Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Localization of the epileptogenic network from scalp EEG using a patient-specific whole-brain model

Computational modeling is a key tool for elucidating the neuronal mechanisms underlying epileptic activity. Despite considerable progress, existing models often lack realistic accuracy in representing electrophysiological epileptic activity. In this study, we used a comprehensive human brain model based on a neural mass model, which is tailored to the layered structure of the neocortex and incorporates patient-specific imaging data. This approach allowed the simulation of scalp EEGs in an epileptic patient suffering from type 2 focal cortical dysplasia (FCD). The simulation specifically addressed epileptic activity induced by FCD, faithfully reproducing intracranial interictal epileptiform discharges (IEDs) recorded with electrocorticography. For constructing the patient-specific scalp EEG, we carefully defined a clear delineation of the epileptogenic zone by numerical simulations to ensure fidelity to the topography, polarity, and diffusion characteristics of IEDs. This nuanced approach improves the accuracy of the simulated EEG signal, provides a more accurate representation of epileptic activity, and enhances our understanding of the mechanism behind the epileptogenic networks. The accuracy of the model was confirmed by a postoperative reevaluation with a secondary EEG simulation that was consistent with the lesion's removal. Ultimately, this personalized approach may prove instrumental in optimizing and tailoring epilepsy treatment strategies.

Combining interictal intracranial EEG and fMRI to compute a dynamic resting-state index for surgical outcome validation

Introduction

Accurate localization of the seizure onset zone (SOZ) is critical for successful epilepsy surgery but remains challenging with current techniques. We developed a novel seizure onset network characterization tool that combines dynamic biomarkers of resting-state intracranial stereoelectroencephalography (rs-iEEG) and resting-state functional magnetic resonance imaging (rs-fMRI), vetted against surgical outcomes. This approach aims to reduce reliance on capturing seizures during invasive monitoring to pinpoint the SOZ.

Methods

We computed the source-sink index (SSI) from rs-iEEG for all implanted regions and from rs-fMRI for regions identified as potential SOZs by noninvasive modalities. The SSI scores were evaluated in 17 pediatric drug-resistant epilepsy (DRE) patients (ages 3-15 years) by comparing outcomes classified as successful (Engel I or II) versus unsuccessful (Engel III or IV) at 1 year post-surgery.

Results

Of 30 reviewed patients, 17 met the inclusion criteria. The combined dynamic index (im-DNM) integrating rs-iEEG and rs-fMRI significantly differentiated good (Engel I-II) from poor (Engel III-IV) surgical outcomes, outperforming the predictive accuracy of individual biomarkers from either modality alone.

Conclusion

The combined dynamic network model demonstrated superior predictive performance than standalone rs-fMRI or rs-iEEG indices.

Significance

By leveraging interictal data from two complementary modalities, this combined approach has the potential to improve epilepsy surgical outcomes, increase surgical candidacy, and reduce the duration of invasive monitoring.

Localizing the seizure onset zone and predicting the surgery outcomes in patients with drug-resistant epilepsy: A new approach based on the causal network

BACKGROUND AND OBJECTIVE

Accurate localization of the seizure onset zone (SOZ) is crucial for surgical treatment in patients with drug-resistant epilepsy (DRE). However, clinical identification of SOZ often relies on physician experience and has a certain subjectivity. Therefore, it is emergent to develop quantitative computational tools to assist clinicians in identifying SOZ.

METHODS

We conduct a retrospective study on intracranial electroencephalography (iEEG) data from 46 patients with DRE. The interactions between different brain regions are quantified by using the phase transfer entropy (PTE), based on which the causal influence index (CII) is proposed to quantify the degree of influence of nodes on the network. Subsequently, the features extracted by the CII are used to construct a random forest classification model, which the performance in identifying SOZ and the generalizability are validated in patients with successful surgeries. Then, based on the CII features of the clinically labeled SOZ, a logistic regression prediction model is constructed to predict the probability of surgical success. The statistical analysis between patients with successful and failed surgery is conducted with the Mann-Whitney U test. Finally, the consistency between the predicted SOZ and the clinically labeled SOZ is verified across different Engel classes.

