Just like a well-stocked supermarket helps you find what you need, a groundbreaking virtual reality (VR) shopping task called VStore is proving effective in assessing age-related cognitive decline (ARCD) in adults. ARCD, or age-related cognitive decline, is like trying to navigate a complicated maze with foggy glasses on. By using an electroencephalogram (EEG) marker called the alpha3/alpha2 power ratio – which is associated with Alzheimer’s disease – researchers discovered that VStore performed with high accuracy in distinguishing between young and aging healthy adults. In fact, VStore achieved a sensitivity of 94% and a specificity of 95%! In contrast, other cognitive tests like the Cogstate Pre-clinical Alzheimer’s Battery and the Cogstate Composite Score showed lower accuracy rates. What’s more, the study found a strong correlation between high alpha3/alpha2 power ratios and lower performance in both VStore and the Cogstate tests. While the study did not find elevated alpha3/alpha2 power ratios in healthy aging individuals compared to young individuals, the findings do support VStore’s utility as an assessment tool for ARCD. Curious about the groundbreaking research? Check out the full article!

BackgroundAspects of cognitive function decline with age. This phenomenon is referred to as age-related cognitive decline (ARCD). Improving the understanding of these changes that occur as part of the ageing process can serve to enhance the detection of the more incapacitating neurodegenerative disorders such as Alzheimer’s disease (AD). In this study, we employ novel methods to assess ARCD by exploring the utility of the alpha3/alpha2 electroencephalogram (EEG) power ratio – a marker of AD, and a novel virtual reality (VR) functional cognition task – VStore, in discriminating between young and ageing healthy adults.Materials and methodsTwenty young individuals aged 20–30, and 20 older adults aged 60–70 took part in the study. Participants underwent resting-state EEG and completed VStore and the Cogstate Computerised Cognitive Battery. The difference in alpha3/alpha2 power ratios between the age groups was tested using t-test. In addition, the discriminatory accuracy of VStore and Cogstate were compared using logistic regression and overlying receiver operating characteristic (ROC) curves. Youden’s J statistic was used to establish the optimal threshold for sensitivity and specificity and model performance was evaluated with the DeLong’s test. Finally, alpha3/alpha2 power ratios were correlated with VStote and Cogstate performance.ResultsThe difference in alpha3/alpha2 power ratios between age cohorts was not statistically significant. On the other hand, VStore discriminated between age groups with high sensitivity (94%) and specificity (95%) The Cogstate Pre-clinical Alzheimer’s Battery achieved a sensitivity of 89% and specificity of 60%, and Cogstate Composite Score achieved a sensitivity of 83% and specificity of 85%. The differences between the discriminatory accuracy of VStore and Cogstate models were statistically significant. Finally, high alpha3/alpha2 power ratios correlated strongly with VStore (r = 0.73), the Cogstate Pre-clinical Alzheimer’s Battery (r = -0.67), and Cogstate Composite Score (r = -0.76).ConclusionWhile we did not find evidence that the alpha3/alpha2 power ratio is elevated in healthy ageing individuals compared to young individuals, we demonstrated that VStore can classify age cohorts with high accuracy, supporting its utility in the assessment of ARCD. In addition, we found preliminary evidence that elevated alpha3/alpha2 power ratio may be linked to lower cognitive performance.

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