Authors :
Presenting Author: Jane Townsend, BS – Cook Childrens Health Care System
Sakar Rijal, M.S – Children's Health Care System
Ioannis Ntoumanis, PhD – Cook Childrens Health Care System
Samantha Laboy, M.S. – Cook Childrens Health Care System
M. Scott Perry, MD – Cook Children’s Physician Network
Christos Papadelis, PhD – Cook Children's Health Care System
Rationale:
Dravet syndrome (DS) is a rare pediatric epileptic encephalopathy, most often caused by de novo mutations in SCN1A that impair Nav1.1 sodium channels in cortical inhibitory interneurons. Although the exact pathophysiology of DS remains unclear, GABAergic dysfunction is strongly supported as a key disease mechanism. Disrupted GABAergic signaling and excitation–inhibition (E/I) imbalance also have been associated with abnormal cortical gamma-band activity, measurable with EEG or magnetoencephalography (MEG). In this study, we aim to develop a noninvasive biomarker by linking EEG/MEG-derived gamma oscillations with peripheral measures of GABA in the blood plasma. Specifically, we assess visually evoked and induced gamma-band activity alongside plasma metabolite profiles in children with DS and healthy controls. We hypothesize that GABAergic dysfunction in DS disrupts the E/I balance, reflected in altered gamma responses to visual stimuli and reduced peripheral plasma GABA levels.
Methods:
We recruited 20 children with DS (12 females; ages: 1–17; mean = 6.95) and 17 healthy controls (5 females; ages: 1–14; mean = 7.76). Participants first underwent venipuncture for plasma metabolite analysis, and simultaneous MEG and HD-EEG recordings during a 340-trial visual task (cartoons overlapping on checkerboards; Fig. 1A). Data were bandpass filtered (1–100 Hz) and averaged from −200 to 1500 ms (Fig. 1B). Cortical activity in the primary visual cortex (V1) was localized using a boundary element forward model and dynamic statistical parametric mapping. Visual evoked fields/potentials (VEFs/VEPs) were extracted by detecting envelope extrema exceeding 3 SDs above baseline. M100, M150, M250, N1, P1, and N2 components were compared between groups at the source level (left/right cuneus). Time–frequency (TF) maps were analyzed using cluster-based permutation tests (Fig. 1C). Group differences in plasma metabolites and TF features were assessed with Wilcoxon rank-sum tests (p < 0.05).