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

Vince Daniel Calhoun, Tor D. Wager, Anjali Krishnan, Keri S. Rosch, Karen E Seymour, Mary Beth Nebel, Stewart H Mostofsky, Prashanth Nyalakanai, Kent Kiehl

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalHuman Brain Mapping
DOIs
StateAccepted/In press - 2017

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Echo-Planar Imaging
Magnetic Resonance Imaging
Sample Size
Software

Keywords

  • Coregistration
  • Echo planar image
  • FMRI
  • Spatial normalization

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Cite this

The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. / Calhoun, Vince Daniel; Wager, Tor D.; Krishnan, Anjali; Rosch, Keri S.; Seymour, Karen E; Nebel, Mary Beth; Mostofsky, Stewart H; Nyalakanai, Prashanth; Kiehl, Kent.

In: Human Brain Mapping, 2017.

Research output: Contribution to journalArticle

Calhoun, Vince Daniel ; Wager, Tor D. ; Krishnan, Anjali ; Rosch, Keri S. ; Seymour, Karen E ; Nebel, Mary Beth ; Mostofsky, Stewart H ; Nyalakanai, Prashanth ; Kiehl, Kent. / The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. In: Human Brain Mapping. 2017.
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AU - Wager, Tor D.

AU - Krishnan, Anjali

AU - Rosch, Keri S.

AU - Seymour, Karen E

AU - Nebel, Mary Beth

AU - Mostofsky, Stewart H

AU - Nyalakanai, Prashanth

AU - Kiehl, Kent

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N2 - 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.

AB - 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.

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