Visual-Evoked Potential Morphology Differences across Rare Genetic Disorders with Heightened Risk for ASD

Poster Presentation
Saturday, May 12, 2018: 11:30 AM-1:30 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
L. Gabard-Durnam1, C. E. Mukerji2, K. J. Varcin3, J. LeBlanc4, L. M. Baczewski5, J. Keller6, S. Jeste7 and C. A. Nelson6, (1)Pediatrics, Harvard Medical School, Boston, MA, (2)Harvard University, Cambridge, MA, (3)Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia, (4)Harvard Medical School, Boston, MA, (5)Education, UCLA Center for Autism Research and Treatment, Los Angeles, CA, (6)Boston Children's Hospital, Boston, MA, (7)University of California, Los Angeles, Los Angeles, CA
Background: Many rare genetic disorders confer elevated risk for developing Autism Spectrum Disorder (ASD). These disorders provide unique opportunities to evaluate how specific mutations contribute to ASD phenotypes, including altered sensory information processing. However, few studies have directly compared how different genetic mutations disrupt cortical processing of sensory information. Visual-evoked potentials (VEPs), measured with electroencephalography (EEG), provide a noninvasive, translational index of basic sensory processing for comparing visual cortical function across ASD populations, and they could serve as a stratification biomarker across conditions.

Objectives: (1) We compared VEP component morphologies to identify distinct markers of disrupted visual processing across the following disorders: Rett Syndrome, Tuberous Sclerosis Complex (TSC), Phelan-McDermid Syndrome (PMS), FOXG1, CDKL5, and PTEN. (2) Given the challenging nature of processing brief, high-artifact EEG recordings collected in infants and young children, we developed a standardized, automated pipeline optimized for this class of EEG data to improve statistical power and reproducibility of results.

Methods: Data collection is ongoing. Using 128-channel HydroCel Geodesic Sensor nets, pattern-reversal VEPs have been elicited from 37 female Rett participants and 20 age-matched typically developing (TD) children, 17 TSC participants, and 18 age-matched TD infants, 7 PMS participants, 2 FOXG1 participants, 4 CDKL5 participants, and 2 PTEN participants. Analyses below focus on the Rett and TSC participants, although we will present data on all groups at INSAR. Data were manually-inspected for artifact, and subject to automated amplitude-based artifact detection. VEPs were extracted for the Oz electrode. We also introduce and compare processing with the Harvard Automated Processing Pipeline for EEG (HAPPE), freely-available software for fully-automated, standardized processing optimized for developmental EEG data.

Results: The TSC group had significantly more negative N1 amplitudes than the Rett group (p < 0.005), but was not significantly different from either TD group (p > 0.05). The Rett group also had significantly smaller P1 and N2 component amplitudes compared to the Rett age-matched TD group, the TSC group, and the TSC age-matched TD group (all p < 0.005). No latency effects distinguishing either disorder group from the other groups were observed. Post-hoc analysis revealed that the N1 amplitude difference between the Rett and TSC groups was driven by the 9 TSC infants who later received an ASD diagnosis (TSC+ASD), where the TSC+ASD subgroup had significantly more negative N1 components than the Rett group (p < 0.001). HAPPE processing of the EEG data outperformed multiple alternative processing strategies, rejecting more artifact while retaining 3 times more trials.

Conclusions: VEP component morphologies are differently affected across the rare genetic disorder groups relative to each other and age-matched TD groups. Preliminary analysis suggests that the youth with ASD differ from each other across the rare genetic disorders as well. Identifying VEP morphology differences that are common across or specific to the distinct mutations may provide translational biomarkers for measuring both the progress of the disorders and future interventions. Therefore, we also introduce HAPPE, an automated pipeline for optimally processing EEG data from developmental populations to facilitate comparisons between studies of these ASD-related disorders.