Neocortical seizure foci localization by means of a directed transfer function method

Preictal connectivity dynamics: Exploring inflow and outflow in iEEG networks

Introduction

Focal resective surgery can be an effective treatment option for patients with refractory epilepsy if the seizure onset zone is accurately identied through intracranial EEG recordings. The traditional concept of the epileptogenic zone has expanded to the notion of an epileptogenic network, emphasizing the role of interconnected brain regions in seizure generation. Precise delineation of this network is essential for optimizing surgical outcomes. Over the past 3 decades, several quantitative connectivity methods have been developed to study the interactions between the seizure onset zone and non-involved regions. Despite these advances, the mechanisms governing the transition from interictal to ictal periods remain poorly understood. In this study, we investigated preictal interactions between the seizure onset zone and the broader network using directed connectivity measures.

Methods

We evaluated their effectiveness in identifying seizure onset zones using a multicenter intracranial EEG dataset, encompassing 243 seizures from 61 patients. Directed transfer function and partial directed coherence were used to extract connectivity matrices during 28-seconds of preictal period in patients with good surgery outcomes. Inflow and outflow metrics were computed for iEEG electrode contact annotated as seizure onset zone and the performance of each metric is assessed in disentangling these electrodes from the rest of the network.

Results

We observed two distinct patterns of network connectivity preceding seizure onset. While there was an increase in inflow of information to seizure onset electrodes in one subset of patients, in the other subset, there was a prominent outflow of information from seizure onset electrodes to the rest of the network, suggesting distinct connectivity patterns associated with the seizure onset zone across patients. Further analyses showed that patients who underwent the grid/strip/depth implantation approach exhibited significantly higher area under curve (AUC) than those with electrocorticography (ECoG) or stereo-electroencephalography (sEEG) alone. Finally, examining the influence of lesional vs non-lesional neuroimaging status demonstrated that a greater proportion of high-inflow and high-outflow were lesional.

Conclusion

Our findings reinforce the notion that seizure generation is driven by dynamic shifts in information flow within the brain's functional network. The preictal connectivity patterns observed --either increased inflow to the seizure onset zone or high outflow from it --underscore the network reorganization involved in epileptic transitions. These results emphasize epilepsy as a network-level phenomenon, supporting the use of network concepts for better understanding seizure dynamics and improving surgical localization strategies.

SEEG-Based Bilateral Seizure Network Analysis for Neurostimulation Treatment

OBJECTIVE

Epilepsy patients with drug-resistant seizures emanating from two or more distinct regions of left and right hemispheres are the primary candidates for neurostimulation treatment. Stereo-electroencephalography (SEEG) is a minimally invasive technique to monitor and evaluate brain activities during seizures before stimulator implantation.

METHODS

This work proposes a seizure network modeling method using SEEG to analyze the functional connectivity of epileptogenic zone during bilateral seizures. Network nodes are selected subset of SEEG contact points, and network edges are directed signal correlations calculated from directed transfer function. Based on signal directionality, four connectivity values are extracted to measure the intra- and inter-activities that are within or between the left and right hemispheres, respectively. Statistical difference between connectivity values is used to quantify the seizure impact of each hemisphere. A subset of network nodes is selected from impactful side as stimulation target candidates.

RESULTS

Experimental results are validated on ten patients having different seizure types with bilateral onset. Each seizure type has specific connectivity patterns that show different importance from each brain side. Selection of neurostimulation targets from primary side are consistent with clinicians' decision. Relationships are found among connectivity differences, seizure types and stimulation outcomes.

CONCLUSION

Using SEEG signals, we can capture specific connectivity differences associated with bilateral seizure networks. Such differences are related with corresponding neurostimulation targets and stimulating outcomes.

SIGNIFICANCE

The proposed work elucidates the difference of network connectivity for bilateral patients, and assists clinicians to choose the stimulation targets and to predict the potential outcomes.

A Case Study on EEG Signal Correlation Towards Potential Epileptic Foci Triangulation

The precise localization of epileptic foci with the help of EEG or iEEG signals is still a clinical challenge with current methodology, especially if the foci are not close to individual electrodes. On the research side, dipole reconstruction for focus localization is a topic of recent and current developments. Relatively low numbers of recording electrodes cause ill-posed and ill-conditioned problems in the inversion of lead-field matrices to calculate the focus location. Estimations instead of tissue conductivity measurements further deteriorate the precision of location tasks. In addition, time-resolved phase shifts are used to describe connectivity. We hypothesize that correlations over runtime approaches might be feasible to predict seizure foci with adequate precision. In a case study on EEG correlation in a healthy subject, we found repetitive periods of alternating high correlation in the short (20 ms) and long (300 ms) range. During these periods, a numerical determination of proportions of predominant latency and, newly established here, directionality is possible, which supports the identification of loops that, according to current opinion, manifest themselves in epileptic seizures. In the future, this latency and directionality analysis could support focus localization via dipole reconstruction using new triangulation calculations.

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.

