Can EEG predict outcomes in genetic generalized epilepsies?

EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis

Idiopathic generalized epilepsy (IGE) represents a common form of epilepsy in both adult and pediatric epilepsy units. Although IGE has been long considered a relatively benign epilepsy syndrome, a remarkable proportion of patients could be refractory to treatment. While some clinical prognostic factors have been largely validated among IGE patients, the impact of routine electroencephalography (EEG) findings in predicting drug resistance is still controversial and a growing number of authors highlighted the potential importance of capturing the sleep state in this setting. In addition, the development of advanced computational techniques to analyze EEG data has opened new opportunities in the identification of reliable and reproducible biomarkers of drug resistance in IGE patients. In this manuscript, we summarize the EEG findings associated with treatment resistance in IGE by reviewing the results of studies considering standard EEGs, 24-h EEG recordings, and resting-state protocols. We discuss the role of 24-h EEG recordings in assessing seizure recurrence in light of the potential prognostic relevance of generalized fast discharges occurring during sleep. In addition, we highlight new and promising biomarkers as identified by advanced EEG analysis, including hypothesis-driven functional connectivity measures of background activity and data-driven quantitative findings revealed by machine learning approaches. Finally, we thoroughly discuss the methodological limitations observed in existing studies and briefly outline future directions to identify reliable and replicable EEG biomarkers in IGE patients.

White Matter Abnormalities in Patients with Treatment-Resistant Genetic Generalized Epilepsies

Background

Genetic generalized epilepsies (GGEs) are associated with microstructural brain abnormalities that can be evaluated with diffusion tensor imaging (DTI). Available studies on GGEs have conflicting results. Our primary goal was to compare the white matter structure in a cohort of patients with video/EEG-confirmed GGEs to healthy controls (HCs). Our secondary goal was to assess the potential effect of age at GGE onset on the white matter structure.

Material/Methods

A convenience sample of 23 patients with well-characterized treatment-resistant GGEs (13 female) was compared to 23 HCs. All participants received MRI at 3T. DTI indices, including fractional anisotropy (FA) and mean diffusivity (MD), were compared between groups using Tract-Based Spatial Statistics (TBSS).

Results

After controlling for differences between groups, abnormalities in DTI parameters were observed in patients with GGEs, including decreases in functional anisotropy (FA) in the hemispheric (left>right) and brain stem white matter. The examination of the effect of age at GGE onset on the white matter integrity revealed a significant negative correlation in the left parietal white matter region FA (R=−0.504; p=0.017); similar trends were observed in the white matter underlying left motor cortex (R=−0.357; p=0.103) and left posterior limb of the internal capsule (R=−0.319; p=0.148).

Conclusions

Our study confirms the presence of widespread white matter abnormalities in patients with GGEs and provides evidence that the age at GGE onset may have an important effect on white matter integrity.

Electroencephalography as a tool for evidence-based diagnosis and improved outcomes in children with epilepsy in a resource-poor setting

Introduction

Electroencephalography (EEG) remains the most important investigative modality in the diagnostic evaluation of individuals with epilepsy. Children living with epilepsy in the developing world are faced with challenges of lack of access to appropriate diagnostic evaluation and a high risk of misdiagnosis and inappropriate therapy. We appraised EEG studies in a cohort of Nigerian children with epilepsy seen in a tertiary center in order to evaluate access to and the impact of EEG in the diagnostic evaluation of the cases.

Methods

Inter-ictal EEG was requested in all cases of pediatric epilepsy seen at the pediatric neurology clinic of the University College Hospital, Ibadan, Nigeria over a period of 18 months. Clinical diagnosis without EEG evaluation was compared with the final diagnosis post- EEG evaluation.

Results

A total of 329 EEGs were recorded in 329 children, aged 3months to 16 years, median 61.0 months. Clinical evaluation pre-EEG classified 69.3% of the epilepsies as generalized. The a posteriori EEG evaluations showed a considerably higher proportion of localization-related epilepsies (33.6%). The final evaluation post EEG showed a 21% reduction in the proportion of cases labeled as generalized epilepsy and a 55% increase in cases of localization-related epilepsy(p<0.001).

Conclusion

Here we show that there is a high risk of misdiagnosis and therefore the use of inappropriate therapies in children with epilepsy in the absence of EEG evaluation. The implications of our findings in the resource-poor country scenario are key for reducing the burden of care and cost of epilepsy treatment on both the caregivers and the already overloaded tertiary care services.