of a developmental and epileptic encephalopathy (DEE): Case 1: a 17-year-old male with complex motor seizures whose EEG demonstrated a slow spike-and-wave pattern and generalized paroxysmal fast activity (GPFA). Case 2: a 12-year-old male with startle-induced asymmetric tonic seizures whose EEG also had a slow spike-and-wave pattern. Both patients had intracranial EEG assessment, and focal cortical resections resulted in long-term seizure freedom and resolution of generalized findings. These cases exemplify patients with focal epilepsy with networks that share similarities to generalized epilepsies, and importantly, these features did not preclude curative epilepsy surgery.

The seizure classification of focal epilepsy based on the network motif analysis

Due to the complexity of focal epilepsy and its risk for transiting to the generalized epilepsy, the development of reliable classification methods to accurately predict and classify focal and generalized seizures is critical for the clinical management of patients with epilepsy. In order to holistically understand the seizure propagation behavior of focal epilepsy, we propose a three-node motif reduced network by respectively simplifying the focal region, surrounding healthy region and their critical regions as the single node. Because three-node motif can richly characterize information evolutions, the motif analysis method could comprehensively investigate the seizure behavior of focal epilepsy. Firstly, we define a new seizure propagation marker value to capture the seizure onsets and intensity. Based on the three-node motif analysis, it is shown that the focal seizure and spreading can be categorized as inhibitory seizure, focal seizure, focal-critical seizure and generalized seizures, respectively. The four types of seizures correspond to specific modal types respectively, reflecting the strong correlation between seizure behavior and information flow evolution. In addition, it is found that the intensity difference of outflow and inflow information from the critical node (connection heterogeneity) and the excitability of the critical node significantly affected the distribution and transition of the four seizure types. In particular, the method of local linear stability analysis also verifies the effectiveness of four types of seizures classification. In sum, this paper computationally confirms the complex dynamic behavior of focal seizures, and the study of criticality is helpful to propose novel seizure control strategies.

An individual data-driven virtual resection model based on epileptic network dynamics in children with intractable epilepsy: a magnetoencephalography interictal activity application

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient's magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient's clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery.

Contributions of Magnetoencephalography to Understanding Mechanisms of Generalized Epilepsies: Blurring the Boundary Between Focal and Generalized Epilepsies?

According to the latest operational 2017 ILAE classification of epileptic seizures, the generalized epileptic seizure is still conceptualized as "originating at some point within and rapidly engaging, bilaterally distributed networks." In contrast, the focal epileptic seizure is defined as "originating within networks limited to one hemisphere." Hence, one of the main concepts of "generalized" and "focal" epilepsy comes from EEG descriptions before the era of source localization, and a presumed simultaneous bilateral onset and bi-synchrony of epileptiform discharges remains a hallmark for generalized seizures. Current literature on the pathophysiology of generalized epilepsy supports the concept of a cortical epileptogenic focus triggering rapidly generalized epileptic discharges involving intact corticothalamic and corticocortical networks, known as the cortical focus theory. Likewise, focal epilepsy with rich connectivity can give rise to generalized spike and wave discharges resulting from widespread bilateral synchronization. Therefore, making this key distinction between generalized and focal epilepsy may be challenging in some cases, and for the first time, a combined generalized and focal epilepsy is categorized in the 2017 ILAE classification. Nevertheless, treatment options, such as the choice of antiseizure medications or surgical treatment, are the reason behind the importance of accurate epilepsy classification. Over the past several decades, plentiful scientific research on the pathophysiology of generalized epilepsy has been conducted using non-invasive neuroimaging and postprocessing of the electromagnetic neural signal by measuring the spatiotemporal and interhemispheric latency of bi-synchronous or generalized epileptiform discharges as well as network analysis to identify diagnostic and prognostic biomarkers for accurate diagnosis of the two major types of epilepsy. Among all the advanced techniques, magnetoencephalography (MEG) and multiple other methods provide excellent temporal and spatial resolution, inherently suited to analyzing and visualizing the propagation of generalized EEG activities. This article aims to provide a comprehensive literature review of recent innovations in MEG methodology using source localization and network analysis techniques that contributed to the literature of idiopathic generalized epilepsy in terms of pathophysiology and clinical prognosis, thus further blurring the boundary between focal and generalized epilepsy.