RESULTS

The classification model combining the low-frequency and high-frequency features can achieve an accuracy of 82.18% (sensitivity: 85.01%, specificity: 79.69%) and an area under curve (AUC) of 0.90 in identifying SOZ. Furthermore, the model exhibits strong generalizability in identifying SOZ in patients with MRI lesional and non-lesional, as well as those implanted with electrocorticography (ECOG) and stereotactic EEG (SEEG) electrodes. Moreover, the prediction model could achieve an average accuracy of 79.8% and an AUC of 0.84. Of note, the prediction of surgical success probability is significant between patients with successful and failed surgeries (P<0.001). Correspondingly, the highest consistency between model-predicted SOZ and clinically labeled SOZ can be observed in patients with successful surgeries, but this consistency gradually decreases with increasing Engel classes.

CONCLUSIONS

These results demonstrate that the CII may be a potential biomarker for identifying the SOZ in patients with DRE, which may provide a new perspective for the treatment of epilepsy.

Gamma amplitude-envelope correlations are strongly elevated within hyperexcitable networks in focal epilepsy

Methods to quantify cortical hyperexcitability are of enormous interest for mapping epileptic networks in patients with focal epilepsy. We hypothesize that, in the resting state, cortical hyperexcitability increases firing-rate correlations between neuronal populations within seizure onset zones (SOZs). This hypothesis predicts that in the gamma frequency band (40-200 Hz), amplitude envelope correlations (AECs), a relatively straightforward measure of functional connectivity, should be elevated within SOZs compared to other areas. To test this prediction, we analyzed archived samples of interictal electrocorticographic (ECoG) signals recorded from patients who became seizure-free after surgery targeting SOZs identified by multiday intracranial recordings. We show that in the gamma band, AECs between nodes within SOZs are markedly elevated relative to those elsewhere. AEC-based node strength, eigencentrality, and clustering coefficient are also robustly increased within the SOZ with maxima in the low-gamma band (permutation test Z-scores > 8) and yield moderate discriminability of the SOZ using ROC analysis (maximal mean AUC ~ 0.73). By contrast to AECs, phase locking values (PLVs), a measure of narrow-band phase coupling across sites, and PLV-based graph metrics discriminate the seizure onset nodes weakly. Our results suggest that gamma band AECs may provide a clinically useful marker of cortical hyperexcitability in focal epilepsy.

Seizure Onset Zone Localization in Focal Epilepsy using stereo-EEG: SCSA and Visibility Graph complementary features

Despite how limiting the symptoms of Epilepsy are in daily life, over a third of epileptic conditions are drug-resistant. These seizures stem from a specific region of the brain: the Seizure Onset Zone (SOZ). The available treatment options include surgical resection or neurostimulation of the SOZ and both options require its accurate localization. In this scope, we aim to analyse stereo-electroencephalograph (s-EEG) data from a feature engineering perspective to propose reliable computer-aided SOZ localization algorithms. This paper investigates Semi-Classical Signal Analysis (SCSA) and Visibility Graphs (VG) features and establishes their complementarity. The study considers brain -scale signals simulated through the Epileptor model using The Virtual Brain (TVB). Results have been undeniably satisfactory and a valid contribution to Seizure Onset Zone localization.Clinical relevance- The paper contributes to the design of frameworks for assisting medical experts in Seizure Onset Zone localization in focal epilepsy.

Generalisability of epileptiform patterns across time and patients

The complexity of localising the epileptogenic zone (EZ) contributes to surgical resection failures in achieving seizure freedom. The distinct patterns of epileptiform activity during interictal and ictal phases, varying across patients, often lead to suboptimal localisation using electroencephalography (EEG) features. We posed two key questions: whether neural signals reflecting epileptogenicity generalise from interictal to ictal time windows within each patient, and whether epileptiform patterns generalise across patients. Utilising an intracranial EEG dataset from 55 patients, we extracted a large battery of simple to complex features from stereo-EEG (SEEG) and electrocorticographic (ECoG) neural signals during interictal and ictal windows. Our features (n = 34) quantified many aspects of the signals including statistical moments, complexities, frequency-domain and cross-channel network attributes. Decision tree classifiers were then trained and tested on distinct time windows and patients to evaluate the generalisability of epileptogenic patterns across time and patients, respectively. Evidence strongly supported generalisability from interictal to ictal time windows across patients, particularly in signal power and high-frequency network-based features. Consistent patterns of epileptogenicity were observed across time windows within most patients, and signal features of epileptogenic regions generalised across patients, with higher generalisability in the ictal window. Signal complexity features were particularly contributory in cross-patient generalisation across patients. These findings offer insights into generalisable features of epileptic neural activity across time and patients, with implications for future automated approaches to supplement other EZ localisation methods.

