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

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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

LaConte S,Anderson J,Muley S,Ashe J,Frutiger S,Rehm K,Hansen LK,Yacoub E,Hu X,Rottenberg D,Strother S

doi

10.1006/nimg.2002.1300

keywords:

subject

Has Abstract

pub_date

2003-01-01 00:00:00

pages

10-27

issue

1

eissn

1053-8119

issn

1095-9572

pii

S1053811902913005

journal_volume

18

pub_type

杂志文章

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