Arterial spin labelling qualitative assessment in paediatric patients with MRI-negative epilepsy

AIM

To evaluate the usefulness of arterial spin labelling (ASL) qualitative analysis for the localisation of seizure-related perfusion abnormalities in paediatric patients with negative brain magnetic resonance imaging (MRI) epilepsy.

MATERIALS AND METHODS

Forty-two patients with a diagnosis of MRI-negative focal or generalised epilepsy, who underwent electroencephalogram (EEG) and MRI with ASL in the interictal phase were included. Perfusion abnormalities were evaluated through a qualitative assessment and then compared to EEG seizure focus.

RESULTS

Among the 42 patients, 26 had focal epilepsy and 16 had generalised epilepsy. Thirty-three patients (79%) showed a perfusion abnormality, mainly hypoperfusion (74.5% of all ASL alterations), whereas hyperperfused alterations were more represented in patients who experienced the last seizure either less than 48 hours prior to ASL acquisition or in the time interval from 1 week to 1 month prior to ASL acquisition (p=0.034). Concordance of ASL abnormality and EEG focus was found in 33 patients (78.5%), as complete in 17 (40.5%) and as partial in 16 (38%). A trend of higher concordance was found in focal epilepsies compared to generalised epilepsies (p=0.059). The concordance between ASL and EEG major alterations was higher for hyperperfused anomalies than for hypoperfused ones (p=0.009). Variables such as age, sedation, and time from last seizure were not significant contributors for concordance.

CONCLUSIONS

The combined use of qualitative ASL and brain MRI and scalp EEG could be a potential tool in daily clinical practice.

Altered integrity of corpus callosum in generalized epilepsy in relation to seizure lateralization after corpus callosotomy

OBJECTIVE

Generalized-onset seizures are usually conceptualized as engaging bilaterally distributed networks with no clear focus. However, the authors previously reported a case series demonstrating that in some patients with generalized-onset seizures, focal seizure onset could be discovered after corpus callosotomy. The corpus callosum is considered to be a major pathway for seizure generalization in this group of patients. The authors hypothesized that, in patients with generalized-onset seizures, the structure of the corpus callosum could be different between patients who have lateralized seizures and those who have nonlateralized seizures after corpus callosotomy. The authors aimed to evaluate the structural difference through statistical analysis of diffusion tensor imaging (DTI) scalars between these two groups of patients.

METHODS

Thirty-two patients diagnosed with generalized-onset motor seizures and without an MRI lesion were included in this study. Among them, 16 patients developed lateralized epileptic activities after corpus callosotomy, and the remaining 16 patients continued to have nonlateralized seizures after corpus callosotomy. Presurgical DTI studies were acquired to quantify the structural integrity of the corpus callosum.

RESULTS

The DTI analysis showed significant reduction of fractional anisotropy (FA) and increase in radial diffusivity (RD) in the body of the corpus callosum in the lateralized group compared with the nonlateralized group.

CONCLUSIONS

The authors' findings indicate the existence of different configurations of bilateral epileptic networks in generalized epilepsy. Generalized seizures with focal onset relying on rapid spread through the corpus callosum might cause more structural damage related to demyelination in the corpus callosum, showing reduced FA and increased RD. This study suggests that presurgical DTI analysis of the corpus callosum might predict the seizure lateralization after corpus callosotomy.

