Intracranial EEG Structure-Function Coupling and Seizure Outcomes After Epilepsy Surgery

Source Causal Connectivity Noninvasively Predicting Surgical Outcomes of Drug-Refractory Epilepsy

AIMS

Drug-refractory epilepsy (DRE) refers to the failure of controlling seizures with adequate trials of two tolerated and appropriately chosen anti-seizure medications (ASMs). For patients with DRE, surgical intervention becomes the most effective and viable treatment, but its success rate is unsatisfactory at only approximately 50%. Predicting surgical outcomes in advance can provide additional guidance to clinicians. Despite the high accuracy of invasive methods, they inevitably carry the risk of post-operative infection and complications. Herein, to noninvasively predict surgical outcomes of DRE, we propose the "source causal connectivity" framework.

METHODS

In this framework, sLORETA, an EEG source imaging technique, was first used to inversely reconstruct intracranial neuronal electrical activity. Then, full convergent cross mapping (FCCM), a robust causal measure was introduced to calculate the causal connectivity between remodeled neuronal signals within epileptogenic zones (EZs). After that, statistical tests were performed to find out if there was a significant difference between the successful and failed surgical groups. Finally, a model for surgical outcome prediction was developed by combining causal network features with machine learning.

RESULTS

A total of 39 seizures with 205 ictal EEG segments were included in this prospective study. Experimental results exhibit that source causal connectivity in α-frequency band (8~13 Hz) gains the most significant differences between the surgical success and failure groups, with a p-value of 5.00e-05 and Cohen's d effect size of 0.68. All machine learning models can achieve an average accuracy of higher than 85%. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest accuracy of 90.73%, with a PPV of 87.91%, an NPV of 92.98%, a sensitivity of 90.91%, a specificity of 90.60%, and an F1-score of 89.39%.

CONCLUSION

Our results demonstrate that the source causal network of EZ is a reliable biomarker for predicting DRE surgical outcomes. The findings promote noninvasive precision medicine for DRE.

Neddylation of protein, a new strategy of protein post-translational modification for targeted treatment of central nervous system diseases

Neddylation, a type of protein post-translational modification that links the ubiquitin-like protein NEDD8 to substrate proteins, can be involved in various significant cellular processes and generate multiple biological effects. Currently, the best-characterized substrates of neddylation are the Cullin protein family, which is the core subunit of the Cullin-RING E3 ubiquitin ligase complex and controls many important biological processes by promoting ubiquitination and subsequent degradation of various key regulatory proteins. The normal or abnormal process of protein neddylation in the central nervous system can lead to a series of occurrences of normal functions and the development of diseases, providing an attractive, reasonable, and effective targeted therapeutic strategy. Therefore, this study reviews the phenomenon of neddylation in the central nervous system and summarizes the corresponding substrates. Finally, we provide a detailed description of neddylation involved in CNS diseases and treatment methods that may be used to regulate neddylation for the treatment of related diseases.

Quantifying hubness to predict surgical outcomes in epilepsy: Assessing resection-hub alignment in interictal intracranial EEG networks

OBJECTIVE

Intracranial EEG can identify epilepsy-related networks in patients with focal epilepsy; however, the association between network organization and post-surgical seizure outcomes remains unclear. Hubness serves as a critical metric to assess network organization by identifying brain regions that are highly influential to other regions. In this study, we tested the hypothesis that favorable post-operative seizure outcomes are associated with the surgical removal of interictal network hubs, measured by the novel metric "Resection-Hub Alignment Degree (RHAD)."

METHODS

We analyzed Phase II interictal intracranial EEG from 69 patients with epilepsy who were seizure-free (n = 45) and non-seizure-free (n = 24) 1 year post-operatively. Connectivity matrices were constructed from intracranial EEG recordings using imaginary coherence in various frequency bands, and centrality metrics were applied to identify network hubs. The RHAD metric quantified the congruence between hubs and resected/ablated areas. We used a logistic regression model, incorporating other clinical factors, and evaluated the association of this alignment regarding post-surgical seizure outcomes.

RESULTS

There was a significant difference in RHAD in fast gamma (80-200 Hz) interictal network between patients with favorable and unfavorable surgical outcomes (p = .025). This finding remained similar across network definitions (i.e., channel-based or region-based network) and centrality measurements (Eigenvector, Closeness, and PageRank). The alignment between surgically removed areas and other commonly used clinical quantitative measures (seizure-onset zone, irritative zone, high-frequency oscillations zone) did not reveal significant differences in post-operative outcomes. This finding suggests that the hubness measurement may offer better predictive performance and finer-grained network analysis. In addition, the RHAD metric showed explanatory validity both alone (area under the curve [AUC] = .66) and in combination with surgical therapy type (resection vs ablation, AUC = .71).

SIGNIFICANCE

Our findings underscore the role of network hub surgical removal, measured through the RHAD metric of interictal intracranial EEG high gamma networks, in enhancing our understanding of seizure outcomes in epilepsy surgery.

