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Correction for pulse height variability reduces physiological noise in functional MRI when studying spontaneous brain activity

机译:研究自发性大脑活动时对脉冲高度变化的校正可减少功能性MRI中的生理噪声

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摘要

EEG correlated functional MRI (EEG‐fMRI) allows the delineation of the areas corresponding to spontaneous brain activity, such as epileptiform spikes or alpha rhythm. A major problem of fMRI analysis in general is that spurious correlations may occur because fMRI signals are not only correlated with the phenomena of interest, but also with physiological processes, like cardiac and respiratory functions. The aim of this study was to reduce the number of falsely detected activated areas by taking the variation in physiological functioning into account in the general linear model (GLM). We used the photoplethysmogram (PPG), since this signal is based on a linear combination of oxy‐ and deoxyhemoglobin in the arterial blood, which is also the basis of fMRI. We derived a regressor from the variation in pulse height (VIPH) of PPG and added this regressor to the GLM. When this regressor was used as predictor it appeared that VIPH explained a large part of the variance of fMRI signals acquired from five epilepsy patients and thirteen healthy volunteers. As a confounder VIPH reduced the number of activated voxels by 30% for the healthy volunteers, when studying the generators of the alpha rhythm. Although for the patients the number of activated voxels either decreased or increased, the identification of the epileptogenic zone was substantially enhanced in one out of five patients, whereas for the other patients the effects were smaller. In conclusion, applying VIPH as a confounder diminishes physiological noise and allows a more reliable interpretation of fMRI results. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.
机译:脑电图相关功能MRI(EEG-fMRI)可以勾画出与自发性大脑活动相对应的区域,例如癫痫样峰或阿尔法心律。通常,fMRI分析的一个主要问题是,可能发生虚假相关,因为fMRI信号不仅与感兴趣的现象相关,而且还与诸如心脏和呼吸功能的生理过程相关。这项研究的目的是通过在一般线性模型(GLM)中考虑生理功能的变化,减少错误检测到的激活区域的数量。我们使用光电容积描记法(PPG),因为该信号基于动脉血中氧合和脱氧血红蛋白的线性组合,这也是fMRI的基础。我们从PPG的脉冲高度(VIPH)的变化中得出了一个回归变量,并将该回归变量添加到GLM中。当使用该回归指标作为预测指标时,似乎VIPH解释了从5例癫痫患者和13例健康志愿者获得的fMRI信号的很大一部分方差。作为混杂因素,当研究阿尔法节奏的产生者时,对于健康的志愿者,VIPH将激活的体素数量减少了30%。尽管对于患者而言,激活体素的数量减少或增加,但五分之一的患者对癫痫发生区的识别大大增强,而对其他患者,影响较小。总之,将VIPH用作混杂因素可减少生理噪声,并能更可靠地解释fMRI结果。嗡嗡声脑图,2010年。©2009 Wiley-Liss,Inc.

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