Making the most of fMRI at 7 T by suppressing spontaneous signal fluctuations.
Making the most of fMRI at 7 T by suppressing spontaneous signal fluctuations.
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DOI：
10.1016/j.neuroimage.2008.08.037
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发表时间：
2009-01-15
期刊：
影响因子：
5.7
通讯作者：
de Zwart JA
中科院分区：
文献类型：
--
作者：
Bianciardi M;van Gelderen P;Duyn JH;Fukunaga M;de Zwart JA
人类灰质中自发BOLD-fMRI信号波动的存在损害了诱发反应的检测和解释，并限制了通过线圈阵列和高场系统可能获得的灵敏度增益。为了克服这些局限性，我们调整和改进了最近描述的相关噪声抑制方法，证明了在7 T下估计超短视觉刺激响应的精度提高。在这个过程中，自发信号波动的时间动态估计从参考脑区域以外的区域与刺激的目标。而不是使用该区域中的平均信号作为回归量，如在原始方法中所提出的，我们使用主成分分析来导出多个回归量，以便最佳地描述滋扰信号（例如自发波动），并将这些与目标区域中的诱发活动分离。实验结果表明，应用原方法的估计精度提高了66%。该方法的新增强版本使用18个PCA衍生的噪声回归器，导致精度提高了160%。这些增加是相对于没有噪声抑制的控制条件，这是通过随机化的滋扰信号回归（S）的时间过程，而不改变其功率谱模拟。估计精度的提高与残差自相关水平的降低有关。这些结果表明，模拟自发的fMRI信号波动作为多个独立的来源，可以显着提高诱发活动的检测，并充分利用高场技术的潜在灵敏度增益。
The presence of spontaneous BOLD-fMRI signal fluctuations in human grey matter compromises the detection and interpretation of evoked responses and limits the sensitivity gains that are potentially available through coil arrays and high field systems. In order to overcome these limitations, we adapted and improved a recently described correlated-noise suppression method, demonstrating improved precision in estimating the response to ultra-short visual stimuli at 7 T. In this procedure, the temporal dynamics of spontaneous signal fluctuations are estimated from a reference brain region outside the area targeted with the stimulus. Rather than using the average signal in this region as regressor, as proposed in the original method, we used Principal Component Analysis to derive multiple regressors in order to optimally describe nuisance signals (e.g. spontaneous fluctuations) and separate these from evoked activity in the target region. Experimental results obtained from application of the original method showed a 66% improvement in estimation precision. The novel, enhanced version of the method, using 18 PCA-derived noise regressors, led to a 160% increase in precision. These increases were relative to a control condition without noise suppression, which was simulated by randomizing the time-course of the nuisance-signal regressor(s) without altering their power spectrum. The increase of estimation precision was associated with decreased autocorrelation levels of the residual errors. These results suggest that modeling of spontaneous fMRI signal fluctuations as multiple independent sources can dramatically improve detection of evoked activity, and fully exploit the potential sensitivity gains available with high field technology.
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