Centre for Advanced Imaging, The University of Queensland, Brisbane QLD 4072, Australia.
Queensland Brain Institute, The University of Queensland, Brisbane QLD 4072, Australia.
Neuroimage. 2018 Feb 1;166:152-166. doi: 10.1016/j.neuroimage.2017.10.043. Epub 2017 Oct 21.
When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.
在对单个体素 fMRI 数据进行统计分析时，需要考虑序列相关性，以进行有效的推断。否则，参数估计的可变性可能会被低估，从而导致假阳性率增加。fMRI 数据中的序列相关性通常用一阶自回归 (AR) 过程来描述，然后通过预白化来消除。预白化所需的噪声模型取决于许多参数，特别是重复时间 (TR)。在这里，我们研究了由同时多层 (SMS) 成像提供的亚秒级时间分辨率如何改变 fMRI 时间序列中的噪声结构。我们拟合了一个更高阶的 AR 模型，然后估计了 TR 小于 600ms 的序列的最佳 AR 模型阶数，以提供全脑覆盖。我们表明，生理噪声模型可以成功地降低所需的 AR 模型阶数，但仍然存在的序列相关性需要一个先进的噪声模型。我们得出结论，常用的噪声模型，如 AR(1)模型，对于使用亚秒 TR 进行 fMRI 中的序列相关性建模是不够的。相反，生理噪声模型与先进的预白化方案相结合，可以在使用快速 fMRI 序列的单个体分析中进行有效的推断。