Electroencephalography Neurofeedback Training with Focus on the State of Attention: An Investigation Using Source Localization and Effective Connectivity

Identifying brain activity and flow direction can help in monitoring the effectiveness of neurofeedback tasks that aim to treat cognitive deficits. The goal of this study was to compare the neuronal electrical activity of the cortex between individuals from two groups-low and high difficulty-based on a spatial analysis of electroencephalography (EEG) acquired through neurofeedback sessions. These sessions require the subjects to maintain their state of attention when executing a task. EEG data were collected during three neurofeedback sessions for each person, including theta and beta frequencies, followed by a comprehensive preprocessing. The inverse solution based on cortical current density was applied to identify brain regions related to the state of attention. Thereafter, effective connectivity between those regions was estimated using the Directed Transfer Function. The average cortical current density of the high-difficulty group demonstrated that the medial prefrontal, dorsolateral prefrontal, and temporal regions are related to the attentional state. In contrast, the low-difficulty group presented higher current density values in the central regions. Furthermore, for both theta and beta frequencies, for the high-difficulty group, flows left and entered several regions, unlike the low-difficulty group, which presented flows leaving a single region. In this study, we identified which brain regions are related to the state of attention in individuals who perform more demanding tasks (high-difficulty group).

Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger

The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (31.5 - 60.5 H z ) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.

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.

Cross-bispectrum connectivity of intracranial EEG: A novel approach to seizure onset zone localization

Connectivity analyses of intracranial electroencephalography (iEEG) could guide surgical planning for epilepsy surgery by improving the delineation of the seizure onset zone. Traditional approaches fail to quantify important interactions between frequency components. To assess if effective connectivity based on cross-bispectrum -a measure of nonlinear multivariate cross-frequency coupling- can quantitatively identify generators of seizure activity, cross-bispectrum connectivity between channels was computed from iEEG recordings of 5 patients (34 seizures) with good postsurgical outcome. Personalized thresholds of 50% and 80% of the maximum coupling values were used to identify generating electrode channels. In all patients, outflow coupling between α (8-15 Hz) and β (16-31 Hz) frequencies identified at least one electrode inside the resected seizure onset zone. With the 50% and 80% thresholds respectively, an average of 5 (44.7%; specificity = 82.6%) and 2 (22.5%; specificity = 99.0%) resected electrodes were correctly identified. Results show promise for the automatic identification of the seizure onset zone based on cross-bispectrum connectivity analysis.

Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level

In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.

[Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection]

Drug-refractory epilepsy (DRE) may be treated by surgical intervention. Intracranial EEG has been widely used to localize the epileptogenic zone (EZ). Most studies of epileptic network focus on the features of EZ nodes, such as centrality and degrees. It is difficult to apply those features to the treatment of individual patients. In this study, we proposed a spatial neighbor expansion approach for EZ localization based on a neural computational model and epileptic network reconstruction. The virtual resection method was also used to validate the effectiveness of our approach. The electrocorticography (ECoG) data from 11 patients with DRE were analyzed in this study. Both interictal data and surgical resection regions were used. The results showed that the rate of consistency between the localized regions and the surgical resections in patients with good outcomes was higher than that in patients with poor outcomes. The average deviation distance of the localized region for patients with good outcomes and poor outcomes were 15 mm and 36 mm, respectively. Outcome prediction showed that the patients with poor outcomes could be improved when the brain regions localized by the proposed approach were treated. This study provides a quantitative analysis tool for patient-specific measures for potential surgical treatment of epilepsy.

Interictal Connectivity Revealed by Granger Analysis of Stereoelectroencephalography: Association With Ictal Onset Zone, Resection, and Outcome

BACKGROUND

Stereoelectroencephalography (sEEG) facilitates electrical sampling and evaluation of complex deep-seated, dispersed, and multifocal locations. Granger causality (GC), previously used to study seizure networks using interictal data from subdural grids, may help identify the seizure-onset zone from interictal sEEG recordings.

OBJECTIVE

To examine whether statistical analysis of interictal sEEG helps identify surgical target sites and whether surgical resection of highly ranked nodes correspond to favorable outcomes.

METHODS

Ten minutes of extraoperative recordings from sequential patients who underwent sEEG evaluation were analyzed (n = 20). GC maps were compared with clinically defined surgical targets using rank order statistics. Outcomes of patients with focal resection/ablation with median follow-up of 3.6 years were classified as favorable (Engel 1, 2) or poor (Engel 3, 4) to assess their relationship with the removal of highly ranked nodes using the Wilcoxon rank-sum test.

RESULTS

In 12 of 20 cases, the rankings of contacts (based on the sum of outward connection weights) mapped to the seizure-onset zone showed higher causal node connectivity than predicted by chance ( P ≤ .02). A very low aggregate probability ( P < 10 -18 , n = 20) suggests that causal node connectivity predicts seizure networks. In 8 of 16 with outcome data, causal connectivity in the resection was significantly greater than in the remaining contacts ( P ≤ .05). We found a significant association between favorable outcome and the presence of highly ranked nodes in the resection ( P < .05).

CONCLUSION

Granger analysis can identify seizure foci from interictal sEEG and correlates highly ranked nodes with favorable outcome, potentially informing surgical decision-making without reliance on ictal recordings.

