ePoster

Neural network modulation of the human visuomotor system during hypoxia and hyperoxia

Daniel Graham, Gary Smerdon, Stephen Hall
FENS Forum 2024(2024)
Messe Wien Exhibition & Congress Center, Vienna, Austria

Conference

FENS Forum 2024

Messe Wien Exhibition & Congress Center, Vienna, Austria

Resources

Authors & Affiliations

Daniel Graham, Gary Smerdon, Stephen Hall

Abstract

In humans, the brain is the most oxygen-sensitive organ, consuming approximately 20% of the total oxygen supply despite representing only 2-3% of the body's mass. However, our understanding of the effects of altered oxygen levels on human brain function is incomplete. Studies investigating the neurocognitive effects of both elevated and reduced oxygen concentration report divergent findings, with improved and impaired performance reported in both conditions. Here, we employ an EEG approach to investigate the relationship between neural network dynamics and simple perceptual effects during inhalation of elevated and reduced oxygen.A single blinded, placebo-controlled, randomised, crossover design was used to measure the neural and perceptual effects of different oxygen levels. Participants (n=30) completed an EEG study, consisting of three identical protocols, receiving air mixtures with three different oxygen concentrations (10%, 21% and 100%) through an orofacial mask. Each condition consisted of twelve identical blocks, with one pre and one post baseline (no mask) and ten masked blocks. Each block contained: rest, eyes open/close, pattern-reversal VEP, and a 30-60Hz critical flicker fusion (CFF) task.We demonstrate the temporal profile of alpha and beta oscillatory power change in the visual and motor cortices at rest and during tasks. We determine the relationship between the steady-state visual evoked potential (VEP), CFF and VEP response latencies and show the effects of hypoxic and hyperoxic conditions on these signatures.This study provides important insights into the impact of altered oxygen concentration on neural network characteristics.

Unique ID: fens-24/neural-network-modulation-human-visuomotor-0cf229d9