PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI
PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI
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DOI：
10.1016/j.neuroimage.2011.08.021
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发表时间：
2012-01-16
期刊：
影响因子：
5.7
通讯作者：
Strother, Stephen C.
中科院分区：
文献类型：
--
作者：
Churchill, Nathan W.;Yourganov, Grigori;Strother, Stephen C.
生理噪声的影响可能会显着限制BOLD功能磁共振成像的可重复性和准确性。然而，生理噪声证明了一个复杂的，欠采样的时间结构，往往是非正交相对于神经元链接的BOLD响应，这提出了一个重大的挑战，识别和消除这样的伪影。本文提出了一种多变量的，数据驱动的方法的表征和去除生理噪声的功能磁共振成像数据，称为PHYCAA（使用典型自相关分析的生理校正）。该方法识别高频，自相关的生理噪声源与可再生的空间结构，使用的典型相关分析的适应中进行的分裂半resolution框架。该技术能够识别与血管相连的空间结构的生理效应，以及任务和受试者依赖的内在维度。我们还表明，这种生理噪声的维数增加与外部测量的呼吸和心脏过程的可变性增加。使用PHYCAA作为去噪技术显著改善了具有生理噪声的模拟信号检测，以及针对块和事件相关任务设计的真实的数据驱动的模型预测和再现性。这与没有生理噪声校正以及与广泛使用的RETROICOR（格洛弗等人，2000）生理去噪算法，其使用外部测量的心脏和呼吸信号。(C)2011 Elsevier Inc. All rights reserved.
The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality that is task- and subject-dependent. We also demonstrate that increasing dimensionality of such physiological noise is correlated with increasing variability in externally-measured respiratory and cardiac processes. Using PHYCAA as a denoising technique significantly improves simulated signal detection with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally measured cardiac and respiration signals. (C) 2011 Elsevier Inc. All rights reserved.