Interictal SEEG Resting-State Connectivity Localizes the Seizure Onset Zone and Predicts Seizure Outcome

Localization of epileptogenic zone currently requires prolonged intracranial recordings to capture seizure, which may take days to weeks. The authors developed a novel method to identify the seizure onset zone (SOZ) and predict seizure outcome using short-time resting-state stereotacticelectroencephalography (SEEG) data. In a cohort of 27 drug-resistant epilepsy patients, the authors estimated the information flow via directional connectivity and inferred the excitation-inhibition ratio from the 1/f power slope. They hypothesized that the antagonism of information flow at multiple frequencies between SOZ and non-SOZ underlying the relatively stable epilepsy resting state could be related to the disrupted excitation-inhibition balance. They found flatter 1/f power slope in non-SOZ regions compared to the SOZ, with dominant information flow from non-SOZ to SOZ regions. Greater differences in resting-state information flow between SOZ and non-SOZ regions are associated with favorable seizure outcome. By integrating a balanced random forest model with resting-state connectivity, their method localized the SOZ with an accuracy of 88% and predicted the seizure outcome with an accuracy of 92% using clinically determined SOZ. Overall, this study suggests that brief resting-state SEEG data can significantly facilitate the identification of SOZ and may eventually predict seizure outcomes without requiring long-term ictal recordings.

The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is an essential stage of early detection and potential intervention for Alzheimer's disease (AD). Patients with aMCI exhibit partially abnormal functional brain connectivity and it is suggested that these features may represent a new diagnostic marker of early AD.

NEW METHOD

In this paper, we constructed two brain network models, a phase synchronization index (PSI) undirected network and a directed transfer function (DTF) directed network, to evaluate the cognitive function in patients with aMCI. We then built SVM classification models using the network clustering coefficient, global efficiency and average node degree as features to distinguish between aMCI patients and controls.

RESULTS

Our results reveal a classification accuracy and AUC of 66.6 ± 1.7% and 0.7475 and 80.0 ± 2.2% and 0.7825, respectively, for the two network models (PSI and DTF). As the directed network model performed better than the undirected model, we introduced an improved graph theory feature, efficiency density, which resulted in an increased classification accuracy and AUC value 86.6 ± 2.6% and 0.8295, respectively.

COMPARISON WITH EXISTING METHODS

The analysis of network models and the directionality of information flow is suitable for analysis of nonlinear EEG signals for assessment of the functional state of the brain. Compared with traditional network features, our proposed improved features more comprehensively evaluate transmission efficiency and density of the brain.

CONCLUSION

In this study, we demonstrate that an improved efficiency density feature is helpful for enhancing classification the accuracy of aMCI. Moreover, directed brain network models exhibit better classification for aMCI diagnosis than undirected networks.

Neurofeedback Training of the Control Network Improves Children's Performance with an SSVEP-based BCI

In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and in neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, there has been little research aiming to improve the performance of brain-computer interfaces (BCIs) using neuromodulation. The present study presents a novel design for a neurofeedback training (NFT) method to improve the operation of a steady-state visual evoked potential (SSVEP)-based BCI and further explores its underlying mechanisms. The use of NFT to upregulate alpha-band power in the user's parietal lobe is presented in this study as a new neuromodulation method to improve SSVEP-based BCI in this study. After users completed this NFT intervention, the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of the SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6%, respectively. However, no improvement was observed in the control group in which the subjects did not participate in NFT. Moreover, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was observed. Evidence from a network analysis and an attention test further indicates that NFT improves attention by developing the control capacity of the parietal lobe and then enhances the above SSVEP indicators. Upregulating the amplitude of parietal alpha oscillations using NFT significantly improves the SSVEP-based BCI performance by modulating the control network. The study validates an effective neuromodulation method and possibly contributes to explaining the function of the parietal lobe in the control network.

Information transmission in mPFC-BLA network during exploratory behavior in the open field

Exploratory behavior plays a fundamental role in motivation, learning, and well-being of organisms. The open field test (OFT) is a classic method to investigate the exploratory behavior in rodents, also a widely adopted and pharmacologically validated procedure for evaluating anxiety and depression. Several lines of evidence have shown that medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) play crucial roles in anxiety-like or depression-like exploratory behavior. However, the dynamic characterization of the mPFC-BLA network in exploratory behavior is less well understood. Therefore, this study aimed to investigate the information transmission mechanism in the mPFC-BLA network during exploratory behavior. Local field potentials (LFPs) from mPFC and BLA were simultaneously recorded while the rats performed the OFT. Directed transfer function (DTF), which was derived from Granger causal connectivity analysis, was applied to measure the functional connectivity among LFPs. Information flow (IF) was calculated to explore the dynamics of information transmission in the mPFC-BLA network. Our results revealed that, for both mPFC and BLA, the theta-band functional connectivity in periphery was significantly higher than that in center of the open field. The IF from BLA to mPFC in the open field task was significantly higher than that from mPFC to BLA. These results suggest that the functional connectivity and IF in the mPFC-BLA network are related to the exploratory behavior, and information transmission from BLA to mPFC could be predominant for exploratory behavior.

