基于同步EEG-fNIRS信号的特征融合分析方法在依托咪酯使用障碍个体中的应用。

IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Biomedical optics express Pub Date : 2025-01-03 eCollection Date: 2025-02-01 DOI:10.1364/BOE.542078
Tianxin Gao, Chao Chen, Guangyao Liang, Yuchen Ran, Qiuping Huang, Zhenjiang Liao, Bolin He, Tefu Liu, Xiaoying Tang, Hongxian Chen, Yingwei Fan
{"title":"基于同步EEG-fNIRS信号的特征融合分析方法在依托咪酯使用障碍个体中的应用。","authors":"Tianxin Gao, Chao Chen, Guangyao Liang, Yuchen Ran, Qiuping Huang, Zhenjiang Liao, Bolin He, Tefu Liu, Xiaoying Tang, Hongxian Chen, Yingwei Fan","doi":"10.1364/BOE.542078","DOIUrl":null,"url":null,"abstract":"<p><p>Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (<i>p</i> < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.</p>","PeriodicalId":8969,"journal":{"name":"Biomedical optics express","volume":"16 2","pages":"382-397"},"PeriodicalIF":2.9000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11828439/pdf/","citationCount":"0","resultStr":"{\"title\":\"Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals.\",\"authors\":\"Tianxin Gao, Chao Chen, Guangyao Liang, Yuchen Ran, Qiuping Huang, Zhenjiang Liao, Bolin He, Tefu Liu, Xiaoying Tang, Hongxian Chen, Yingwei Fan\",\"doi\":\"10.1364/BOE.542078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (<i>p</i> < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.</p>\",\"PeriodicalId\":8969,\"journal\":{\"name\":\"Biomedical optics express\",\"volume\":\"16 2\",\"pages\":\"382-397\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-01-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11828439/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical optics express\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1364/BOE.542078\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/2/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical optics express","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1364/BOE.542078","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

依托咪酯通常用于麻醉诱导,但长期使用会影响脑神经血管机制,可能导致使用障碍。然而,关于依托咪酯对脑功能影响的研究有限,准确、无创地提取和分析脑神经血管特征仍然是一个挑战。本研究提出了一种基于全脑同步脑电图(EEG)-功能近红外光谱(fNIRS)信号的特征融合方法,旨在解决联合分析特定部位神经和血流动力学信号和特征的困难,这对了解依托咪酯使用障碍个体的神经血管机制变化至关重要。为了解决优化神经血管耦合分析准确性的挑战,我们提出了一种多波段局部神经血管耦合(MBLNVC)方法。该方法通过对多模态脑信号的整合,提高了NVC分析的空间精度。然后,我们将不同的大脑特征映射到Yeo 7大脑网络上,并基于这些网络构建特征向量。这种多层特征融合方法解决了在大脑特定位置同时分析复杂神经和血管信号的问题。我们的方法揭示了感觉运动和背侧注意网络中显著的神经血管耦合增强
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals.

Etomidate is commonly used for induction of anesthesia, but prolonged use can affect brain neurovascular mechanisms, potentially leading to use disorders. However, limited research exists on the impact of etomidate on brain function, and accurately and noninvasively extracting and analyzing neurovascular brain features remains a challenge. This study introduces a novel feature fusion approach based on whole-brain synchronous Electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) signals aimed at addressing the difficulty of jointly analyzing neural and hemodynamic signals and features in specific locations, which is critical for understanding neurovascular mechanism changes in etomidate use disorder individuals. To address the challenge of optimizing the accuracy of neurovascular coupling analysis, we proposed a multi-band local neurovascular coupling (MBLNVC) method. This method enhances spatial precision in NVC analysis by integrating multi-modal brain signals. We then mapped the different brain features to the Yeo 7 brain networks and constructed feature vectors based on these networks. This multilayer feature fusion approach resolves the issue of analyzing complex neural and vascular signals together in specific brain locations. Our approach revealed significant neurovascular coupling enhancement in the sensorimotor and dorsal attention networks (p < 0.05, FDR corrected), corresponding with different frequency bands and brain networks from single-modal features. These features of the intersection of bands and networks showed high sensitivity to etomidate using machine learning classifiers compared to other features (accuracy: support vector machine (SVM) - 82.10%, random forest (RF) - 80.50%, extreme gradient boosting (XGBoost) - 78.40%). These results showed the potential of the proposed feature fusion analysis approach in exploring changes in brain mechanisms and provided new insights into the effects of etomidate on resting neurovascular brain mechanisms.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biomedical optics express
Biomedical optics express BIOCHEMICAL RESEARCH METHODS-OPTICS
CiteScore
6.80
自引率
11.80%
发文量
633
审稿时长
1 months
期刊介绍: The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including: Tissue optics and spectroscopy Novel microscopies Optical coherence tomography Diffuse and fluorescence tomography Photoacoustic and multimodal imaging Molecular imaging and therapies Nanophotonic biosensing Optical biophysics/photobiology Microfluidic optical devices Vision research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信