Search

Virtual Reality (VR) is an evolving wearable technology across many domain applications, including health delivery. Yet, human physiological adoption of VR technology is limited by cybersickness (CS) - a debilitating sensation accompanied by a cluster of symptoms, including nausea, oculomotor issues and dizziness. A leading problem is the lack of automated objective tools to predict or detect CS in individuals, which can then be used for resistance training, timely warning systems or clinical intervention. This paper explores the spatiotemporal brain dynamics and heart rate variability involved in cybersickness, and uses this information to both predict and detect CS episodes. The present study applies deep learning of EEG in a spiking neural network (SNN) architecture with a fusion of sympathetic heart rate variability parameters to predict CS prior to using VR (77.5%) and detect it (75.0%), which is more accurate than using just EEG (75%, 70.3%) or ECG alone (74.2%, 72.6%). The study found that Cz (premotor and supplementary motor cortex) and O2 (primary visual cortex) are key hubs in functionally connected networks associated with both CS events and susceptibility to CS. Consequently, Cz and O2 are presented here as promising targets for therapeutic interventions to alleviate and/or prevent the cybersickness.

Background Studies have suggested that patients with inflammatory bowel disease (IBD) have an increased risk of rheumatoid arthritis (RA). However, the available data on this association are inconsistent. This meta-analysis aimed to determine the association between IBD and the risk of RA. Methods Observational studies investigating the RA risk among patients with IBD (Crohn disease (CD) and/or ulcerative colitis (UC)) were searched in PubMed, Embase, and Web of Science from the date of inception to December 2019. The methodological quality of the included studies was assessed using the Newcastle-Ottawa Scale. Relative risks (RRs) and corresponding 95% confidential intervals (CIs) were pooled with a random-effects model. Heterogeneity was evaluated using I2 statistics while publication bias was determined using Begg’s and Egger’s tests. Subgroup and sensitivity analyses were performed. Results A total of three cohort studies, three cross-sectional studies, and two case-control studies were included in the meta-analyses. Compared to the non-IBD control or general population, there was a significantly higher risk of RA among patients with IBD (RR = 2.59; 95% CI: 1.93–3.48). Moreover, both CD (RR = 3.14; 95% CI: 2.46–4.01) and UC (RR = 2.29; 95% CI: 1.76–2.97) were associated with a significantly increased risk of RA. However, heterogeneity was substantial across studies and the subgroup analyses failed to identify the potential source of heterogeneity. Conclusions Patients with IBD have a greater risk of developing RA. Rheumatologists should be consulted when patients with IBD present with undifferentiated joint complaints. However, more prospective cohort studies are needed to validate these results.