Multiscale multivariate transfer entropy and application to functional corticocortical coupling

Objective. Complex biological systems consist of multi-level mechanism in terms of within- and cross-subsystems correlations, and they are primarily manifested in terms of connectivity, multiscale properties, and nonlinearity. Existing studies have each only explored one aspect of the functional corticocortical coupling (FCCC), which has some limitations in portraying the complexity of multivariable systems. The present study investigated the direct interactions of brain networks at multiple time scales.Approach. We extended the multivariate transfer entropy (MuTE) method and proposed a novel method, named multiscale multivariate transfer entropy (MSMVTE), to explore the direct interactions of brain networks across multiple time scale. To verify this aim, we introduced three simulation models and compared them with multiscale transfer entropy (MSTE) and MuTE methods. We then applied MSMVTE method to analyze FCCC during a unilateral right-hand steady-state force task.Main results. Simulation results showed that the MSMVTE method, compared with MSTE and MuTE methods, better detected direct interactions and avoid the spurious effects of indirect relationships. Further analysis of experimental data showed that the connectivity from left premotor/sensorimotor cortex to right premotor/sensorimotor cortex was significantly higher than that of opposite directionality. Furthermore, the connectivities from central motor areas to both sides of premotor/sensorimotor areas were higher than those of opposite directionalities. Additionally, the maximum coupling strength was found to occur at a specific scale (3-10).Significance. Simulation results confirmed the effectiveness of the MSMVTE method to describe direct relationships and multiscale characteristics in complex systems. The enhancement of FCCC reflects the interaction of more extended activation in cortical motor regions. Additionally, the neurodynamic process of brain depends not only on emergent behavior at small scales, but also on the constraining effects of the activity at large scales. Taken together, our findings provide a basis for better understanding dynamics in brain networks.

Semi-automatic Extraction of Functional Dynamic Networks Describing Patient's Epileptic Seizures

Intracranial electroencephalography (EEG) studies using stereotactic EEG (SEEG) have shown that during seizures, epileptic activity spreads across several anatomical regions from the seizure onset zone toward remote brain areas. A full and objective characterization of this patient-specific time-varying network is crucial for optimal surgical treatment. Functional connectivity (FC) analysis of SEEG signals recorded during seizures enables to describe the statistical relations between all pairs of recorded signals. However, extracting meaningful information from those large datasets is time consuming and requires high expertise. In the present study, we first propose a novel method named Brain-wide Time-varying Network Decomposition (BTND) to characterize the dynamic epileptogenic networks activated during seizures in individual patients recorded with SEEG electrodes. The method provides a number of pathological FC subgraphs with their temporal course of activation. The method can be applied to several seizures of the patient to extract reproducible subgraphs. Second, we compare the activated subgraphs obtained by the BTND method with visual interpretation of SEEG signals recorded in 27 seizures from nine different patients. As a whole, we found that activated subgraphs corresponded to brain regions involved during the course of the seizures and their time course was highly consistent with classical visual interpretation. We believe that the proposed method can complement the visual analysis of SEEG signals recorded during seizures by highlighting and characterizing the most significant parts of epileptic networks with their activation dynamics.

Current Conceptual Understanding of the Epileptogenic Network From Stereoelectroencephalography-Based Connectivity Inferences

Localization of the epileptogenic zone (EZ) is crucial in the surgical treatment of focal epilepsy. Recently, EEG studies have revealed that the EZ exhibits abnormal connectivity, which has led investigators to now consider connectivity as a biomarker to localize the EZ. Further, abnormal connectivity of the EZ may provide an explanation for the impact of focal epilepsy on more widespread brain networks involved in typical cognition and development. Stereo-electroencephalography (sEEG) is a well-established method for localizing the EZ that has recently been applied to examine altered brain connectivity in epilepsy. In this manuscript, we review recent computational methods for identifying the EZ using sEEG connectivity. Findings from previous sEEG studies indicate that during interictal periods, the EZ is prone to seizure generation but concurrently receives inward connectivity preventing seizures. At seizure onset, this control is lost, allowing seizure activity to spread from the EZ. Regulatory areas within the EZ may be important for subsequently ending the seizure. After the seizure, the EZ appears to regain its influence on the network, which may be how it is able to regenerate epileptiform activity. However, more research is needed on the dynamic connectivity of the EZ in order to build a biomarker for EZ localization. Such a biomarker would allow for patients undergoing sEEG to have electrode implantation, localization of the EZ, and resection in a fraction of the time currently needed, preventing patients from having to endure long hospital stays and induced seizures.

Seizure Onset Zone Lateralization Using a Non-linear Analysis of Micro vs. Macro Electroencephalographic Recordings During Seizure-Free Stages of the Sleep-Wake Cycle From Epilepsy Patients

The application of non-linear signal analysis techniques to biomedical data is key to improve our knowledge about complex physiological and pathological processes. In particular, the use of non-linear techniques to study electroencephalographic (EEG) recordings can provide an advanced characterization of brain dynamics. In epilepsy these dynamics are altered at different spatial scales of neuronal organization. We therefore apply non-linear signal analysis to EEG recordings from epilepsy patients derived with intracranial hybrid electrodes, which are composed of classical macro contacts and micro wires. Thereby, these electrodes record EEG at two different spatial scales. Our aim is to test the degree to which the analysis of the EEG recorded at these different scales allows us to characterize the neuronal dynamics affected by epilepsy. For this purpose, we retrospectively analyzed long-term recordings performed during five nights in three patients during which no seizures took place. As a benchmark we used the accuracy with which this analysis allows determining the hemisphere that contains the seizure onset zone, which is the brain area where clinical seizures originate. We applied the surrogate-corrected non-linear predictability score (ψ), a non-linear signal analysis technique which was shown previously to be useful for the lateralization of the seizure onset zone from classical intracranial EEG macro contact recordings. Higher values of ψ were found predominantly for signals recorded from the hemisphere containing the seizure onset zone as compared to signals recorded from the opposite hemisphere. These differences were found not only for the EEG signals recorded with macro contacts, but also for those recorded with micro wires. In conclusion, the information obtained from the analysis of classical macro EEG contacts can be complemented by the one of micro wire EEG recordings. This combined approach may therefore help to further improve the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy patients.

