Evaluating Properties of Electrophysiological Biomarkers in Duplications of Chromosome 15q11.2-13.1(Dup15q syndrome)

Background:

Dup15q syndrome is highly penetrant for autism, intellectual disability, hypotonia, and epilepsy. The 15q region harbors genes critical for brain development and synaptic function, particularly the UBE3A and GABA_Areceptor genes. In a recent study we found that individuals with Dup15q syndrome show an electrophysiological biomarker characterized by excessive beta oscillations (12-30Hz) (Frohlich, 2016). This pattern resembles EEG changes induced by allosteric modulation of GABA_ARs, and the frequency at which we see the highest peak in the beta band (peak beta frequency), is modulated by GABA_AR kinetics. Given the overexpression of GABA_AR genes and UBE3A, which modulates synaptic GABA release, beta power and peak frequency may reflect abnormal GABA neurotransmission in Dup15q syndrome. Therefore, we sought to examine properties of these biomarkers, namely relation to phenotype and stability across states, that might inform its use in future clinical trials of pharmacological agents that modulate GABA neurotransmission.

Objectives:

To determine whether beta power and peak beta frequency 1) correlate with cognition, 2) differ between children with and without epilepsy, and 3) differ between awake and sleep states.

Methods:

Spontaneous resting awake EEG were collected at UCLA and two Dup15q family meetings, from 46 individuals, ages 9months-16years. To analyze sleep, we accessed clinical overnight EEG from 8 individuals with Dup15q syndrome, ages 2-11 years. Participants under medications known to pharmacologically elicit beta oscillations were excluded. EEG data were processed offline using MATLAB-EEGLAB software. Data were filtered, artifact reduced using both manual and independent component analysis, and physiological artifacts removed. Data were average referenced, and spectral power and peak frequency computed. Clinical measures tested included verbal(VDQ) and nonverbal cognition(NVDQ) based on the Mullen Scales of Early Learning, and adaptive function based on the Vineland Adaptive Behavior Scales. Simple and multiple linear regression models were implemented to model the effects of clinical features.

Results:

Beta power did not correlate with cognition (VDQ: R²=0.0052, p=0.6756; NVDQ: R²=0.0002, p=0.9354) and did not significantly differ based on epilepsy status (R²=0.0003, p=0.9002). Peak beta frequency did not correlate with cognition (VDQ: R²=0.0489, p=0.1947; NVDQ: R²=0.0547, p=0.1695). Peak beta frequency was significantly lower in children with epilepsy compared to those without an epilepsy diagnosis at the time of EEG recording (R²=0.1055, p=0.0383). Analysis of sleep EEG demonstrated persistent beta oscillations in sleep. Additionally, we found evidence of remarkably abnormal sleep characterized by attenuated slow wave sleep, presence of alpha-delta and beta-delta patterns. Particularly in participants with epilepsy, frequent spikes, and reduced spindle density were found.

Conclusions:

Peak beta frequency stratifies children with Dup15q syndrome based on epilepsy status. Sleep EEG revealed presence of excessive beta oscillations. Persistent beta oscillations and other abnormal electrophysiological patterns may compromise healthy sleep physiology and therefore disrupt sleep-dependent cognition. While this needs further investigation, stability of the biomarker across brain states underscores our hypothesis that the Dup15q EEG signature reflects the underlying genetic variation. Such genetically informed brain-based biomarkers can inform and improve clinical trials by serving as measures of target engagement or as outcome measures that precede behavioral responses to pharmacological treatment.