Development of a novel virtual environment for assessing cognitive function. Design, Development and Evaluation of a Novel Virtual Environment to Investigate Cognitive Function and Discriminate between Mild Cognitive Impairment and Healthy Elderly.

摘要

Alzheimer's disease (AD) is neurodegenerative disorder that causesudmemory loss and cognitive dysfunction. It affects one in five peopleudover the age of 80 and is distressing for both sufferers and theirudfamilies. A transitional stage between normal ageing and dementiaudincluding AD is termed a mild cognitive impairment (MCI). Recentudstudies have shown that people with MCI may convert to AD over timeudalthough not all MCI cases progress to AD. Much research is nowudfocussing on early detection of AD and diagnosing an MCI that willudprogress to AD to allow prompt treatment and disease managementudbefore the neurons degenerate to a stage beyond repair. Hence, theudability to obtain a method of identifying MCI is of great importance.udVirtual reality plays an important role in healthcare and offersudopportunities for detection of MCI. There are various studies that haveudfocused on detection of early AD using virtual environments, althoughudresults remain limited. One significant drawback of these studies hasudbeen their limited capacity to incorporate levels of difficulty toudchallenge users' capability. Furthermore, at best, these studies haveudonly been able to discriminate between early AD and healthy elderlyudwith about 80% of overall accuracy.udAs a result, a novel virtual simulation called Virtual Reality forudEarly Detection of Alzheimer's Disease (VREAD) was developed.udVREAD is a quick, easy and friendly tool that aims to investigateudcognitive functioning in a group of healthy elderly participants andudthose with MCI. It focuses on the task of following a route, since Topographical Disorientation (TD) is common in AD. An investigation was set up with two cohorts: non-elderly and elderly participants. The findings with regard to the non-elderly are important as they represent a first step towards implementation with elderly people. The results with elderly participants indicate that this simulation based assessment could provide a method for the detection of MCI since significant correlations between the virtual simulation and existing neuropsychological tests were found. In addition, the results proved that VREAD is comparable with well-known neuropsychological tests, such as Cambridge Neuropsychological Automated Test Battery, Paired Associate Learning (CANTAB PAL) and Graded Naming Test (GNT). Furthermore, analysis through the use of machine learning techniques with regard to the prediction of MCI also obtained encouraging results. This novel simulation was able to predict with about 90% overall accuracy using weighting function proposed to discriminate between MCI and healthy elderly.