The effect of fatigue on brain connectivity networks

In the fatigue state, the neural response characteristics of the brain might be different from those in the normal state. Brain functional connectivity analysis is an effective tool for distinguishing between different brain states. For example, comparative studies on the brain functional connectivity have the potential to reveal the functional differences in different mental states. The purpose of this study was to explore the relationship between human mental states and brain control abilities by analyzing the effect of fatigue on the brain response connectivity. In particular, the phase‐scrambling method was used to generate images with two noise levels, while the N‐back working memory task was used to induce the fatigue state in subjects. The paradigm of rapid serial visual presentation (RSVP) was used to present visual stimuli. The analysis of brain connections in the normal and fatigue states was conducted using the open‐source eConnectome toolbox. The results demonstrated that the control areas of neural responses were mainly distributed in the parietal region in both the normal and fatigue states. Compared to the normal state, the brain connectivity power in the parietal region was significantly weakened under the fatigue state, which indicates that the control ability of the brain is reduced in the fatigue state.

Desynchronization of temporal lobe theta-band activity during effective anterior thalamus deep brain stimulation in epilepsy

BACKGROUND

Bilateral cyclic high frequency deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) reduces the seizure count in a subset of patients with epilepsy. Detecting stimulation-induced alterations of pathological brain networks may help to unravel the underlying physiological mechanisms related to effective stimulation delivery and optimize target engagement.

METHODS

We acquired 64-channel electroencephalography during ten ANT-DBS cycles (145 ​Hz, 90 ​μs, 3-5 ​V) of 1-min ON followed by 5-min OFF stimulation to detect changes in cortical activity related to seizure reduction. The study included 14 subjects (three responders, four non-responders, and seven healthy controls). Mixed-model ANOVA tests were used to compare differences in cortical activity between subgroups both ON and OFF stimulation, while investigating frequency-specific effects for the seizure onset zones.

RESULTS

ANT-DBS had a widespread desynchronization effect on cortical theta and alpha band activity in responders, but not in non-responders. Time domain analysis showed that the stimulation induced reduction in theta-band activity was temporally linked to the stimulation period. Moreover, stimulation induced theta-band desynchronization in the temporal lobe channels correlated significantly with the therapeutic response. Responders to ANT-DBS and healthy-controls had an overall lower level of theta-band activity compared to non-responders.

CONCLUSION

This study demonstrated that temporal lobe channel theta-band desynchronization may be a predictive physiological hallmark of therapeutic response to ANT-DBS and may be used to improve the functional precision of this intervention by verifying implantation sites, calibrating stimulation contacts, and possibly identifying treatment responders prior to implantation.

Spatiotemporal dynamics of working memory under the influence of emotions based on EEG

OBJECTIVE

Previous studies have reported that working memory (WM) may be affected by emotions and that the effect may exist in different stages of WM. However, at present it remains controversial whether emotions inhibit or facilitate WM, and how the mechanism of dynamic information transmission in the brain during WM is affected by emotions.

APPROACH

In this study, we used a video database to induce three emotions (negative, neutral, and positive) and adopted a change detection paradigm based on electroencephalography. Event-related potential (ERP) analysis, event-related spectral perturbation analysis, source location analysis based on the dipole localization method and the distributed source localization method, and effective connectivity analysis were performed.

MAIN RESULTS

Both behavioral and ERP results suggest that positive emotions have no significant effect on WM capacity, while negative emotions could facilitate WM capacity. Furthermore, the effective connectivity results based on two source location methods suggest that the long-range connectivity between the frontal and posterior areas can reflect the influence of positive and negative emotions on the WM network, in which the connectivity under the positive emotion condition occurs in the earlier period of WM maintenance, while the connectivity under the negative emotion condition occurs in the later period of WM maintenance.

SIGNIFICANCE

The consistency of the behavioral, ERP, and effective connectivity results suggests that under the negative emotion condition, the top-down attention modulation between the frontoparietal area and posterior area could promote the most relevant information storage during WM maintenance.

Central sulcus is a barrier to causal propagation in epileptic networks

the functional interactions of different brain regions are dependent of their underlying structural connectivity. These interactions are important for normal brain activity as well as in epileptic disorders where spontaneous epileptic discharges are generated. To understand the effects of brain topography on both normal and epileptic functional networks, we evaluated propagation frequency with respect to geodesic distance and the specific role of sulcal patterns in modulating these functional connections. We implemented a multimodal approach combining Electrocorticography (ECoG) and magnetic resonance imaging (MRI) to evaluate the relationships between epileptic and non-epileptic networks as they are related to structural connectivity in the human brain. We analyzed interictal ECoG along with MRI 3D reconstructions from seven epileptic patients and found that interictal full-dataset network propagations travel to both nearby and distant cortical locations, however with longer distance the propagations occurrence attenuates dramatically. We also discovered that the central sulcus acts as a strong barrier allowing only 30% of the propagation to cross central sulcus whereas the rest of the 70% propagations are observed on the same side of the sulcus. Epileptic spike propagations, however, were more highly localized with significantly less distant spread and had further reduction in spread across sulci. This is a novel approach to explore the structural influence of brain topography on the functional network and would help understand the interaction of different brain regions and how these are altered in patients with epilepsy. This approach could assist physician decision making during epilepsy surgery by revealing an appropriate brain network and cortical interactions.

