Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning

Virtual reality interventions and their effects on the cognition of individuals with Alzheimer's disease: A systematic review and meta-analysis

BACKGROUND

Dementia due to Alzheimer's disease (AD) is the most prevalent neurocognitive disorder in the world and impacts the individual's cognitive functions and functionality in the early stages of the condition. Virtual reality (VR) interventions can assist in non-pharmacological treatment in a more ecological way, positively impacting cognitive abilities. However, there are few studies on VR exclusively involving people with AD in randomized controlled trials.

OBJECTIVE

To evaluate the effects of VR intervention on the cognitive functions of people with AD.

METHODS

A systematically conducted search was carried out in MEDLINE, EMBASE, BVS, Web of Science, and Scopus. Eligible studies were randomized controlled trials comparing the efficacy of VR and traditional cognitive interventions in people with AD. Methodologic quality was assessed with the Cochrane risk of bias tool, and outcomes were calculated as risk ratios (for dichotomous outcomes) and mean differences (for continuous outcomes) with 95% confidence interval.

RESULTS

A total of three randomized controlled trials with 75 participants were included. An improvement in the performance of the VR group was observed in memory, especially when comparing the usual treatment [MD = 0.99; CI95%: 0.33; 1.66; I2 = 0%]. VR has little or no effect on participants' executive function [MD = 1.36; 95%CI: -1.12; 3.85; I2 = 0%] compared to the usual treatment.

CONCLUSIONS

Our study results cautiously suggest, despite the small number of participants, that VR intervention may be a suitable memory treatment for individuals diagnosed with AD.

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.