High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker

Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. The purpose of this study is to improve the performance of model predicting the epilepsy surgery outcomes using genetic neural network (GNN) model based on a hybrid intracranial electroencephalography (iEEG) marker. We extracted 21 SOZ related markers based on iEEG data from 79 epilepsy patients. The least absolute shrinkage and selection operator (LASSO) regression was employed to integrated seven markers, selected after testing in pairs with all 21 biomarkers and 7 machine learning models, into a hybrid marker. Based on the hybrid marker, we devised a GNN model and compared its predictive performance for surgical outcomes with six other mainstream machine-learning models. Compared to the mainstream models, underpinning the GNN with the hybrid iEEG marker resulted in a better prediction of surgical outcomes, showing a significant increase of the prediction accuracy from approximately 87% to 94.3% (P = 0.0412). This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables.

Network-motif delay differential analysis of brain activity during seizures

Delay Differential Analysis (DDA) is a nonlinear method for analyzing time series based on principles from nonlinear dynamical systems. DDA is extended here to incorporate network aspects to improve the dynamical characterization of complex systems. To demonstrate its effectiveness, DDA with network capabilities was first applied to the well-known Rössler system under different parameter regimes and noise conditions. Network-motif DDA, based on cortical regions, was then applied to invasive intracranial electroencephalographic data from drug-resistant epilepsy patients undergoing presurgical monitoring. The directional network motifs between brain areas that emerge from this analysis change dramatically before, during, and after seizures. Neural systems provide a rich source of complex data, arising from varying internal states generated by network interactions.

Passive and active markers of cortical excitability in epilepsy

Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

Localizing epileptogenic zones with high-frequency oscillations and directed connectivity

PURPOSE

Precise localization of the epileptogenic zone (EZ) is essential for epilepsy surgery. Existing methods often fail to detect slow onset patterns or similar neural activities presented in the recorded signals. To address this issue, we propose a new measure to quantify epileptogenicity, i.e., the connectivity high-frequency epileptogenicity index (cHFEI).

METHODS

The cHFEI method combines directed connectivity and high-frequency oscillations (HFOs) to measure the epileptogenicity of regions involved in a brain network. By applying this method to stereoelectroencephalography (SEEG) recordings of 49 seizures in 20 patients, we calculated the accuracy, sensitivity, and precision with a visually identified epileptogenic zone as a reference. The performance was evaluated by the confusion matrix and the area under the receiver operating characteristic (ROC) curve.

RESULTS

Epileptic network estimation based on cHFEI successfully distinguished brain regions involved in seizure onset from the propagation network. Moreover, cHFEI outperformed other existing detection methods in the estimation of EZs in all patients, with an average area under the ROC curve of 0.88 and an accuracy of 0.85.

CONCLUSIONS

cHFEI can characterize EZ in a robust manner despite various seizure onset patterns and has potential application in epilepsy therapy.

Quantitative approaches to guide epilepsy surgery from intracranial EEG

Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.

Resting-state SEEG-based brain network analysis for the detection of epileptic area

BACKGROUND

Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.

NEW METHOD

The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes.

RESULTS

By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p < 0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone.

CONCLUSIONS AND SIGNIFICANCE

The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.

