Background: Emerging technologies paves the way for the development of novel cognitive assessment approaches. Virtual reality (VR) has high ecological validity, increases participant engagement, and allows the assessment of functions that are challenging to evaluate with traditional methods. The present study aimed to validate a novel cognitive assessment tool and investigate the effects of demographical variables.

Method: A total of 575 healthy individuals aged between 18-66 years were included in the study. A subgroup of the participants (n = 326) attended the verbal and visual versions of the computerized N-back task. All participants underwent the modified version of NORA VRx™ - Core experience. Seven metrics were calculated from working memory, attention, and information processing tasks during the experience. The relative contributions of the age, education and gender variables on the VR-based metrics were assessed with multiple linear regression analysis. The concurrent validity was investigated by the correlation between VR-based scores and N-back scores using the Pearson Correlation.

Results: Age, gender, and education were significant predictors of the working memory scores, whereas only age and education were the significant predictors for the information processing scores. All the calculated cognitive scores worsened with the increasing age and lower education levels. Male had better scores in working memory and attention measures than female. The correct answer number at the verbal and visual N-back tasks showed moderate correlations with the VR-based scores. The working memory scores showed the strongest correlations with the 2- and 3-back conditions (p<0.001 for all).

Conclusion: The present study introduces a novel and valid cognitive assessment tool. The sample was stratified according to age and education and norm values were prepared accordingly. In the future studies, it is important to determine the characteristics of the population aged 50 years and older and to examine the discriminatory power of the novel tool among various patient groups.

Download full-text PDF

Source
http://dx.doi.org/10.1002/alz.093073DOI Listing

Publication Analysis

Top Keywords

working memory
16
novel cognitive
8
cognitive assessment
8
verbal visual
8
memory attention
8
age education
8
scores
8
vr-based scores
8
education predictors
8
memory scores
8

Similar Publications

Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.

View Article and Find Full Text PDF

Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.

View Article and Find Full Text PDF

Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.

Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.

View Article and Find Full Text PDF

Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.

View Article and Find Full Text PDF

Objective: Difficulty updating information in working memory has been proposed to underlie ruminative thinking in individuals with anorexia nervosa (AN). However, evidence regarding updating difficulties in AN remains inconclusive, particularly among adolescents. It has been proposed that exposure to negative emotion and disorder-salient stimuli may uniquely influence updating in AN.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!