Characterizing systemic physiological effects on the blood oxygen level dependent signal of resting-state fMRI in time-frequency space using wavelets.
Characterizing systemic physiological effects on the blood oxygen level dependent signal of resting-state fMRI in time-frequency space using wavelets.
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用小波在时-频空间表征全身生理效应对静息fMRI血氧水平依赖信号的影响。
DOI：
10.1002/hbm.26533
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发表时间：
2023-12-15
影响因子：
4.8
通讯作者：
Fan, Audrey P.
中科院分区：
文献类型：
--
作者：
Lee, Quimby N.;Chen, Jingyuan E.;Wheeler, Gregory J.;Fan, Audrey P.
关键词：
众所周知，心率变异性(HRV)和每次呼吸量(RVT)等全身生理动力学可以解释静息功能磁共振成像(RsfMRI)的血氧水平依赖(BOLD)信号的显著差异。然而，这些心肺变化与BOLD信号之间的同步性可能是由于神经元(即引起心率和呼吸变化的自主神经活动)或血管(即心肺活动促进血流动力学变化从而导致BOLD信号)的影响，并且这些影响的贡献可能在空间上、时间上和频谱上不同。在这项研究中，我们在快速采样的rsfMRI数据中使用小波分析来表征这些脑体动力学，同时进行人类Connectome项目的脉搏血氧仪和呼吸监测。我们的静息状态网络(RSN)的时频分析显示，BOLD信号和心跳间期(HBI)/RVT动态在不同频率之间的一致性存在差异，每个网络具有独特的配置文件。与其他网络相比，躯体运动(SMN)、视觉(VN)和突显(VAN)网络与两种系统生理信号显示出最大的同步性；然而，无论是否直接参与自主神经，在所有RSN中都观察到了显著的一致性。我们的相位分析显示，对于RSN上的不同相位偏移，大胆的和系统的生理信号之间具有显著的一致性，时间百分比的频率分布不同，这表明信号的相位偏移和时间顺序随频率而变化。最后，我们对连贯性的时间变异性的分析为自主神经状态对脑-体交流的潜在影响提供了洞察。总体而言，新的小波分析能够在空间、时间和频谱维度上有效地表征心肺活动和BOLD信号之间的动态关系，以帮助我们了解自主神经状态并改进我们对BOLD信号的解释。我们使用小波分析来描述心肺活动和血氧水平依赖(BOLD)信号在空间、时间和光谱维度上的动态关系，以帮助我们了解自主神经状态并改进我们对BOLD信号的理解。在BOLD信号和系统生理动力学相一致的情况下，我们为不同的相位偏移确定了独特的频率分布。
Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting‐state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain–body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time–frequency analysis across resting‐state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain–body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal. We characterized the dynamic relationship between cardiorespiratory activity and the blood oxygen level dependent (BOLD) signal in spatial, temporal, and spectral dimensions using a wavelet analysis to inform our understanding of autonomic states and improve our interpretation of the BOLD signal. We identified unique frequency profiles for different phase offsets in instances of coherence between the BOLD signal and systemic physiological dynamics.
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影响因子：
5.8
作者：
Guyenet PG
通讯作者：
Guyenet PG
影响因子：
5.7
作者：
Chang C;Metzger CD;Glover GH;Duyn JH;Heinze HJ;Walter M
通讯作者：
Walter M
DOI：
10.1073/pnas.1608117113
发表时间：
2016-10-25
影响因子：
11.1
作者：
Lewis, Laura D.;Setsompop, Kawin;Polimeni, Jonathan R.
通讯作者：
Polimeni, Jonathan R.
影响因子：
14.5
作者：
Critchley, HD;Mathias, CJ;Dolan, RJ
通讯作者：
Dolan, RJ
DOI：
10.1073/pnas.0807010105
发表时间：
2008-10-14
影响因子：
11.1
作者：
He, Biyu J.;Snyder, Abraham Z.;Raichle, Marcus E.
通讯作者：
Raichle, Marcus E.