Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure

Quantitative EEG signatures in patients with and without epilepsy development after a first seizure

OBJECTIVE

Diagnosing epilepsy after a first unprovoked seizure in the absence of visible epileptogenic lesions and interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is challenging. Quantitative EEG analysis and functional connectivity (FC) have shown promise in identifying patterns across epilepsy syndromes. Hence, we retrospectively investigated whether there were differences in FC (imaginary part of coherency) and spectral band power in non-lesional, IED-free, unmedicated patients after a first unprovoked seizure in contrast to controls. Further, we investigated if there were differences between the patients who developed epilepsy and those who remained with a single seizure for at least 6 months after the first seizure.

METHODS

We used 240 s of resting-state EEG (19 channels) recordings of patients (n = 41) after a first unprovoked seizure and age and sex-matched healthy controls (n = 46). Twenty-one patients developed epilepsy (epilepsy group), while 20 had no further seizures during follow-up (single-seizure group). We computed source-reconstructed power and FC in five frequency bands (1 ± 29 Hz). Group differences were assessed using permutation analysis of linear models.

RESULTS

Patients who developed epilepsy showed increased theta power and FC, increased delta power, and decreased delta FC compared to healthy controls. The single-seizure group exhibited reduced beta-1 FC relative to the control group. In comparison with the single-seizure group, patients with epilepsy demonstrated elevated delta and theta power and decreased delta FC.

SIGNIFICANCE

Source-reconstructed data from routine EEGs identified distinct network patterns between non-lesional, IED-free, unmedicated patients who developed epilepsy and those who remained with a single seizure. Increased delta and theta power, along with decreased delta FC, could be a potential epilepsy biomarker. Further, decreases in beta-1 FC after a single seizure may point toward a protective mechanism for patients without further seizures.

PLAIN LANGUAGE SUMMARY

After a first seizure, some people develop epilepsy, while others do not. We looked at brain activity in people who had a seizure but showed no clear signs of epilepsy. By comparing those who later developed epilepsy to those who did not, we found that certain slow brain wave patterns (delta and theta) might indicate a higher risk of developing epilepsy. This could help doctors identify high-risk patients sooner.

The diagnostic value of sleep-deprived EEG in epilepsy: A meta-analysis

Sleep deprivation has been studied as a method to induce sleep before EEG testing to improve the diagnosis of epilepsy. However, the effectiveness of sleep deprivation in diagnosing epilepsy through EEG in humans showed conflicting findings in previous studies. This meta-analysis aimed to provide statistical evidence for the diagnostic value of sleep-deprived EEG in epilepsy. A systematic search of the Web of Science and PubMed databases identified 12 relevant studies from May 1997 to the present. These studies were included to examine the diagnostic value of sleep-deprived EEG in epilepsy and its associated clinical variables, such as patient age, duration of sleep deprivation, and EEG recording duration. The results of the random effects model did not show a significant overall diagnostic effect for sleep-deprived EEG in epilepsy, but revealed high heterogeneity among the studies. Notably, this heterogeneity was not accounted for by the clinical variables analyzed. Upon excluding outliers, a trend suggesting a modest diagnostic value of sleep-deprived EEG emerged. The high heterogeneity among studies indicates the need for a standardized protocol for sleep deprivation in future studies. Overall, while sleep deprivation may have a small positive effect on EEG-based epilepsy diagnosis, further research is needed to better understand its impact and optimize its use in clinical practice.

Increased coherence predicts medical refractoriness in patients with temporal lobe epilepsy on monotherapy

Among patients with epilepsy, 30-40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

Literature review and protocol for a prospective multicentre cohort study on multimodal prediction of seizure recurrence after unprovoked first seizure

INTRODUCTION

Epilepsy is a common neurological disorder characterised by recurrent seizures. Almost half of patients who have an unprovoked first seizure (UFS) have additional seizures and develop epilepsy. No current predictive models exist to determine who has a higher risk of recurrence to guide treatment. Emerging evidence suggests alterations in cognition, mood and brain connectivity exist in the population with UFS. Baseline evaluations of these factors following a UFS will enable the development of the first multimodal biomarker-based predictive model of seizure recurrence in adults with UFS.

METHODS AND ANALYSIS

200 patients and 75 matched healthy controls (aged 18-65) from the Kingston and Halifax First Seizure Clinics will undergo neuropsychological assessments, structural and functional MRI, and electroencephalography. Seizure recurrence will be assessed prospectively. Regular follow-ups will occur at 3, 6, 9 and 12 months to monitor recurrence. Comparisons will be made between patients with UFS and healthy control groups, as well as between patients with and without seizure recurrence at follow-up. A multimodal machine-learning model will be trained to predict seizure recurrence at 12 months.

ETHICS AND DISSEMINATION

This study was approved by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen's University (DMED-2681-22) and the Nova Scotia Research Ethics Board (1028519). It is supported by the Canadian Institutes of Health Research (PJT-183906). Findings will be presented at national and international conferences, published in peer-reviewed journals and presented to the public via patient support organisation newsletters and talks.

TRIAL REGISTRATION NUMBER

NCT05724719.

Direct Estimation of Equivalent Bioelectric Sources Based on Huygens' Principle

An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens' Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart's surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens' Principle, measurements obtained from the surfaceof a patient's thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens' sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions' expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes.