Multimodal neuroimaging with optically pumped magnetometers: A simultaneous MEG-EEG-fNIRS acquisition system

Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in...

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Main Authors: Duan, H. (Author), Gao, J.-H (Author), Gu, W. (Author), He, K. (Author), Li, C. (Author), Li, D. (Author), Li, T. (Author), Liu, J. (Author), Lyu, B. (Author), Ma, X. (Author), Na, S. (Author), Qin, J. (Author), Ru, X. (Author), Sheng, J. (Author), Wan, S. (Author), Xu, W. (Author), Yan, X. (Author), Yin, Y. (Author), Zheng, F. (Author)
Format: Article
Language:English
Published: Academic Press Inc. 2022
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Online Access:View Fulltext in Publisher
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Summary:Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces. © 2022
ISBN:10538119 (ISSN)
DOI:10.1016/j.neuroimage.2022.119420