Computational Characteristics of the SYNGAP1-RD EEG
Abstract number :
2.129
Submission category :
3. Neurophysiology / 3C. Other Clinical EEG
Year :
2024
Submission ID :
860
Source :
www.aesnet.org
Presentation date :
12/8/2024 12:00:00 AM
Published date :
Authors :
Laycee Cordell, BS – University of Alabama at Birmingham
Presenting Author: Noshin Tasnia, PhD – University of Alabama at Birmingham
Siddharth Gupta, MD – Kennedy Krieger Institute
Aida Doucoure, BS – University of Virginia
Constance Smith-Hicks, MD, PhD – Center for Synaptic Disorders, Rett and Related Disorders Clinic, Kennedy Krieger Institute
Rachel Smith, PhD – University of Alabama at Birmingham
Rationale: The neurobiological basis and genetic etiologies of neurodevelopmental disorders such as intellectual disability (ID), autism spectrum disorder (ASD) and epilepsy often overlap at both the structural and functional levels. SYNGAP1-Related Disability (SYNGAP1-RD) is an autosomal dominant neurodevelopmental disorder resulting from pathogenic variants in SYNGAP1. Electroencephalography (EEG) is often used to characterize brain network dysfunction in neurodevelopmental disorders like SYNGAP1-RD. We aimed to identify features that distinguish the SYNGAP1-RD EEG from neurotypical control subjects, highlighting potential disease relevant biomarkers.
Methods: We have performed preliminary analysis of the power spectrum in nine SYNGAP1-RD patients and eight neurotypical control resting-state EEGs. The data were first cleaned of artifacts as identified by a board-certified epileptologist. Remaining data were linked-ear re-referenced and broadband bandpass filtered from 1-50 Hz using a least squares FIR filter. Using Morlet wavelet decomposition, the average power in a four-minute clip of EEG was calculated across linearly-spaced frequencies from 1-40 Hz in each channel. The spectra for the SYNGAP1-RD children were compared to the average control spectra via a decibel conversion. Significance was defined as a change from control greater than 2 dB.
Results: Generally across all channels except for Oz and F9, we found significantly stronger power in the delta (1-4 Hz) frequency band in 6/9 SYNGAP1-RD subjects. In the low gamma band (as defined by 30-40 Hz here), we found significantly stronger power in 3/9 subjects and somewhat elevated power in a total of 7/9 subjects. Interestingly, all SYNGAP1-RD subjects displayed a suppression in the high alpha to low beta frequency range (approximately 10-18 Hz) in comparison to control subjects.
Conclusions: There is variability in the SYNGAP1-RD EEG power spectrum, possibly indicating the existence of phenotype-specific biomarkers. However, consistent differences in the SYNGAP1-RD EEG in the delta, high alpha to low beta, and low gamma range indicate that robust EEG differences exist between the SYNGAP1-RD EEG and control EEG. These biomarkers may be useful in quantitatively assessing how the brain responds to therapy for SINGAP1-RD.
Funding: This project was funded by the UAB School of Engineering.
Neurophysiology