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鉴定与心率和呼吸相关的静息态 fMRI 波动的生理反应功能,以进行校正。

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration.

机构信息

Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.

Department of Bioengineering, McGill University, Montreal, QC, Canada.

出版信息

Neuroimage. 2019 Nov 15;202:116150. doi: 10.1016/j.neuroimage.2019.116150. Epub 2019 Sep 2.

Abstract

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.

摘要

功能磁共振成像(fMRI)因其具有较高的空间分辨率和非侵入性,被广泛认为是研究大脑功能的金标准。然而,众所周知,呼吸模式和心率的变化强烈影响血氧水平依赖(BOLD)fMRI 信号,这反过来又会对 fMRI 研究,特别是静息态研究产生相当大的影响。生理过程的动态效应通常通过使用卷积模型和同时记录的生理数据进行量化。在这种情况下,生理响应函数(PRF)曲线(心脏和呼吸响应函数)与相应的生理波动进行卷积,通常被采用。虽然经常有人认为 PRF 曲线可能是区域或个体特异性的,但这是否属实仍然是一个悬而未决的问题。在本研究中,我们提出了一种用于稳健估计 PRF 曲线的新框架,并使用该框架严格检查使用群体、个体、会话和扫描特异性 PRF 曲线的影响。该框架在静息态 fMRI 和人类连接组计划的生理数据上进行了测试。我们的结果表明,PRF 曲线在个体之间存在显著差异,在同一个体的会话之间也存在差异。这些差异部分归因于扫描过程中心率的平均值和方差等生理变量。所提出的方法框架可用于从持续时间超过 5 分钟的数据记录中获取稳健的扫描特异性 PRF 曲线,与之前定义的典型心脏和呼吸响应函数相比,其性能显著提高。除了从 BOLD 信号中去除生理混杂因素外,准确建模个体(或会话/扫描)特异性 PRF 曲线对于涉及血管反应改变的人群的研究(如老年个体)也很重要。

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