Multiple Oscillatory Push-Pull Antagonisms Constrain Seizure Propagation

OBJECTIVE

Drug-resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low-frequency activity (LFA), high-frequency activity (HFA), or low-to-high cross-frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear.

METHODS

We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within-frequency and cross-frequency directional connectivity were estimated by an adaptive directed transfer function and cross-frequency directionality, respectively.

RESULTS

In the within-frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross-frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions' HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions' HFA.

INTERPRETATION

Our results suggest that the secondary generalization of focal seizures is regulated by numerous within- and cross-frequency push-pull dynamics, potentially reflecting impaired excitation-inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683-694.

The road ahead in clinical network neuroscience

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

Neuroimaging in Pediatric Epilepsy

Pediatric epilepsy presents with various diagnostic challenges. Recent advances in neuroimaging play an important role in the diagnosis, management and in guiding the treatment of pediatric epilepsy. Structural neuroimaging techniques such as CT and MRI can identify underlying structural abnormalities associated with epileptic focus. Functional neuroimaging provides further information and may show abnormalities even in cases where MRI was normal, thus further helping in the localization of the epileptogenic foci and guiding the possible surgical management of intractable/refractory epilepsy when indicated. A multi-modal imaging approach helps in the diagnosis of refractory epilepsy. In this review, we will discuss various imaging techniques, as well as aspects of structural and functional neuroimaging and their application in the management of pediatric epilepsy.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

SIGNIFICANCE

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

Electrophysiological Brain Connectivity: Theory and Implementation

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

Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy

OBJECTIVE

Abnormal and dynamic epileptogenic networks cause difficulties for clinical epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ.

METHODS

We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical epileptologists and the results of epilepsy resection surgeries.

RESULTS

Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20-50 ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network.

CONCLUSION

The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.

Interictal spike connectivity in human epileptic neocortex

OBJECTIVE

Interictal spikes are a biomarker of epilepsy, yet their precise roles are poorly understood. Using long-term neocortical recordings from epileptic patients, we investigated the spatial-temporal propagation patterns of interictal spiking.

METHODS

Interictal spikes were detected in 10 epileptic patients. Short time direct directed transfer function was used to map the spatial-temporal patterns of interictal spike onset and propagation across different cortical topographies.

RESULTS

Each patient had unique interictal spike propagation pattern that was highly consistent across times, regardless of the frequency band. High spiking brain regions were often not spike onset regions. We observed frequent spike propagations to shorter distances and that the central sulcus forms a strong barrier to spike propagation. Spike onset and seizure onset seemed to be distinct networks in most cases.

CONCLUSIONS

Patients in epilepsy have distinct and unique network of causal propagation pattern which are very consistent revealing the underlying epileptic network. Although spike are epileptic biomarkers, spike origin and seizure onset seems to be distinct in most cases.

SIGNIFICANCE

Understanding patterns of interictal spike propagation could lead to the identification patient-specific epileptic networks amenable to surgical or other treatments.

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outcome after the epilepsy surgery. The utility of HFO in planning the surgery, though, is unclear. Reasons include the variability of the HFO across patients and brain regions and the influence of the sleep-wake cycle, which causes large fluctuations in the ratio between the HFO observed in SOZ and non-SOZ regions. To cope with these limitations, a rank-based solution is proposed to identify the SOZ by using the HFO in multichannel intracranial EEG. A time-varying index of the epileptic susceptibility of the different brain areas is derived from the HFO rate and a support vector machine is applied on this index to identify the SOZ. The solution is trained and tested on separate groups of patients to avoid the use of patient-specific information and provides optimal SOZ prediction using as little as 30 min of recordings per channel (window). Tested on 14 patients with various combinations of seizure type, epilepsy etiology, and SOZ arrangement (172.7 ± 90.1 h/channel per patient and 75.6 ± 23.5 channels/patient, mean ± S.D.), our solution identified the SOZ with 0.92 ± 0.03 accuracy and 0.91 ± 0.03 area under the ROC curve (mean ± S.D.) across patients. For each patient, the window onset time was varied over 72 continuous hours and the prediction of the SOZ remained insensitive to the onset time, thus showing potential for surgery planning.

Granger Causality Analysis of Interictal iEEG Predicts Seizure Focus and Ultimate Resection

BACKGROUND

A critical conceptual step in epilepsy surgery is to locate the causal region of seizures. In practice, the causal region may be inferred from the set of electrodes showing early ictal activity. There would be advantages in deriving information about causal regions from interictal data as well. We applied Granger's statistical approach to baseline interictal data to calculate causal interactions. We hypothesized that maps of the Granger causality network (or GC maps) from interictal data might inform about the seizure network, and set out to see if “causality” in the Granger sense correlated with surgical targets.

