Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

Jing Zhang, Jon R. Anderson, Lichen Liang, Sujit K. Pulapura, Lael Gatewood, David A. Rottenberg, Stephen C. Strother

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which 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 (nonparametric prediction, activation, influence and reproducibility resampling) 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 novel second-level CVA. We found that for the block-design data, (a) 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; (b) the combined optimization of spatial smoothing, temporal detrending and CVA model parameters on average improved between-subject reproducibility; and (c) the most important pipeline choices include univariate or multivariate statistical models and 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.

Original languageEnglish (US)
Pages (from-to)264-278
Number of pages15
JournalMagnetic Resonance Imaging
Volume27
Issue number2
DOIs
StatePublished - Feb 2009

Bibliographical note

Funding Information:
We would like to thank James Ashe, M.D., and Suraj Muley, M.D., for providing the static force data. We are also grateful to Kelly Rehm, Kirt Schaper and Kate Fissell for valuable discussions and technical assistance. This work was partly supported by the NIH Human Brain Project P20 Grant MN EB002013.

Keywords

  • CVA
  • GLM
  • Pipeline evaluation
  • fMRI
  • fMRI processing pipeline

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