A java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines
In fMRI analysis, although univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interests in multivariate approaches such as principal component analysis (PCA), canonical variates analysis (CVA), independent component analysis (ICA) and cluster analysis that have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly-used fMRI preprocessing steps and optimized the associated multivariate CVA-based single-subject processing pipelines with the NPAIRS performance metrics (prediction accuracy and statistical parametric image (SPI) reproducibility) on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.FEAT, NPAIRS.GLM and NPAIRS.CVA software packages (or modules) using a second-level CVA. We found that for the block-design data: (1) Slice timing correction and global intensity normalization have little consistent impact on the fMRI processing pipeline, but spatial smoothing, temporal detrending or high-pass filtering, and motion correction significantly improved pipeline performance across all subjects; (2) The combined optimization of spatial smoothing, temporal detrending and CVA model parameters further improved pipeline performance; and (3) The most important pipeline choices include univariate-or-multivariate statistical models, and individually optimized spatial smoothing. This study suggests that considering options other than simply using GLM with a fixed spatial filter may be of critical importance in determining activation patterns in BOLD fMRI studies.
Zhang J., Liang L., Anderson J.R., Gatewood L., Rottenberg D.A. & Strother S.C.
ISSN: 1539-2791. Keywords: fMRI, fMRI processing pipeline, Machine learning, Data mining, Prediction accuracy, Classification accuracy, Reproducibility, Cross-validation