The 10 Common Evidence-Supported Indications for MEG in Epilepsy Surgery: An Illustrated Compendium

Unfamiliarity with the indications for and benefits of magnetoencephalography (MEG) persists, even in the epilepsy community, and hinders its acceptance to clinical practice, despite the evidence. The wide treatment gap for patients with drug-resistant epilepsy and immense underutilization of epilepsy surgery had similar effects. Thus, educating referring physicians (epileptologists, neurologists, and neurosurgeons) both about the value of epilepsy surgery and about the potential benefits of MEG can achieve synergy and greatly improve the process of selecting surgical candidates. As a practical step toward a comprehensive educational process to benefit potential MEG users, current MEG referrers, and newcomers to MEG, the authors have elected to provide an illustrated guide to 10 everyday situations where MEG can help in the evaluation of people with drug-resistant epilepsy. They are as follows: (1) lacking or imprecise hypothesis regarding a seizure onset; (2) negative MRI with a mesial temporal onset suspected; (3) multiple lesions on MRI; (4) large lesion on MRI; (5) diagnostic or therapeutic reoperation; (6) ambiguous EEG findings suggestive of "bilateral" or "generalized" pattern; (7) intrasylvian onset suspected; (8) interhemispheric onset suspected; (9) insular onset suspected; and (10) negative (i.e., spikeless) EEG. Only their practical implementation and furtherance of personal and collective education will lead to the potentially impactful synergy of the two-MEG and epilepsy surgery. Thus, while fulfilling our mission as physicians, we must not forget that ignoring the wealth of evidence about the vast underutilization of epilepsy surgery - and about the usefulness and value of MEG in selecting surgical candidates - is far from benign neglect.

Neuronal network models of epileptogenesis

Epilepsy is a chronic neurological condition, following some trigger, transforming a normal brain to one that produces recurrent unprovoked seizures. In the search for the mechanisms that best explain the epileptogenic process, there is a growing body of evidence suggesting that the epilepsies are network level disorders. In this review, we briefly describe the concept of neuronal networks and highlight 2 methods used to analyse such networks. The first method, graph theory, is used to describe general characteristics of a network to facilitate comparison between normal and abnormal networks. The second, dynamic causal modelling, is useful in the analysis of the pathways of seizure spread. We concluded that the end results of the epileptogenic process are best understood as abnormalities of neuronal circuitry and not simply as molecular or cellular abnormalities. The network approach promises to generate new understanding and more targeted treatment of epilepsy.

Increased Functional MEG Connectivity as a Hallmark of MRI-Negative Focal and Generalized Epilepsy

Epilepsy is one of the most prevalent neurological diseases with a high morbidity. Accumulating evidence has shown that epilepsy is an archetypical neural network disorder. Here we developed a non-invasive cortical functional connectivity analysis based on magnetoencephalography (MEG) to assess commonalities and differences in the network phenotype in different epilepsy syndromes (non-lesional/cryptogenic focal and idiopathic/genetic generalized epilepsy). Thirty-seven epilepsy patients with normal structural brain anatomy underwent a 30-min resting state MEG measurement with eyes closed. We only analyzed interictal epochs without epileptiform discharges. The imaginary part of coherency was calculated as an indicator of cortical functional connectivity in five classical frequency bands. This connectivity measure was computed between all sources on individually reconstructed cortical surfaces that were surface-aligned to a common template. In comparison to healthy controls, both focal and generalized epilepsy patients showed widespread increased functional connectivity in several frequency bands, demonstrating the potential of elevated functional connectivity as a common pathophysiological hallmark in different epilepsy types. Furthermore, the comparison between focal and generalized epilepsies revealed increased network connectivity in bilateral mesio-frontal and motor regions specifically for the generalized epilepsy patients. Our study indicated that the surface-based normalization of MEG sources of individual brains enables the comparison of imaging findings across subjects and groups on a united platform, which leads to a straightforward and effective disclosure of pathological network characteristics in epilepsy. This approach may allow for the definition of more specific markers of different epilepsy syndromes, and increased MEG-based resting-state functional connectivity seems to be a common feature in MRI-negative epilepsy syndromes.

Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data

Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types.