{"title":"与轻度认知障碍相关的静息状态脑网络特征：一项初步的fNIRS概念验证研究。","authors":"Guohui Yang, Chenyu Fan, Haozheng Li, Yu Tong, Shuang Lin, Yashuo Feng, Fengzhi Liu, Chunrong Bao, Hongyu Xie, Yi Wu","doi":"10.31083/JIN26406","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>This study investigates the reliability of functional near-infrared spectroscopy (fNIRS) in detecting resting-state brain network characteristics in patients with mild cognitive impairment (MCI), focusing on static resting-state functional connectivity (sRSFC) and dynamic resting-state functional connectivity (dRSFC) patterns in MCI patients and healthy controls (HCs) without cognitive impairment.</p><p><strong>Methods: </strong>A total of 89 MCI patients and 83 HCs were characterized using neuropsychological scales. Subject sRSFC strength and dRSFC variability coefficients were evaluated via fNIRS. The study evaluated the feasibility of using fNIRS to measure these connectivity metrics and compared resting-state brain network characteristics between the two groups. Correlations with Montreal Cognitive Assessment (MoCA) scores were also explored.</p><p><strong>Results: </strong>sRSFC strength in homologous brain networks was significantly lower than in heterologous networks (<i>p</i> < 0.05). A significant negative correlation was also observed between sRSFC strength and dRSFC variability at both the group and individual levels (<i>p</i> < 0.001). While sRSFC strength did not differentiate between MCI patients and HCs, the dRSFC variability between the dorsal attention network (DAN) and default mode network (DMN), and between the ventral attention network (VAN) and visual network (VIS), emerged as sensitive biomarkers after false discovery rate correction (<i>p</i> < 0.05). No significant correlation was found between MoCA scores and connectivity measures.</p><p><strong>Conclusions: </strong>fNIRS can be used to study resting-state brain networks, with dRSFC variability being more sensitive than sRSFC strength for discriminating between MCI patients and HCs. The DAN-DMN and VAN-VIS regions were found to be particularly useful for the identification of dRSFC differences between the two groups.</p><p><strong>Clinical trial registration: </strong>ChiCTR2200057281, registered on 6 March, 2022; https://www.chictr.org.cn/showproj.html?proj=133808.</p>","PeriodicalId":16160,"journal":{"name":"Journal of integrative neuroscience","volume":"24 2","pages":"26406"},"PeriodicalIF":2.5000,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resting-State Brain Network Characteristics Related to Mild Cognitive Impairment: A Preliminary fNIRS Proof-of-Concept Study.\",\"authors\":\"Guohui Yang, Chenyu Fan, Haozheng Li, Yu Tong, Shuang Lin, Yashuo Feng, Fengzhi Liu, Chunrong Bao, Hongyu Xie, Yi Wu\",\"doi\":\"10.31083/JIN26406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>This study investigates the reliability of functional near-infrared spectroscopy (fNIRS) in detecting resting-state brain network characteristics in patients with mild cognitive impairment (MCI), focusing on static resting-state functional connectivity (sRSFC) and dynamic resting-state functional connectivity (dRSFC) patterns in MCI patients and healthy controls (HCs) without cognitive impairment.</p><p><strong>Methods: </strong>A total of 89 MCI patients and 83 HCs were characterized using neuropsychological scales. Subject sRSFC strength and dRSFC variability coefficients were evaluated via fNIRS. The study evaluated the feasibility of using fNIRS to measure these connectivity metrics and compared resting-state brain network characteristics between the two groups. Correlations with Montreal Cognitive Assessment (MoCA) scores were also explored.</p><p><strong>Results: </strong>sRSFC strength in homologous brain networks was significantly lower than in heterologous networks (<i>p</i> < 0.05). A significant negative correlation was also observed between sRSFC strength and dRSFC variability at both the group and individual levels (<i>p</i> < 0.001). While sRSFC strength did not differentiate between MCI patients and HCs, the dRSFC variability between the dorsal attention network (DAN) and default mode network (DMN), and between the ventral attention network (VAN) and visual network (VIS), emerged as sensitive biomarkers after false discovery rate correction (<i>p</i> < 0.05). No significant correlation was found between MoCA scores and connectivity measures.</p><p><strong>Conclusions: </strong>fNIRS can be used to study resting-state brain networks, with dRSFC variability being more sensitive than sRSFC strength for discriminating between MCI patients and HCs. The DAN-DMN and VAN-VIS regions were found to be particularly useful for the identification of dRSFC differences between the two groups.</p><p><strong>Clinical trial registration: </strong>ChiCTR2200057281, registered on 6 March, 2022; https://www.chictr.org.cn/showproj.html?proj=133808.</p>\",\"PeriodicalId\":16160,\"journal\":{\"name\":\"Journal of integrative neuroscience\",\"volume\":\"24 2\",\"pages\":\"26406\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of integrative neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.31083/JIN26406\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of integrative neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.31083/JIN26406","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Resting-State Brain Network Characteristics Related to Mild Cognitive Impairment: A Preliminary fNIRS Proof-of-Concept Study.
Background: This study investigates the reliability of functional near-infrared spectroscopy (fNIRS) in detecting resting-state brain network characteristics in patients with mild cognitive impairment (MCI), focusing on static resting-state functional connectivity (sRSFC) and dynamic resting-state functional connectivity (dRSFC) patterns in MCI patients and healthy controls (HCs) without cognitive impairment.
Methods: A total of 89 MCI patients and 83 HCs were characterized using neuropsychological scales. Subject sRSFC strength and dRSFC variability coefficients were evaluated via fNIRS. The study evaluated the feasibility of using fNIRS to measure these connectivity metrics and compared resting-state brain network characteristics between the two groups. Correlations with Montreal Cognitive Assessment (MoCA) scores were also explored.
Results: sRSFC strength in homologous brain networks was significantly lower than in heterologous networks (p < 0.05). A significant negative correlation was also observed between sRSFC strength and dRSFC variability at both the group and individual levels (p < 0.001). While sRSFC strength did not differentiate between MCI patients and HCs, the dRSFC variability between the dorsal attention network (DAN) and default mode network (DMN), and between the ventral attention network (VAN) and visual network (VIS), emerged as sensitive biomarkers after false discovery rate correction (p < 0.05). No significant correlation was found between MoCA scores and connectivity measures.
Conclusions: fNIRS can be used to study resting-state brain networks, with dRSFC variability being more sensitive than sRSFC strength for discriminating between MCI patients and HCs. The DAN-DMN and VAN-VIS regions were found to be particularly useful for the identification of dRSFC differences between the two groups.
Clinical trial registration: ChiCTR2200057281, registered on 6 March, 2022; https://www.chictr.org.cn/showproj.html?proj=133808.
期刊介绍：
JIN is an international peer-reviewed, open access journal. JIN publishes leading-edge research at the interface of theoretical and experimental neuroscience, focusing across hierarchical levels of brain organization to better understand how diverse functions are integrated. We encourage submissions from scientists of all specialties that relate to brain functioning.
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