Structure-function coupling in macroscale human brain networks

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

Causal Brain Network Predicts Surgical Outcomes in Patients With Drug-Resistant Epilepsy: A Retrospective Comparative Study

Network neuroscience, especially causal brain network, has facilitated drug-resistant epilepsy (DRE) studies, while surgical success rate in patients with DRE is still limited, varying from 30%  ∼  70 %. Predicting surgical outcomes can provide additional guidance to adjust treatment plans in time for poorly predicted curative effects. In this retrospective study, we aim to systematically explore biomarkers for surgical outcomes by causal brain network methods and multicenter datasets. Electrocorticogram (ECoG) recordings from 17 DRE patients with 58 seizures were included. Ictal ECoG within clinically annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgeries. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α -frequency band ( 8  ∼  13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen's d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with [Formula: see text]. Based on the brain network features, machine learning models were developed to predict the surgical outcomes. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest average accuracy of 84.48% by 5-fold cross-validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE surgical outcomes.

A spatial perturbation framework to validate implantation of the epileptogenic zone

Stereo-electroencephalography (SEEG) is the gold standard to delineate surgical targets in focal drug-resistant epilepsy. SEEG uses electrodes placed directly into the brain to identify the seizure-onset zone (SOZ). However, its major constraint is limited brain coverage, potentially leading to misidentification of the 'true' SOZ. Here, we propose a framework to assess adequate SEEG sampling by coupling epileptic biomarkers with their spatial distribution and measuring the system's response to a perturbation of this coupling. We demonstrate that the system's response is strongest in well-sampled patients when virtually removing the measured SOZ. We then introduce the spatial perturbation map, a tool that enables qualitative assessment of the implantation coverage. Probability modelling reveals a higher likelihood of well-implanted SOZs in seizure-free patients or non-seizure free patients with incomplete SOZ resections, compared to non-seizure-free patients with complete resections. This highlights the framework's value in sparing patients from unsuccessful surgeries resulting from poor SEEG coverage.

Detecting local perturbations of networks in a latent hyperbolic embedding space

This paper introduces two novel scores for detecting local perturbations in networks. For this, we consider a non-Euclidean representation of networks, namely, their embedding onto the Poincaré disk model of hyperbolic geometry. We numerically evaluate the performances of these scores for the detection and localization of perturbations on homogeneous and heterogeneous network models. To illustrate our approach, we study latent geometric representations of real brain networks to identify and quantify the impact of epilepsy surgery on brain regions. Results suggest that our approach can provide a powerful tool for representing and analyzing changes in brain networks following surgical intervention, marking the first application of geometric network embedding in epilepsy research.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices

OBJECTIVE

Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.

METHODS

We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.

RESULTS

We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations.

SIGNIFICANCE

iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Network coupling and surgical treatment response in temporal lobe epilepsy: A proof-of-concept study

OBJECTIVE

This proof-of-concept study aimed to examine the overlap between structural and functional activity (coupling) related to surgical response.

METHODS

We studied intracranial rest and ictal stereoelectroencephalography (sEEG) recordings from 77 seizures in thirteen participants with temporal lobe epilepsy (TLE) who subsequently underwent resective/laser ablation surgery. We used the stereotactic coordinates of electrodes to construct functional (sEEG electrodes) and structural connectomes (diffusion tensor imaging). A Jaccard index was used to assess the similarity (coupling) between structural and functional connectivity at rest and at various intraictal timepoints.

RESULTS

We observed that patients who did not become seizure free after surgery had higher connectome coupling recruitment than responders at rest and during early and mid seizure (and visa versa).

SIGNIFICANCE

Structural networks provide a backbone for functional activity in TLE. The association between lack of seizure control after surgery and the strength of synchrony between these networks suggests that surgical intervention aimed to disrupt these networks may be ineffective in those that display strong synchrony. Our results, combined with findings of other groups, suggest a potential mechanism that explains why certain patients benefit from epilepsy surgery and why others do not. This insight has the potential to guide surgical planning (e.g., removal of high coupling nodes) following future research.

Physiological and pathological neuronal connectivity in the living human brain based on intracranial EEG signals: the current state of research

Over the past decades, studies of human brain networks have received growing attention as the assessment and modelling of connectivity in the brain is a topic of high impact with potential application in the understanding of human brain organization under both physiological as well as various pathological conditions. Under specific diagnostic settings, human neuronal signal can be obtained from intracranial EEG (iEEG) recording in epilepsy patients that allows gaining insight into the functional organisation of living human brain. There are two approaches to assess brain connectivity in the iEEG-based signal: evaluation of spontaneous neuronal oscillations during ongoing physiological and pathological brain activity, and analysis of the electrophysiological cortico-cortical neuronal responses, evoked by single pulse electrical stimulation (SPES). Both methods have their own advantages and limitations. The paper outlines available methodological approaches and provides an overview of current findings in studies of physiological and pathological human brain networks, based on intracranial EEG recordings.