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The user base of the virtual reality (VR) medium is growing, and many of these users will experience cybersickness. Accounting for the vast inter-individual variability in cybersickness forms a pivotal step in solving the issue. Most studies of cybersickness focus on a single factor (e.g., balance, sex, vection), while other contributors are overlooked. Here, we characterize the complex relationship between cybersickness and several measures of sensorimotor processing. In a single session, we conducted a battery of tests of balance control, vection responses, and vestibular sensitivity to self-motion. Following this, we measured cybersickness after VR exposure. We constructed a principal components regression model using the measures of sensorimotor processing. The model significantly predicted 37% of the variability in cybersickness measures, with 16% of this variance being accounted for by a principal component that represented balance control measures. The strongest predictor was participants' sway path length during vection, which was inversely related to cybersickness (r(28) = -.53, p = .002) and uniquely accounted for 7.5% of the variance in cybersickness scores across participants. Vection strength reports and measures of vestibular sensitivity were not significant predictors of cybersickness. We discuss the possible role of sensory reweighting in cybersickness that is suggested by these results, and we identify other factors that may account for the remaining variance in cybersickness. The results reiterate that the relationship between balance control and cybersickness is anything but straightforward.

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The user base of the virtual reality (VR) medium is growing, and many of these users will experience cybersickness. Accounting for the vast inter-individual variability in cybersickness forms a pivotal step in solving the issue. Most studies of cybersickness focus on a single factor (e.g., balance, sex, vection), while other contributors are overlooked. Here, we characterize the complex relationship between cybersickness and several indices of sensorimotor processing. In a single session, we conducted a battery of tests of balance control, vection responses, and vestibular sensitivity to self-motion. A principal components regression model, primarily composed of balance control measures during vection, significantly predicted 37% of the variability in cybersickness measures. We observed strong, inverse associations between measures of sway and cybersickness. The results reiterate that the relationship between balance control and cybersickness is anything but straightforward. We discuss other factors that may account for the remaining variance in cybersickness.

Background and Objectives:Seizures are common during neonatal encephalopathy, but the contribution of seizure burden to outcomes remains controversial. This study aims to examine the relationship between electrographic seizure burden and neurological outcomes after neonatal encephalopathy.Methods:This prospective cohort study recruited newborns ≥36 weeks PMA around 6 hours of life between August 2014 to November 2019 from a Neonatal Intensive Care Unit. Participants underwent continuous electroencephalography for at least 48 hours, brain MRI within 3-5 days of life, and structured follow-up at 18 months. Electrographic seizures were identified by board-certified neurophysiologists, and quantified as total seizure burden and maximum hourly seizure burden. A medication exposure score was calculated based on all anti-seizure medications given during NICU admission. Brain MRI injury severity was classified based on basal ganglia and watershed scores. Developmental outcomes were measured using the Bayley Scales of Infant Development, 3rdEdition. Multivariable regression analyses were performed, adjusting for significant potential confounders.Results:Of 108 enrolled subjects, 98 subjects had cEEG and MRI data collected, of which 5 were lost to follow-up, and 6 died before age 18 months. All subjects with moderate-severe encephalopathy completed therapeutic hypothermia. cEEG-confirmed neonatal seizures occurred in 21(24%) newborns, with a total seizure burden mean of 12.5 ± 36.4 minutes, and a maximum hourly seizure burden mean of 4 ± 10 min/hr. After adjusting for MRI brain injury severity and medication exposure, total seizure burden was significantly associated with lower cognitive (-0.21, 95%CI -0.33 – -0.08, p=0.002) and language (-0.25, 95%CI -0.39 – -0.11, p=0.001) scores at 18 months. Total seizure burden of 60 minutes was associated with 15-point decline in language scores, and 70 minutes for cognitive scores. However, seizure burden was not significantly associated with epilepsy, neuromotor score, or cerebral palsy (p>0.1).Discussion:Higher seizure burden during neonatal encephalopathy was independently associated with worse cognitive and language scores at 18 months, even after adjusting for exposure to anti-seizure medications and severity of brain injury. These observations support the hypothesis that neonatal seizures occurring during neonatal encephalopathy independently contribute to long-term outcomes.