Physiological noise in brainstem FMRI.
Physiological noise in brainstem FMRI.
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DOI：
10.3389/fnhum.2013.00623
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发表时间：
2013
影响因子：
2.9
通讯作者：
Jenkinson M
中科院分区：
文献类型：
--
作者：
Brooks JC;Faull OK;Pattinson KT;Jenkinson M
脑干直接参与控制血压、呼吸、睡眠/觉醒周期、疼痛调制、运动和心输出量。因此，它具有重要的基础科学和临床意义。然而，脑干的位置靠近主要动脉和相邻的脉动脑脊液填充空间，这意味着很难可靠地记录功能磁共振成像（fMRI）数据。这些生理噪声源在fMRI数据中产生时变信号，如果不进行校正，则会使感兴趣的信号模糊。在这篇方法文章中，我们将提供一个实用的介绍，用于纠正的存在下，生理噪声的时间序列功能磁共振成像数据的技术。基于心脏和呼吸周期的独立测量的技术，例如回顾性图像校正（RETROICOR，格洛弗等人），并讨论它们的应用和限制。的生理噪声模型，在一般的线性模型的框架内实施，静息fMRI数据采集在3和7 T的影响。描述了基于独立分量分析（伊卡）的数据驱动方法。MR采集策略，试图最大限度地减少生理波动对记录的功能磁共振成像数据的影响，或提供额外的信息，以纠正他们的存在，将被提及。一般建议建模噪声源，其影响统计推断通过损失的自由度，和非正交的回归，给出。最后，不同的策略，评估不同的方法来生理噪声建模的好处。
The brainstem is directly involved in controlling blood pressure, respiration, sleep/wake cycles, pain modulation, motor, and cardiac output. As such it is of significant basic science and clinical interest. However, the brainstem’s location close to major arteries and adjacent pulsatile cerebrospinal fluid filled spaces, means that it is difficult to reliably record functional magnetic resonance imaging (fMRI) data from. These physiological sources of noise generate time varying signals in fMRI data, which if left uncorrected can obscure signals of interest. In this Methods Article we will provide a practical introduction to the techniques used to correct for the presence of physiological noise in time series fMRI data. Techniques based on independent measurement of the cardiac and respiratory cycles, such as retrospective image correction (RETROICOR, Glover et al.,), will be described and their application and limitations discussed. The impact of a physiological noise model, implemented in the framework of the general linear model, on resting fMRI data acquired at 3 and 7 T is presented. Data driven approaches based such as independent component analysis (ICA) are described. MR acquisition strategies that attempt to either minimize the influence of physiological fluctuations on recorded fMRI data, or provide additional information to correct for their presence, will be mentioned. General advice on modeling noise sources, and its effect on statistical inference via loss of degrees of freedom, and non-orthogonality of regressors, is given. Lastly, different strategies for assessing the benefit of different approaches to physiological noise modeling are presented.
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影响因子：
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作者：
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通讯作者：
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