Investigation of Potential fNIRS-based Biomarkers in Multi-Domain Virtual Reality Tasks for MCI Assessment

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative condition and is currently the fourth leading cause of death in advanced nations. The primary cause of AD is the deterioration of neurons in areas of the brain crucial for memory, typically presenting symptoms like loss of memory and a decline in cognitive abilities. Mild Cognitive Impairment (MCI) represents a transitional phase between normal cognitive health and AD. Recent studies have shown that within five years, 32% of individuals diagnosed with MCI experience a progression to Alzheimer's disease. Hence, the early detection and treatment of MCI are vital in decreasing the likelihood of developing AD. Traditionally, MCI assessment has relied on neuropsychological tests such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Nevertheless, these methods have limitations, including inducing participant anxiety and fatigue, cultural biases, and the need for skilled administrators. This has shifted towards more innovative assessment methods, particularly Virtual Reality (VR) technology. VR's engaging and multisensory environment offers the potential for more effective MCI assessment. Various VR tasks, such as the Virtual Supermarket Task (VST) and VR adaptations of the Morris Water Maze and Trail Making Test, have shown promise in delivering insightful performance metrics. However, existing research has primarily focused on VR task performance evaluation, often overlooking the corresponding brain activation patterns these tasks stimulate. Compared with the task performance, the stimulated brain patterns could more directly reflect the cognitive function changes resulting from MCI. Whether these VR tasks can induce distinguishable changes in functional near-infrared spectroscopy (fNIRS) data between MCI and healthy individuals and which fNIRS parameters could be useful for MCI assessment is still unknown. To address this research gap, we investigated human brain activity across MIC and healthy individuals in multi-domain VR tasks. First, we selected a VR drumming task which engages multiple cognitive domains, including motor skills, rhythm, and spatial-temporal orientation. Second, we extracted some potential MCI indicators, such as functional connectivity from fNIRS data to analyse brain activity across MIC and healthy individuals in the VR task. Lastly, we examined the statistically significant parameters and discussed the underlying brain activity patterns and their potential for MCI assessment. Our findings revealed that specific brain activity and functional connectivity parameters indicated significant differences between healthy and MCI groups, suggesting the potential value of these parameters as biomarkers for VR-based MCI assessment. This study introduced the potential fNIRS parameters for MCI assessment and discussed their implications and underlying reasons. In conclusion, our study lays a promising foundation for developing and refining VR-based MCI assessments. We anticipate our findings will lead to more effective VR task designs and promote widespread MCI screening in larger populations, ultimately aiding early detection and intervention in individuals at risk of dementia. Future research should address the identified limitations and explore further enhancements in MCI and related condition assessments.