Luo Qingfei, Misaki Masaya, Mulyana Ben, Wong Chung-Ki, Bodurka Jerzy
Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.
Stephenson School for Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, USA.
Magn Reson Med. 2020 Sep;84(3):1293-1305. doi: 10.1002/mrm.28203. Epub 2020 Feb 14.
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.
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 ) 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).
The measured false positive characteristics agreed well with the theoretical curve when using the AR , and the activation maps in the smoothed data matched the ground truth. The AR showed improved performance than the fixed AR(p) method.
The AR can effectively remove noise SC, and accurate statistical analysis results can be obtained with the AR correction in fast fMRI.
在快速采集的功能磁共振成像（fast fMRI）数据中，噪声序列相关性（SC）会导致T统计量被高估，从而产生有问题的无效统计推断。本研究旨在评估并提高基于高阶自回归模型（AR(p)，其中p为模型阶数）的预白化方法在SC校正中的准确性。
使用多波段同时多层回波平面成像脉冲序列，在静息状态下（空数据）采集fast fMRI图像，重复时间（TR）=300和500毫秒。基于两种AR(p)模型的预白化方法对fast fMRI数据中的SC效应进行校正：（1）传统模型（固定AR(p)），为所有图像体素预先选择一个恒定的p；（2）改进模型（AR ），逐体素采用校正后的赤池信息准则自动选择每个体素的模型阶数。为评估SC校正的准确性，在未进行图像平滑处理的空数据中假设存在块任务和事件相关任务，测量假阳性特征。通过向空数据中添加伪任务fMRI信号并将检测到的激活与模拟激活（真实情况）进行比较，还在平滑图像中检查了预白化的性能。
使用AR 时，测量的假阳性特征与理论曲线吻合良好，平滑数据中的激活图与真实情况匹配。AR 显示出比固定AR(p)方法更好的性能。
AR 可以有效去除噪声SC，在fast fMRI中使用AR 校正可获得准确的统计分析结果。
