Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment
IntroductionAlzheimer’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 u...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2024.1469620/full |
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| author | Zheng Wang Chaojie Niu Yong Duan Hao Yang Jinpeng Mi Chao Liu Guodong Chen Qihao Guo |
| author_facet | Zheng Wang Chaojie Niu Yong Duan Hao Yang Jinpeng Mi Chao Liu Guodong Chen Qihao Guo |
| author_sort | Zheng Wang |
| collection | DOAJ |
| description | IntroductionAlzheimer’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.MethodsAn 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.ResultsFunctional 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.DiscussionfNIRS could potentially serve as a non-invasive biomarker for the early detection of dementia. |
| format | Article |
| id | doaj-art-0bfa3a582f2c4dfdbe28408569464bc2 |
| institution | OA Journals |
| issn | 1663-4365 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-0bfa3a582f2c4dfdbe28408569464bc22025-08-20T02:31:58ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652024-12-011610.3389/fnagi.2024.14696201469620Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairmentZheng Wang0Chaojie Niu1Yong Duan2Hao Yang3Jinpeng Mi4Chao Liu5Guodong Chen6Qihao Guo7Department of Gerontology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaJiangsu Provincial Key Laboratory of Advanced Robotics, and Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, ChinaJiangsu Provincial Key Laboratory of Advanced Robotics, and Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, ChinaInstitute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, ChinaJiangsu Provincial Key Laboratory of Advanced Robotics, and Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, ChinaJiangsu Provincial Key Laboratory of Advanced Robotics, and Robotics and Microsystems Center, School of Mechanical and Electrical Engineering, Soochow University, Suzhou, ChinaDepartment of Gerontology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaIntroductionAlzheimer’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.MethodsAn 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.ResultsFunctional 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.DiscussionfNIRS could potentially serve as a non-invasive biomarker for the early detection of dementia.https://www.frontiersin.org/articles/10.3389/fnagi.2024.1469620/fullfunctional near-infrared spectroscopyMCISCDAlzheimer’s diseaseearly diagnosis |
| spellingShingle | Zheng Wang Chaojie Niu Yong Duan Hao Yang Jinpeng Mi Chao Liu Guodong Chen Qihao Guo Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment Frontiers in Aging Neuroscience functional near-infrared spectroscopy MCI SCD Alzheimer’s disease early diagnosis |
| title | Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment |
| title_full | Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment |
| title_fullStr | Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment |
| title_full_unstemmed | Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment |
| title_short | Research on the application of functional near-infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment |
| title_sort | research on the application of functional near infrared spectroscopy in differentiating subjective cognitive decline and mild cognitive impairment |
| topic | functional near-infrared spectroscopy MCI SCD Alzheimer’s disease early diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2024.1469620/full |
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