OBJECTIVE

To determine whether interictal baseline data could produce GC maps, and whether the regions of high GC would statistically resemble the topography of the ictally active electrode (IAE) set and resection.

METHODS

Twenty-minute interictal baselines obtained from 25 consecutive patients were analyzed. The “GC maps” were quantitatively compared to conventionally constructed surgical plans, by using rank order and Cartesian distance statistics.

RESULTS

In 16 of 25 cases, the interictal GC rankings of the electrodes in the IAE set were lower than predicted by chance (P < .05). The aggregate probability of such a match by chance alone is very small (P < 10⁻²⁰) suggesting that interictal GC maps correlated with ictal networks. The distance of the highest GC electrode to the IAE set and to the resection averaged 4 and 6 mm (Wilcoxon P < .001).

CONCLUSION

GC analysis has the potential to help localize ictal networks from interictal data.

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network

OBJECTIVE

Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for directional connection analysis in multivariate time series. However, most existing methods based on Granger causality assume fixed time lags in information transmission, while the propagation delay between brain signals is usually changing constantly.

METHODS

We propose a Granger causality estimator based on the recurrent neural network, called RNN-GC, to deal with the multivariate brain connectivity detection problem. Our model takes input of time-series signals with arbitrary length of transmission time lags and learns the information flow from the data using the gated RNN model, i.e., long short-term memory (LSTM). The LSTM model can sequentially update the gates in memory cells to determine how many preceding points should be considered for prediction. Therefore, the LSTM-based RNN-GC estimator works well on varying-length time lags and shows effectiveness even on very long transmission delays.

RESULTS

Experiments are carried out in comparison with other methods using both simulation data and epileptic electroencephalography signals. The RNN-GC estimator achieves superior performance in brain connectivity estimation and shows robustness in modeling multivariate connections with varying-length time lags.

CONCLUSION

The RNN-GC method is capable of modeling nonlinear and varying-length lagged information transmission and effective in directional brain connectivity estimation.

SIGNIFICANCE

The proposed method is promising to serve as a robust brain connection analysis tool in clinical applications.

Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.

Betweenness centrality of intracranial electroencephalography networks and surgical epilepsy outcome

OBJECTIVE

We sought to determine whether the presence or surgical removal of certain nodes in a connectivity network constructed from intracranial electroencephalography recordings determines postoperative seizure freedom in surgical epilepsy patients.

METHODS

We analyzed connectivity networks constructed from peri-ictal intracranial electroencephalography of surgical epilepsy patients before a tailored resection. Thirty-six patients and 123 seizures were analyzed. Their Engel class postsurgical seizure outcome was determined at least one year after surgery. Betweenness centrality, a measure of a node's importance as a hub in the network, was used to compare nodes.

RESULTS

The presence of larger quantities of high-betweenness nodes in interictal and postictal networks was associated with failure to achieve seizure freedom from the surgery (p < 0.001), as was resection of high-betweenness nodes in three successive frequency groups in mid-seizure networks (p < 0.001).

CONCLUSIONS

Betweenness centrality is a biomarker for postsurgical seizure outcomes. The presence of high-betweenness nodes in interictal and postictal networks can predict patient outcome independent of resection. Additionally, since their resection is associated with worse seizure outcomes, the mid-seizure network high-betweenness centrality nodes may represent hubs in self-regulatory networks that inhibit or help terminate seizures.

SIGNIFICANCE

This is the first study to identify network nodes that are possibly protective in epilepsy.

Increased neuronal synchrony prepares mesial temporal networks for seizures of neocortical origin

OBJECTIVE

To gain understanding of the neuronal mechanisms underlying regional seizure spread, the impact of regional synchrony between seizure focus and downstream networks on neuronal activity during the transition to seizure in those downstream networks was assessed.

METHODS

Seven patients undergoing diagnostic intracranial electroencephalographic studies for surgical resection of epileptogenic regions were implanted with subdural clinical electrodes into the cortex (site of seizure initiation) and mesial temporal lobe (MTL) structures (downstream) as well as microwires into MTL. Neural activity was recorded (24/7) in parallel with the clinical intracranial electroencephalogram recordings for the duration of the patient's diagnostic stay. Changes in (1) regional synchrony (ie, coherence) between the presumptive neocortical seizure focus and MTL, (2) local synchrony between MTL neurons and their local field potential, and (3) neuronal firing rates within MTL in the time leading up to seizure were examined to study the mechanisms underlying seizure spread.

RESULTS

In seizures of neocortical origin, an increase in regional synchrony preceded the spread of seizures into MTL (predominantly hippocampal). Within frequencies similar to those of regional synchrony, MTL networks showed an increase in unit-field coherence and a decrease in neuronal firing rate, specifically for inhibitory interneuron populations but not pyramidal cell populations.

SIGNIFICANCE

These results suggest a mechanism of spreading seizures whereby the seizure focus first synchronizes local field potentials in downstream networks to the seizure activity. This change in local field coherence modifies the activity of interneuron populations in these downstream networks, which leads to the attenuation of interneuronal firing rate, effectively shutting down local interneuron populations prior to the spread of seizure. Therefore, regional synchrony may influence the failure of downstream interneurons to prevent the spread of the seizures during generalization.

Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks

OBJECTIVE

The goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks.

METHODS

Systematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis.

RESULTS

Source imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly.

CONCLUSIONS

While iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes.

SIGNIFICANCE

The present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.

Personalized translational epilepsy research - Novel approaches and future perspectives: Part I: Clinical and network analysis approaches

Despite the availability of more than 15 new "antiepileptic drugs", the proportion of patients with pharmacoresistant epilepsy has remained constant at about 20-30%. Furthermore, no disease-modifying treatments shown to prevent the development of epilepsy following an initial precipitating brain injury or to reverse established epilepsy have been identified to date. This is likely in part due to the polyetiologic nature of epilepsy, which in turn requires personalized medicine approaches. Recent advances in imaging, pathology, genetics and epigenetics have led to new pathophysiological concepts and the identification of monogenic causes of epilepsy. In the context of these advances, the First International Symposium on Personalized Translational Epilepsy Research (1st ISymPTER) was held in Frankfurt on September 8, 2016, to discuss novel approaches and future perspectives for personalized translational research. These included new developments and ideas in a range of experimental and clinical areas such as deep phenotyping, quantitative brain imaging, EEG/MEG-based analysis of network dysfunction, tissue-based translational studies, innate immunity mechanisms, microRNA as treatment targets, functional characterization of genetic variants in human cell models and rodent organotypic slice cultures, personalized treatment approaches for monogenic epilepsies, blood-brain barrier dysfunction, therapeutic focal tissue modification, computational modeling for target and biomarker identification, and cost analysis in (monogenic) disease and its treatment. This report on the meeting proceedings is aimed at stimulating much needed investments of time and resources in personalized translational epilepsy research. Part I includes the clinical phenotyping and diagnostic methods, EEG network-analysis, biomarkers, and personalized treatment approaches. In Part II, experimental and translational approaches will be discussed (Bauer et al., 2017) [1].

Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging.

Identification of the epileptogenic zone of temporal lobe epilepsy from stereo-electroencephalography signals: A phase transfer entropy and graph theory approach

The aim of this research is to apply an approach based on phase transfer entropy (PTE) and graph theory to study the interactions between the stereo-electroencephalography (SEEG) activities recorded in multilobar origin, in order to evaluate their ability to detect the epileptogenic zone (EZ) of temporal lobe epilepsies (TLE). Forty-three patients were included in this retrospective study. Five to sixteen (median = 12) multilead electrodes were implanted per patient, and, for each patient, a sub-set of between 10 and 32 (median = 22) bipolar derivations was selected for analysis. The leads were classified into the onset leads (OLs), the early propagation leads (EPLs), and the rest of the leads (RLs). The results showed that a significantly different dynamic trend of the out/in ratio (more obvious in the gamma band) distinguishes the OLs from RLs in the 23 patients who were seizure-free not only during the ictal event (significant elevation), but also during the inter-,pre-, late-ictal periods, and especially in the post-ictal (sharp decline) state. However, in the 20 patients who were not-seizure-free, the differences between the OLs and RLs during the post-ictal period were not found in any frequency band. The dynamic trend was used to predict surgical outcome, and the results showed that the sensitivity was 91% and the specificity was 70%. In brief, this study indicates that our approach may add new and valuable information, providing efficient quantitative measures useful for localizing the EZ.

Multimodal investigation of epileptic networks: The case of insular cortex epilepsy

The insula is a deep cortical structure sharing extensive synaptic connections with a variety of brain regions, including several frontal, temporal, and parietal structures. The identification of the insular connectivity network is obviously valuable for understanding a number of cognitive processes, but also for understanding epilepsy since insular seizures involve a number of remote brain regions. Ultimately, knowledge of the structure and causal relationships within the epileptic networks associated with insular cortex epilepsy can offer deeper insights into this relatively neglected type of epilepsy enabling the refining of the clinical approach in managing patients affected by it. In the present chapter, we first review the multimodal noninvasive tests performed during the presurgical evaluation of epileptic patients with drug refractory focal epilepsy, with particular emphasis on their value for the detection of insular cortex epilepsy. Second, we review the emerging multimodal investigation techniques in the field of epilepsy, that aim to (1) enhance the detection of insular cortex epilepsy and (2) unveil the architecture and causal relationships within epileptic networks. We summarize the results of these approaches with emphasis on the specific case of insular cortex epilepsy.

Estimation of resting state effective connectivity in epilepsy using direct-directed transfer function

There has been an increasing demand among neuroscientists to understand the complex network of functionally connected neural assemblies in the human brain. For this purpose, computational EEG research is widely used by researchers due to its remarkable advantage in providing high temporal resolution, and ease of analysis across different frequency bands. Here we analyzed Electrocorticographic (ECoG) signals of electrodes placed on frontal-parietal neocortex brain region of 8 pediatric epileptic patients. In order to evaluate the directed causal relationship among different brain regions, we employed a Granger causality based multivariate connectivity estimator named direct Directed Transfer Function (dDTF) to identify signal propagations among the selected set of electrode in the frequency range 1-50Hz. A consistent network pattern emerged that was unique to each patient. The fidelity of such dDTF-derived connectivity patterns can support a clearer understanding of effective connectivity in epileptic networks.