Source-sink connectivity: a novel interictal EEG marker for seizure localization

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Implementation of a Morphological Filter for Removing Spikes from the Epileptic Brain Signals to Improve Identification Ripples

Epilepsy is a very common disease affecting at least 1% of the population, comprising a number of over 50 million people. As many patients suffer from the drug-resistant version, the number of potential treatment methods is very small. However, since not only the treatment of epilepsy, but also its proper diagnosis or observation of brain signals from recordings are important research areas, in this paper, we address this very problem by developing a reliable technique for removing spikes and sharp transients from the baseline of the brain signal using a morphological filter. This allows much more precise identification of the so-called epileptic zone, which can then be resected, which is one of the methods of epilepsy treatment. We used eight patients with 5 KHz data set and depended upon the Staba 2002 algorithm as a reference to detect the ripples. We found that the average sensitivity and false detection rate of our technique are significant, and they are ∼94% and ∼14%, respectively.

A complex systems view on the current hypotheses of epilepsy pharmacoresistance

Drug-resistant epilepsy remains to this day as a highly prevalent condition affecting around one-third of patients with epilepsy, despite all the research and the development of several new antiseizure medications (ASMs) over the last decades. Epilepsies are multifactorial complex diseases, commonly associated with psychiatric, neurological, and somatic comorbidities. Thus, to solve the puzzling problem of pharmacoresistance, the diagnosis and modeling of epilepsy and comorbidities need to change toward a complex system approach. In this review, we have summarized the sequence of events for the definition of epilepsies and comorbidities, the search for mechanisms, and the major hypotheses of pharmacoresistance, drawing attention to some of the many converging aspects between the proposed mechanisms, their supporting evidence, and comorbidities-related alterations. The use of systems biology applied to epileptology may lead to the discovery of new targets and the development of new ASMs, as may advance our understanding of the epilepsies and their comorbidities, providing much deeper insight on multidrug pharmacoresistance.

Normative intracranial EEG maps epileptogenic tissues in focal epilepsy

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy.

Characterizing Brain Network Dynamics using Persistent Homology in Patients with Refractory Epilepsy

Epilepsy is a common serious neurological disorder that affects more than 65 million persons worldwide and it is characterized by repeated seizures that lead to higher mortality and disabilities with corresponding negative impact on the quality of life of patients. Network science methods that represent brain regions as nodes and the interactions between brain regions as edges have been extensively used in characterizing network changes in neurological disorders. However, the limited ability of graph network models to represent high dimensional brain interactions are being increasingly realized in the computational neuroscience community. In particular, recent advances in algebraic topology research have led to the development of a large number of applications in brain network studies using topological structures. In this paper, we build on a fundamental construct of cliques, which are all-to-all connected nodes with a k-clique in a graph G (V, E), where V is set of nodes and E is set of edges, consisting of k-nodes to characterize the brain network dynamics in epilepsy patients using topological structures. Cliques represent brain regions that are coupled for similar functions or engage in information exchange; therefore, cliques are suitable structures to characterize the dynamics of brain dynamics in neurological disorders. We propose to detect and use clique structures during well-defined clinical events, such as epileptic seizures, to combine non-linear correlation measures in a matrix with identification of geometric structures underlying brain connectivity networks to identify discriminating features that can be used for clinical decision making in epilepsy neurological disorder.

Sources and Sinks in Interictal iEEG Networks: An iEEG Marker of the Epileptogenic Zone

Around 30% of epilepsy patients have seizures that cannot be controlled with medication. The most effective treatments for medically resistant epilepsy are interventions that surgically remove the epileptogenic zone (EZ), the regions of the brain that initiate seizure activity. A precise identification of the EZ is essential for surgical success but unfortunately, current success rates range from 20-80%. Localization of the EZ requires visual inspection of intracranial EEG (iEEG) recordings during seizure events. The need for seizure occurrence makes the process both costly and time-consuming and in the end, less than 1% of the data captured is used to assist in EZ localization. In this study, we aim to leverage interictal (between seizures) data to localize the EZ. We develop and test the source-sink index as an interictal iEEG marker by identifying two groups of network nodes from a patient's interictal iEEG network: those that inhibit a set of their neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, we i) estimate patient-specific dynamical network models from interictal iEEG data and ii) compute a source-sink index for every network node (iEEG channel) to identify pathological nodes that correspond to the EZ. Our results suggest that in patients with successful surgical outcomes, the source-sink index clearly separates the clinically identified EZ (CA-EZ) channels from other channels whereas in patients with failed outcomes CA-EZ channels cannot be distinguished from the rest of the network.

