Wang Zheng, Niu Chaojie, Duan Yong, Yang Hao, Mi Jinpeng, Liu Chao, Chen Guodong, Guo Qihao
Department of Gerontology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiangsu Provincial Key Laboratory of Advanced Robotics, and Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.
Front Aging Neurosci. 2024 Dec 24;16:1469620. doi: 10.3389/fnagi.2024.1469620. eCollection 2024.
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.
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.
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.
fNIRS could potentially serve as a non-invasive biomarker for the early detection of dementia.
阿尔茨海默病(AD)是一种常见的神经疾病。根据临床特征,它可分为正常认知(NC)、主观认知下降(SCD)、轻度认知障碍(MCI)和痴呆(AD)。一旦病情开始进展,这个过程通常是不可逆的。因此,早期识别和干预对患者至关重要。本研究旨在探讨功能近红外光谱(fNIRS)在区分SCD和MCI方面的敏感性。
对两组患者在静息状态和不同记忆认知任务期间的功能连接(FC)和氧合血红蛋白(HbO)特征进行深入分析,以寻找潜在的生物标志物。33名参与者被分为两组:SCD组和MCI组。
使用fNIRS测量静息状态下的功能连接强度以及执行语言流畅性任务(VFT)和MemTrax任务期间的血流动力学变化。结果显示,与MCI患者相比,SCD患者在静息状态下额叶不同通道之间的平均FC水平更高,有两个通道的FC显示出区分SCD和MCI的显著能力。在VFT任务期间,来自5个实验范式的SCD患者额叶的总体平均HbO浓度高于MCI患者。受试者工作特征分析表明,上述特征区分SCD和MCI的准确率分别为78.8%、72.7%、75.8%和66.7%。
fNIRS有可能作为一种非侵入性生物标志物用于痴呆的早期检测。