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Hum Brain Mapp. 2017 Nov;38(11):5331-5342. doi: 10.1002/hbm.23737. Epub 2017 Jul 26.

The impact of T1 versus EPI spatial normalization templates for fMRI data analyses.

Author information

1
The Mind Research Network & LBERI, Albuquerque, New Mexico.
2
Department of ECE, University of New Mexico, Albuquerque, New Mexico.
3
University of Colorado at Boulder, Boulder, Colorado.
4
Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland.
5
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.
6
Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
7
Department of Psychology, University of New Mexico, Albuquerque, New Mexico.

Abstract

Spatial normalization of brains to a standardized space is a widely used approach for group studies in functional magnetic resonance imaging (fMRI) data. Commonly used template-based approaches are complicated by signal dropout and distortions in echo planar imaging (EPI) data. The most widely used software packages implement two common template-based strategies: (1) affine transformation of the EPI data to an EPI template followed by nonlinear registration to an EPI template (EPInorm) and (2) affine transformation of the EPI data to the anatomic image for a given subject, followed by nonlinear registration of the anatomic data to an anatomic template, which produces a transformation that is applied to the EPI data (T1norm). EPI distortion correction can be used to adjust for geometric distortion of EPI relative to the T1 images. However, in practice, this EPI distortion correction step is often skipped. We compare these template-based strategies empirically in four large datasets. We find that the EPInorm approach consistently shows reduced variability across subjects, especially in the case when distortion correction is not applied. EPInorm also shows lower estimates for coregistration distances among subjects (i.e., within-dataset similarity is higher). Finally, the EPInorm approach shows higher T values in a task-based dataset. Thus, the EPInorm approach appears to amplify the power of the sample compared to the T1norm approach when not using distortion correction (i.e., the EPInorm boosts the effective sample size by 12-25%). In sum, these results argue for the use of EPInorm over the T1norm when no distortion correction is used. Hum Brain Mapp 38:5331-5342, 2017.

KEYWORDS:

coregistration; echo planar image; fMRI; spatial normalization

PMID:
28745021
PMCID:
PMC5565844
DOI:
10.1002/hbm.23737
[Indexed for MEDLINE]
Free PMC Article

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