Modeling low-frequency fluctuation and hernodynamic response timecourse in event-related fMRI
Modeling low-frequency fluctuation and hernodynamic response timecourse in event-related fMRI
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DOI：
10.1002/hbm.20379
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发表时间：
2008-02-01
影响因子：
4.8
通讯作者：
Gallant, Jack L.
中科院分区：
文献类型：
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作者：
Kay, Kendrick N.;David, Stephen V.;Gallant, Jack L.
功能性磁共振成像（fMRI）存在许多问题，使得信号估计困难。这些包括体素间血流动力学反应的变化和低信噪比（SNR）。我们评估了几种分析技术，解决这些问题的事件相关功能磁共振成像。(1)许多fMRI分析假设一个典型的血流动力学响应函数，但这种假设可能会导致不准确的数据模型。通过采用有限脉冲响应模型，我们表明，体素特定的血流动力学响应函数可以直接从数据估计。(2)血氧水平依赖（BOLD）时间序列数据中存在大量的低频噪声波动（LFF）。为了弥补这个问题，我们使用多项式作为LFF的回归量。我们表明，这种技术大大提高了信噪比，是比高通滤波的数据更准确。(3)由于BOLD响应的低SNR，有限脉冲响应模型的模型过拟合是一个问题。为了减少过拟合，我们估计每个体素的血流动力学响应时程，并将时间-事件可分离性的约束，即事件类型之间的血流动力学响应在比例因子上是相同的约束。我们表明，这种技术大大提高了血流动力学反应估计的准确性，可以有效地计算。对于我们提出的分析技术，我们通过10倍交叉验证评估建模精度的提高。(c)2007 Wiley-Liss，Inc.
Functional magnetic resonance imaging (fMRI) suffers from many problems that make signal estimation difficult. These include variation in the hemodynamic response across voxels and low signal-to-noise ratio (SNR). We evaluate several analysis techniques that address these problems for event-related fMRI. (1) Many fMRI analyses assume a canonical hemodynamic response function, but this assumption may lead to inaccurate data models. By adopting the finite impulse response model, we show that voxel-specific hemodynamic response functions can be estimated directly from the data. (2) There is a large amount of low-frequency noise fluctuation (LFF) in blood oxygenation level dependent (BOLD) time-series data. To compensate for this problem, we use polynomials as regressors for LFF. We show that this technique substantially improves SNR and is more accurate than high-pass filtering of the data. (3) Model overfitting is a problem for the finite impulse response model because of the low SNR of the BOLD response. To reduce overfitting, we estimate a hemodynamic response timecourse for each voxel and incorporate the constraint of time-event separability, the constraint that hemodynamic responses across event types are identical up to a scale factor. We show that this technique substantially improves the accuracy of hemodynamic response estimates and can be computed efficiently. For the analysis techniques we present, we evaluate improvement in modeling accuracy via 10-fold cross-validation. (c) 2007 Wiley-Liss, Inc.