MP-PCA denoising of fMRI time-series data can lead to artificial activation spreading

MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective thresh-old to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further con-founding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation spreading caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conven-tional multislice and ultrafast fMRI acquisitions (1 s and 50 ms temporal resolution, respectively), using a visual stimulation paradigm. MP-PCA denoising produced SNR gains of 64% and 39%, and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation spreading with increased false positive rate and smoother functional maps due to local bleeding of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This spreading effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.

Automated summary

MP-PCA denoising is a popular method for denoising MRI data, but in rodent fMRI, thermal noise from coils can affect activation mapping accuracy and violate MP-PCA assumptions. In this study, we developed a method to denoise vendor data in rodent fMRI and evaluated the effects of MP-PCA denoising on activation spreading. Our results showed that MP-PCA denoising improved SNR and Fourier Spectral Amplitude, but also caused activation spreading and smoother functional maps. The optimal denoising window for improved specificity depends on the data's tSNR and functional CNR.