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Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment

痴呆 口语流利性测试 认知 听力学 认知功能衰退 认知障碍 功能近红外光谱 心理学 静息状态功能磁共振成像 生物标志物 医学 神经科学 神经心理学 疾病 内科学 化学 生物化学 前额叶皮质
作者
Zheng Wang,Chen Niu,Yong Duan,Hao Yang,Jun-Xian Mi,Haiping Hao,Guodong Chen,Qihao Guo
出处
期刊:Frontiers in Aging Neuroscience [Frontiers Media SA]
卷期号:16
标识
DOI:10.3389/fnagi.2024.1469620
摘要

Introduction Alzheimer’s disease (AD) is a common neurological disorder. Based on clinical characteristics, it can be categorized into normal cognition (NC), subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia (AD). Once the condition begins to progress, the process is usually irreversible. Therefore, early identification and intervention are crucial for patients. This study aims to explore the sensitivity of fNIRS in distinguishing between SCD and MCI. Methods An in-depth analysis of the Functional Connectivity (FC) and oxygenated hemoglobin (HbO) characteristics during resting state and different memory cognitive tasks is conducted on two patient groups to search for potential biomarkers. The 33 participants were divided into two groups: SCD and MCI. Results Functional connectivity strength during the resting state and hemodynamic changes during the execution of Verbal Fluency Tasks (VFT) and MemTrax tasks were measured using fNIRS. The results showed that compared to individuals with MCI, patients with SCD exhibited higher average FC levels between different channels in the frontal lobe during resting state, with two channels’ FC demonstrating significant ability to distinguish between SCD and MCI. During the VFT task, the overall average HbO concentration in the frontal lobe of SCD patients was higher than that of MCI patients from 5 experimental paradigm. Receiver operating characteristic analysis indicated that the accuracy of the above features in distinguishing SCD from MCI was 78.8%, 72.7%, 75.8%, and 66.7%, respectively. Discussion fNIRS could potentially serve as a non-invasive biomarker for the early detection of dementia.

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