ArticleHuman brain mapping2017
The impact of T1 versus EPI spatial normalization templates for fMRI data analyses.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 205 papers, 1 of them a synthesis that pooled it.
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Who cites it
205 citing papers in PubMed, 1 synthesis or guideline pooled it, 321 citations in OpenAlex.
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- Robustness of NeuroMark-Derived Functional Networks to fMRI Spatial Normalization Across the Human Lifespan.bioRxiv : the preprint server for biology · 2026Article
- The Effect of Caffeine Consumption and Acute Withdrawal on Resting-State fMRI Brain Connectivity, Mood and Cognition.The European journal of neuroscience · 2026Article
- Gestational PBDE concentrations and functional connectivity in adolescents: The HOME Study.International journal of hygiene and environmental health · 2026Article
145 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
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. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.