Interictal high-frequency oscillations, spikes, and connectivity profiles: A fingerprint of epileptogenic brain pathologies

Interictal stereo-electroencephalography features below 45 Hz are sufficient for correct localization of the epileptogenic zone and postsurgical outcome prediction

OBJECTIVE

Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction.

METHODS

In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test.

RESULTS

The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution.

SIGNIFICANCE

Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

Inhibiting the IRAK4/NF-κB/NLRP3 signaling pathway can reduce pyroptosis in hippocampal neurons and seizure episodes in epilepsy

BACKGROUND

Interleukin-1 receptor-associated kinase 4 (IRAK4) plays an important role in immune modulation in various central nervous system disorders. However, IRAK4 has not been reported in epilepsy models in animal and clinical studies, nor has its involvement in regulating pyroptosis in epilepsy.

METHOD

First, we performed transcriptome sequencing, quantitative real-time polymerase chain reaction, and western blot analysis on the hippocampal tissues of refractory epilepsy patients to measure the mRNA and protein levels of IRAK4 and pyroptosis-related proteins. Second, we successfully established a pentylenetetrazol (PTZ)-induced seizure mouse model. We conducted behavioral tests, electroencephalography, virus injection, and molecular biology experiments to investigate the role of IRAK4 in seizure activity regulation.

RESULTS

IRAK4 is upregulated in the hippocampus of epilepsy patients and PTZ-induced seizure model mice. IRAK4 expression is observed in the hilar neurons of PTZ-induced mice. Knocking down IRAK4 in PTZ-induced mice downregulated pyroptosis-related protein expression and alleviated seizure activity. Overexpressing IRAK4 in naive mice upregulated pyroptosis-related protein expression and increased PTZ-induced abnormal neuronal discharges. IRAK4 and NF-κB were found to bind to each other in patient hippocampal tissue samples. Pyrrolidine dithiocarbamate reversed the pyroptosis-related protein expression increase caused by PTZ. PF-06650833 alleviated seizure activity and inhibited pyroptosis in PTZ-induced seizure mice.

CONCLUSION

IRAK4 plays a key role in the pathological process of epilepsy, and its potential mechanism may be related to pyroptosis mediated by the NF-κB/NLRP3 signaling pathway. PF-06650833 has potential as a therapeutic agent for alleviating epilepsy.

Spatial and temporal properties of intra-operatively recorded spikes and high frequency oscillations in focal cortical dysplasia

OBJECTIVE

Focal cortical dysplasias (FCD) are characterized by distinct interictal spike patterns and high frequency oscillations (HFOs; ripples: 80-250 Hz; fast ripples: 250-500 Hz) in the intra-operative electrocorticogram (ioECoG). We studied the temporal relation between intra-operative spikes and HFOs and their relation to resected tissue in people with FCD with a favorable outcome.

METHODS

We included patients who underwent ioECoG-tailored epilepsy surgery with pathology confirmed FCD and long-term Engel 1A outcome. Spikes and HFOs were automatically detected and visually checked in 1-minute pre-resection-ioECoG. Channels covering resected and non-resected tissue were compared using a logistic mixed model, assessing event numbers, co-occurrence ratios, and time-based properties.

RESULTS

We found pre-resection spikes, ripples in respectively 21 and 20 out of 22 patients. Channels covering resected tissue showed high numbers of spikes and HFOs, and high ratios of co-occurring events. Spikes, especially with ripples, have a relatively sharp rising flank with a long descending flank and early ripple onset over resected tissue.

CONCLUSIONS

A combined analysis of event numbers, ratios, and temporal relationships between spikes and HFOs may aid identifying epileptic tissue in epilepsy surgery.

SIGNIFICANCE

This study shows a promising method for clinically relevant properties of events, closely associated with FCD.

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.

Learn how to interpret and use intracranial EEG findings

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.