Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls

Abstract

AbstractAbnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalised epilepsy in both ictal and interictal states. Here, we characterise differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy (JME) and assess factors influencing the heterogeneity of these EEG features. We collected EEG data from 147 participants with JME through the Biology of Juvenile Myoclonic Epilepsy (BIOJUME) study. 95 control EEGs were acquired from two independent studies (Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project). We extracted frequency and functional network-based features from 10-20s epochs of resting-state EEG, including relative power spectral density (PSD), peak alpha frequency, network topology measures and Brain Network Ictogenicity (BNI): a computational measure of the propensity of networks to generate seizure dynamics. The influence of covariates such as age, sex, antiseizure medication, EEG time and epoch length were investigated for each EEG feature prior to testing for differences between JME and control EEGs using univariate, multivariable and receiver operating curve (ROC) analysis. Additionally, associations of clinical phenotypes (seizure type, seizure control) with EEG features were investigated in the JME cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in PSD in delta (2-5Hz) (p=0.0007, hedges’ g=0.55) and low-alpha (6-9Hz) (p=2.9×10-8, g=0.80) frequency bands, peak alpha frequency (p=0.000007, g=0.66), functional network mean degree (p=0.0006, g=0.48) and BNI (p=0.00006, g=0.56) between JME and controls. Since age (p=0.009) and epoch length (p=1.7×10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between JME and controls remained. ROC analysis showed low-alpha PSD was optimal at distinguishing JME from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in JME patients. To conclude, individuals with JME have increased power of neural oscillatory activity at low-alpha frequencies, along with increased BNI compared to controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in IGE particularly for their use as biomarkers.