Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients.

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. 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. 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. P-values were corrected for multiple comparisons. 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. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. 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. Ninety-five control EEGs were acquired from two independent studies. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. Here, we characterize differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. 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.

Thebrain version 9 beata features