Evaluating the EEG Power Spectrum across Three Neurogenetic Disorders of the mTOR Pathway

Background: Neural networks act as a bridge between genotype and phenotype, thus offering clues about how basic biology translates to behaviorally-defined disorders such as autism spectrum disorder (ASD). Electroencephalography (EEG) serves as an index of the synchronous activity of large populations of neurons, and is therefore an ideal tool for measuring the activity of neural networks. Several genetic disorders of the mammalian target of rapamycin (mTOR) pathway confer significantly increased risk of ASD, despite differences in their exact function on the mTOR pathway: Examples include tuberous sclerosis complex (TSC), Phelan-McDermid Syndrome (PMS), and PTEN Hamartoma Tumor Syndrome (PHTS).

Objectives: We evaluate the EEG power spectrum among children with each of these genetic disorders, compared to typically developing controls.

Methods: As part of the ongoing multisite Developmental Synaptopathies Consortium, EEG data are being acquired at baseline (i.e., without a time-locked task) across 6 sites on children and adolescents with TSC (n=11), PMS (n=32), PHTS (n=8), and age-matched typically developing controls (n=23). We used the Batch EEG Automated Processing Platform (BEAPP) to standardized EEG processing across sites. Within BEAPP, preprocessing occurred via the Harvard Automated Preprocessing Pipeline (HAPPE), which was developed specifically for preprocessing of EEG in children with neurodevelopmental disorders. To standardize data length for each participant, 180 seconds of useable data (after processing) were randomly selected for further analysis. Because limited data were previously available on the EEG power spectrum in these disorders, we began by calculating mean power across all electrodes in the 10-20 electrode system. Normalized spectral power was calculated in each frequency band, and we used a Mann-Whitney U test (threshold p<.05), corrected for multiple comparisons, to compare power in each group to typically developing controls.

Results: Children with TSC show reduced gamma power, children with PMS show reduced alpha power, and children with PHTS show no significant differences in power compared to typically developing controls (Figure 1). There were no significant differences in EEG power across sites in any frequency band.

Conclusions: Although all of these mTOR pathway disorders increase the risk for ASD, each disorder shows a different pattern of EEG spectral power as compared to controls. This may help to explain the heterogeneity of EEG power findings among children with ASD who are not stratified by genotype. Further studies probing the mechanisms underlying these spectral power differences may offer helpful insights into the biology underlying ASD.