The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics

Stephen LaConte, Jon Anderson, Suraj Muley, James Ashe, Sally Frutiger, Kelly Rehm, Lars Kai Hansen, Essa Yacoub, Xiaoping Hu, David Rottenberg, Stephen Strother

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

Abstract

This work proposes an alternative to simulation-based receiver operating characteristic (ROC) analysis for assessment of fMRI data analysis methodologies. Specifically, we apply the rapidly developing nonparametric prediction, activation, influence, and reproducibility resampling (NPAIRS) framework to obtain cross-validation-based model performance estimates of prediction accuracy and global reproducibility for various degrees of model complexity. We rely on the concept of an analysis chain meta-model in which all parameters of the preprocessing steps along with the final statistical model are treated as estimated model parameters. Our ROC analog, then, consists of plotting prediction vs. reproducibility results as curves of model complexity for competing meta-models. Two theoretical underpinnings are crucial to utilizing this new validation technique. First, we explore the relationship between global signal-to-noise and our reproducibility estimates as derived previously. Second, we submit our model complexity curves in the prediction versus reproducibility space as reflecting classic bias-variance tradeoffs. Among the particular analysis chains considered, we found little impact in performance metrics with alignment, some benefit with temporal detrending, and greatest improvement with spatial smoothing.

Original languageEnglish (US)
Pages (from-to)10-27
Number of pages18
JournalNeuroImage
Volume18
Issue number1
DOIs
StatePublished - 2003

Bibliographical note

Funding Information:
The authors acknowledge the thoughtful comments from our anonymous reviewers; the practical discussions with Professor Vladimir Cherkassky; the helpful comments of Dr. Shing-Chung Ngan, Kirt Shaper, and Craig Benson; and the technical assistance from James Arnold. This work was partly supported by the NIH Human Brain Project P20 Grant MN57180.

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