Transfer Entropy between Intracranial EEG Nodes Highlights Network Dynamics that Cause and Stop Epileptic Seizures

Transfer entropy (TE) is used to examine the connectivity between nodes and the roles of nodes in epileptic neural networks during rest, moments before seizure, during seizure, and moments after seizure. There is a set of nodes that dominate information flow to epileptogenic zone (EZ) nodes, regions that trigger seizure, and non-EZ nodes during rest. The TE from the dominant to the EZ nodes decreases shortly before a seizure event and reaches a minimum during seizure. During the seizure, the dominant nodes cease or only weakly interact with the EZ nodes. This supports the hypothesis that seizure occurs when some nodes stop inhibiting the EZ nodes. The TE from the dominant to the EZ nodes peaks immediately after seizure, suggesting that seizure may stop when the brain exerts the highest level of information flow/activation/communication to the EZ nodes. The information flow from the dominant to EZ nodes is different from that to non-EZ nodes. This TE dynamics entering and exiting seizures may identify more accurately the EZ nodes, which may improve surgical planning.

Neural fragility as an EEG marker of the seizure onset zone

Over 15 million patients with epilepsy worldwide do not respond to drugs. Successful surgical treatment requires complete removal or disconnection of the seizure onset zone (SOZ), brain region(s) where seizures originate. Unfortunately, surgical success rates vary between 30 and 70% because no clinically validated biological marker of the SOZ exists. We develop and retrospectively validate a new electroencephalogram (EEG) marker-neural fragility-in a retrospective analysis of 91 patients by using neural fragility of the annotated SOZ as a metric to predict surgical outcomes. Fragility predicts 43 out of 47 surgical failures, with an overall prediction accuracy of 76% compared with the accuracy of clinicians at 48% (successful outcomes). In failed outcomes, we identify fragile regions that were untreated. When compared to 20 EEG features proposed as SOZ markers, fragility outperformed in predictive power and interpretability, which suggests neural fragility as an EEG biomarker of the SOZ.

Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

Searching for a paradigm shift in the research on the epilepsies and associated neuropsychiatric comorbidities. From ancient historical knowledge to the challenge of contemporary systems complexity and emergent functions

In this review, we will discuss in four scenarios our challenges to offer possible solutions for the puzzle associated with the epilepsies and neuropsychiatric comorbidities. We need to recognize that (1) since quite old times, human wisdom was linked to the plural (distinct global places/cultures) perception of the Universe we are in, with deep respect for earth and nature. Plural ancestral knowledge was added with the scientific methods; however, their joint efforts are the ideal scenario; (2) human behavior is not different than animal behavior, in essence the product of Darwinian natural selection; knowledge of animal and human behavior are complementary; (3) the expression of human behavior follows the same rules that complex systems with emergent properties, therefore, we can measure events in human, clinical, neurobiological situations with complexity systems' tools; (4) we can use the semiology of epilepsies and comorbidities, their neural substrates, and potential treatments (including experimental/computational modeling, neurosurgical interventions), as a source and collection of integrated big data to predict with them (e.g.: machine/deep learning) diagnosis/prognosis, individualized solutions (precision medicine), basic underlying mechanisms and molecular targets. Once the group of symptoms/signals (with a myriad of changing definitions and interpretations over time) and their specific sequences are determined, in epileptology research and clinical settings, the use of modern and contemporary techniques such as neuroanatomical maps, surface electroencephalogram and stereoelectroencephalography (SEEG) and imaging (MRI, BOLD, DTI, SPECT/PET), neuropsychological testing, among others, are auxiliary in the determination of the best electroclinical hypothesis, and help design a specific treatment, usually as the first attempt, with available pharmacological resources. On top of ancient knowledge, currently known and potentially new antiepileptic drugs, alternative treatments and mechanisms are usually produced as a consequence of the hard, multidisciplinary, and integrated studies of clinicians, surgeons, and basic scientists, all over the world. The existence of pharmacoresistant patients, calls for search of other solutions, being along the decades the surgeries the most common interventions, such as resective procedures (i.e., selective or standard lobectomy, lesionectomy), callosotomy, hemispherectomy and hemispherotomy, added by vagus nerve stimulation (VNS), deep brain stimulation (DBS), neuromodulation, and more recently focal minimal or noninvasive ablation. What is critical when we consider the pharmacoresistance aspect with the potential solution through surgery, is still the pursuit of localization-dependent regions (e.g.: epileptogenic zone (EZ)), in order to decide, no matter how sophisticated are the brain mapping tools (EEG and MRI), the size and location of the tissue to be removed. Mimicking the semiology and studying potential neural mechanisms and molecular targets - by means of experimental and computational modeling - are fundamental steps of the whole process. Concluding, with the conjunction of ancient knowledge, coupled to critical and creative contemporary, scientific (not dogmatic) clinical/surgical, and experimental/computational contributions, a better world and of improved quality of life can be offered to the people with epilepsy and neuropsychiatric comorbidities, who are still waiting (as well as the scientists) for a paradigm shift in epileptology, both in the Basic Science, Computational, Clinical, and Neurosurgical Arenas. This article is part of the Special Issue "NEWroscience 2018".

