Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest.
Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest.
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DOI：
10.3389/fnhum.2013.00168
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发表时间：
2013
影响因子：
2.9
通讯作者：
Moser E
中科院分区：
文献类型：
--
作者：
Boubela RN;Kalcher K;Huf W;Kronnerwetter C;Filzmoser P;Moser E
使用fMRI分析静息态网络通常会忽略BOLD信号中的高频波动--可能是因为低TR禁止分析频率高于0.25 Hz的波动（典型TR为2 s），或者是因为应用了带通滤波器（通常将信号限制在低于0.1 Hz的频率）。虽然卷积神经元活动与血流动力学响应函数的标准模型表明，在功能磁共振成像中的感兴趣的信号的特征在于缓慢的波动，它实际上是不清楚的信号的高频动态是否只包括噪声。在这项研究中，10名受试者进行了扫描，在3 T在6分钟的休息，使用多波段EPI序列与TR为354 ms的关键采样波动高达1.4 Hz。对预处理后的数据进行高通滤波，仅包括0.25 Hz以上的频率，并使用体素全脑时间伊卡（tICA）来识别一致的高频信号。所得到的分量包括生理背景信号源，最显著的是脉动和心跳分量，其可以用本文提出的方法具体地识别和定位。也许更令人惊讶的是，像默认模式网络这样的常见静止状态网络也作为单独的tICA组件出现。这意味着以相当T1加权对比度采样的高频振荡仍然包含关于这些静止状态网络的特定信息，以一致地识别它们，这与通常认为这些网络仅在低频波动上运行的观点不一致。因此，应重新考虑在静息状态数据分析中使用带通滤波器，因为这一步骤消除了潜在的相关信息。相反，用于消除生理背景信号的更具体的方法，例如通过生理噪声分量的回归，可能被证明是可行的替代方案。
Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal – be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.