Feature fusion analysis approach based on synchronous EEG-fNIRS signals: application in etomidate use disorder individuals.

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
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引用次数: 0

Abstract

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.

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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.
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