Resting state EEG in young children with Tuberous Sclerosis Complex: associations with medications and seizures

Background: Tuberous Sclerosis Complex (TSC) is a rare genetic condition caused by mutation to TSC1 or TSC2 genes, with a population prevalence of 1/7000 births. TSC manifests behaviorally with features of autism, epilepsy, and intellectual disability. Resting state electroencephalography (EEG) offers a window into neural oscillatory activity and may serve as an intermediate biomarker between gene expression and behavioral manifestations. Such a biomarker could be useful in clinical trials as an endpoint or predictor of treatment response. However, seizures and antiepileptic medications also affect resting neural oscillatory activity and could undermine the utility of resting state EEG features as biomarkers in neurodevelopmental disorders such as TSC.

Methods: This paper compares resting state EEG features in a cross-sectional cohort of young children with TSC (n = 49, ages 12-37 months) to 49 age- and sex-matched typically developing controls. Within children with TSC, associations were examined between resting state EEG features, seizure severity composite score, and use of GABA agonists.

Results: Compared to matched typically developing children, children with TSC showed significantly greater beta power in permutation cluster analyses. Children with TSC also showed significantly greater aperiodic offset (reflecting nonoscillatory neuronal firing) after power spectra were parameterized using SpecParam into aperiodic and periodic components. Within children with TSC, both greater seizure severity and use of GABAergic antiepileptic medication were significantly and independently associated with increased periodic peak beta power.

Conclusions: The elevated peak beta power observed in children with TSC compared to matched typically developing controls may be driven by both seizures and GABA agonist use. It is recommended to collect seizure and medication data alongside EEG data for clinical trials. These results highlight the challenge of using resting state EEG features as biomarkers in trials with neurodevelopmental disabilities when epilepsy and anti-epileptic medication are common.