Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Department of Neurology, College of Medicine, Hanyang University, Seoul 04763, Republic of Korea.
Sensors (Basel). 2024 May 30;24(11):3543. doi: 10.3390/s24113543.
Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.
遗忘型轻度认知障碍（aMCI）是正常衰老和阿尔茨海默病之间的过渡阶段，因此早期筛查对于潜在的干预和预防向阿尔茨海默病（AD）的进展至关重要。因此，需要研究来确定有效的、易于使用的 aMCI 筛查工具。虽然虚拟现实环境中的行为测试已经成功捕捉到了与工具性日常生活活动相关的行为特征，但仍需要进一步研究来建立认知能力下降和神经变化之间的联系。本研究利用稳态视觉诱发电位脑电图，探讨了在虚拟现实测试中记录的行为特征与通过测量背流中的神经活动获得的神经特征之间的相关性。结果，这种多模态方法实现了令人印象深刻的 98.38%的筛查准确率。