Localizing the epileptogenic zone

PURPOSE OF REVIEW

Epilepsy surgery is the therapy of choice for 30-40% of people with focal drug-resistant epilepsy. Currently only ∼60% of well selected patients become postsurgically seizure-free underlining the need for better tools to identify the epileptogenic zone. This article reviews the latest neurophysiological advances for EZ localization with emphasis on ictal EZ identification, interictal EZ markers, and noninvasive neurophysiological mapping procedures.

RECENT FINDINGS

We will review methods for computerized EZ assessment, summarize computational network approaches for outcome prediction and individualized surgical planning. We will discuss electrical stimulation as an option to reduce the time needed for presurgical work-up. We will summarize recent research regarding high-frequency oscillations, connectivity measures, and combinations of multiple markers using machine learning. This latter was shown to outperform single markers. The role of NREM sleep for best identification of the EZ interictally will be discussed. We will summarize recent large-scale studies using electrical or magnetic source imaging for clinical decision-making.

SUMMARY

New approaches based on technical advancements paired with artificial intelligence are on the horizon for better EZ identification. They are ultimately expected to result in a more efficient, less invasive, and less time-demanding presurgical investigation.

Classification of Stereo-EEG Contacts in White Matter vs. Gray Matter Using Recorded Activity

For epileptic patients requiring resective surgery, a modality called stereo-electroencephalography (SEEG) may be used to monitor the patient's brain signals to help identify epileptogenic regions that generate and propagate seizures. SEEG involves the insertion of multiple depth electrodes into the patient's brain, each with 10 or more recording contacts along its length. However, a significant fraction (≈ 30% or more) of the contacts typically reside in white matter or other areas of the brain which can not be epileptogenic themselves. Thus, an important step in the analysis of SEEG recordings is distinguishing between electrode contacts which reside in gray matter vs. those that do not. MRI images overlaid with CT scans are currently used for this task, but they take significant amounts of time to manually annotate, and even then it may be difficult to determine the status of some contacts. In this paper we present a fast, automated method for classifying contacts in gray vs. white matter based only on the recorded signal and relative contact depth. We observe that bipolar referenced contacts in white matter have less power in all frequencies below 150 Hz than contacts in gray matter, which we use in a Bayesian classifier to attain an average area under the receiver operating characteristic curve of 0.85 ± 0.079 (SD) across 29 patients. Because our method gives a probability for each contact rather than a hard labeling, and uses a feature of the recorded signal that has direct clinical relevance, it can be useful to supplement decision-making on difficult to classify contacts or as a rapid, first-pass filter when choosing subsets of contacts from which to save recordings.

Validation of virtual resection on intraoperative interictal data acquired during epilepsy surgery

OBJECTIVE

A 'Virtual resection' consists of computationally simulating the effect of an actual resection on the brain. We validated two functional connectivity based virtual resection methods with the actual connectivity measured using post-resection intraoperative recordings.

