期刊
MAGNETIC RESONANCE IN MEDICINE
卷 84, 期 3, 页码 1293-1305出版社
WILEY
DOI: 10.1002/mrm.28203
关键词
Akaike information criterion; autocorrelation; autoregressive model; fast fMRI; noise serial correlation; prewhitening
资金
- National Institute of General Medical Sciences
- William K. Warren Foundation
- National Institutes of Health [P20GM121312]
Purpose In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T-statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high-order autoregressive model (AR(p), where p is the model order) based prewhitening method in the SC correction. Methods Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (AR(AICc)) that employs the corrected Akaike information criterion voxel-wise to automatically select the model orders for each voxel. To evaluate accuracy of SC correction, false positive characteristics were measured by assuming the presence of block and event-related tasks in the null data without image smoothing. The performance of prewhitening was also examined in smoothed images by adding pseudo task fMRI signals into the null data and comparing the detected to simulated activations (ground truth). Results The measured false positive characteristics agreed well with the theoretical curve when using the AR(AICc), and the activation maps in the smoothed data matched the ground truth. The AR(AICc) showed improved performance than the fixed AR(p) method. Conclusion The AR(AICc) can effectively remove noise SC, and accurate statistical analysis results can be obtained with the AR(AICc) correction in fast fMRI.
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