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

Abstract

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalized epilepsy in both ictal and interictal states. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Ninety-five 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 to 20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. In addition, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (P = 0.0007, hedges' g = 0.55) and low-alpha (6-9 Hz) (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 brain network ictogenicity (P = 0.00006, g = 0.56) between epilepsy 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 epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy 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 epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared with 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 idiopathic generalized epilepsy particularly for their use as biomarkers.

Keywords: EEG; biomarkers; epilepsy; heterogeneity; networks.

Graphical abstract

Figure 1

Summary of methods for EEG processing and analysis. BIOJUME = Biology of Juvenile Myoclonic Epilepsy; BNI = brain network ictogenicity; JME = juvenile myoclonic epilepsy; LEAP = Longitudinal European Autism Project

Figure 2

Results from analysis of EEG features between JME and controls. (A) Summary of the estimated marginal mean difference of EEG features in JME compared with controls, from multiple linear regression models. Models control for epoch length, age (for all measures) and EEG time (for log₁₀ alpha shift and PAF only). The central marker shows estimated marginal mean difference and error bars are 95% confidence intervals. Model result tables, including beta coefficients and exact P-values are presented in Supplementary material. *P < 0.05, **P < 0.01 and ***P < 0.001. (B) ROC curves for EEG features in JME and controls. Area under the ROC values are presented in the legend. Low-alpha PSD, Alpha shift & Delta PSD: JME N = 147, control N = 95; PAF: JME N = 146, control N = 93; mean strength and BNI: JME N = 146, control N = 95 BNI = brain network ictogenicity; JME = juvenile myoclonic epilepsy; LEAP = Longitudinal European Autism Project; PAF = peak alpha frequency; PSD = power spectral density; ROC AUC = area under the receiver operating curve

Figure 3

Factors influencing EEG features within and between cohorts. (A) Heatmap representing the influence of continuous covariates on each EEG feature in control and JME cohorts. Colour represents the Spearman’s rank correlation coefficient between the continuous covariates and EEG features (green = positive correlation, red = negative correlation). Age and EEG time results are stratified by JME/controls. Test statistics, exact P-values and N for each correlation is displayed in Supplementary Table 7. (B) Heatmap showing the standardized beta coefficients of clinical variables in multiple linear regression models of EEG features in JME cohort. All models control for age and epoch length. Unstandardized and standardized β coefficients and exact P-values for each association are displayed in Supplementary Table 8. *P < 0.05, **P < 0.01 BNI = brain network ictogenicity; JME = juvenile myoclonic epilepsy; PPR = photoparoxysmal response; PSD = power spectral density; Sz = seizures