METHODS

A non-linear association index was applied to pre-resection recordings from 11 extra-temporal focal epilepsy patients. We computed two virtual resection strategies: first, a 'naive' one obtained by simply removing from the connectivity matrix the electrodes that were resected; second, a virtual resection with partialization accounting for the influence of resected electrodes on not-resected electrodes. We validated the virtual resections with two analysis: 1) We tested with a Kolmogorov-Smirnov test if the distributions of connectivity values after the virtual resections differed from the actual post-resection connectivity distribution; 2) we tested if the overall effect of the resection measured by contrasting pre-resection and post-resection connectivity values is detectable with the virtual resection approach using a Kolmogorv-Smirnov test.

RESULTS

The estimation of post-resection connectivity values did not succeed for both methods. In the second analysis, the naive method failed completely to detect the effect found between pre-resection and post-resection connectivity distributions, while the partialization method agreed with post-resection measurements in detecting a drop connectivity compared to pre-resection recordings.

CONCLUSION

Our findings suggest that the partialization technique is superior to the naive method in detecting the overall effect after the resection.

SIGNIFICANCE

We pointed out how a realistic validation based on actual post-resection recordings reveals that virtual resection methods are not yet mature to inform the clinical decision-making.

Network Fragility for Seizure Genesis in an Acute in vivo Model of Epilepsy

Epilepsy affects over 50 million people worldwide and 30% of patients' seizures are medically refractory. The process of localizing and removing the epileptogenic zone is error-prone and ill-posed in part because we do not understand how epilepsy manifests. It has recently been proposed that the epileptic cortex is fragile in the sense that seizures manifest through small perturbations in the synaptic connections that render the entire cortical network unstable. If the fragility of the cortical network could be computed over a period in which seizure genesis occurs, then it might elucidate network mechanisms correlated to the epileptogenic zone. In this study, we used local field potentials (LFP) from neocortex by implementing an acute model of epilepsy in mice. These recordings were used to develop a dynamical network model that quantifies the fragility of the nodes from LFP epochs of baseline activity, preictal and ictal states. Fragility was quantified by the generation of a linear time-varying model to which we then applied a perturbation to determine the sensitivity of nodes in the network. Spatiotemporal fragility maps showed clear quantifiable changes in the epileptogenic network's properties throughout different states of seizure genesis. We quantified this difference over a baseline, preictal and ictal periods to show that network fragility is modulated in the manifestation of epilepsy.

High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

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We analyze interictal iEEG recordings and neuroimaging from epilepsy patients

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We find that high interictal strength selectivity is associated with better outcomes

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This effect appears to be driven largely by connectivity within the resection zone

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Interictal recordings can guide identification of seizure-generating networks

Redaction of false high frequency oscillations due to muscle artifact improves specificity to epileptic tissue

OBJECTIVE

High Frequency Oscillations (HFOs) are a promising biomarker of epilepsy. HFOs are typically acquired on intracranial electrodes, but contamination from muscle artifacts is still problematic in HFO analysis. This paper evaluates the effect of myogenic artifacts on intracranial HFO detection and how to remove them.

METHODS

Intracranial EEG was recorded in 31 patients. HFOs were detected for the entire recording using an automated algorithm. When available, simultaneous scalp EEG was used to identify periods of muscle artifact. Those markings were used to train an automated scalp EMG detector and an intracranial EMG detector. Specificity to epileptic tissue was evaluated by comparison with seizure onset zone and resected volume in patients with good outcome.

RESULTS

EMG artifacts are frequent and produce large numbers of false HFOs, especially in the anterior temporal lobe. The scalp and intracranial EMG detectors both had high accuracy. Removing false HFOs improved specificity to epileptic tissue.

CONCLUSIONS

Evaluation of HFOs requires accounting for the effect of muscle artifact. We present two tools that effectively mitigate the effect of muscle artifact on HFOs.

SIGNIFICANCE

Removing muscle artifacts improves the specificity of HFOs to epileptic tissue. Future HFO work should account for this effect, especially when using automated algorithms or when scalp electrodes are not present.

Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.

Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often require observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during, and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

Editorial: New Trends in Connectomics

Connectomics is an integral part of network neuroscience. The field has undergone rapid expansion over recent years and increasingly involves a blend of experimental and computational approaches to brain connectivity. This Focus Feature on “New Trends in Connectomics” aims to track the progress of the field and its many applications across different neurobiological systems and species.