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

Document Type

Article

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. © 2002 Elsevier Science (USA).

Publication Date

1-1-2003

Publication Title

NeuroImage

ISSN

10538119

Volume

18

Issue

1

First Page

10

Last Page

27

PubMed ID

12507440

Digital Object Identifier (DOI)

10.1006/nimg.2002.1300

This document is currently not available here.

Share

COinS