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

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 templatebased 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.

Interregional causal influences of brain metabolic activity reveal the spread of aging effects during normal aging

“…We chose the direct normalization approach rather than using an anatomical MRI as a mediator, because the spatial resolutions of the PET images were adequate and the direct normalization has its own advantage compared with the anatomical MRI-mediated method (Calhoun et al, 2017). For each subject, if there were more than one PET image in a session, all the PET images in the session were realigned to the first image and the mean image of the session was calculated.…”

“…The cross-sessional mean image was normalized directly to the PET template in SPM in standard Montreal Neurological Institute (MNI) space, and then all the images were normalized to MNI space using the same set of parameters. We chose the direct normalization approach rather than using an anatomical MRI as a mediator, because the spatial resolutions of the PET images were adequate and the direct normalization has its own advantage compared with the anatomical MRI-mediated method (Calhoun et al, 2017). The images were then spatially smoothed using a Gaussian kernel with 8 mm full width at half maximum.…”

Comparison of heritability estimates on resting state fMRI connectivity phenotypes using the ENIGMA analysis pipeline

“…Arguably, a disadvantage of this approach is that spatial distortions in rsfMRI data may prevent consistent registration with the structural data unless corrected for. Calhoun and colleagues examined the unimodal and multimodal approaches in four datasets and registering functional data to a population template led to superior registration quality, lower inter-subject variance and higher statistical significance when compared to the multimodal approach (Calhoun, et al, 2017). This problem is likely to be encountered in ENIGMA studies that aggregate data collected over the past 20 years.…”

“…The ENIGMA-rsfMRI pipeline is uni-modal and is based on a populationbased brain template. Prior works on rsfMRI analysis are generally multimodal and use functional and structural data (Calhoun et al, 2017). In such approaches, a spatial transformation of the functional data to the structural image for each subject is followed by nonlinear registration of the structural data to an anatomic template.…”

Longitudinally consistent estimates of intrinsic functional networks

“…Each rs‐fMRI image was registered to the SRI24 atlas (Rohlfing, Zahr, Sullivan, & Pfefferbaum, ). While some recent studies suggest to directly register the mean BOLD image to the atlas (Adhikara, Jahanshad, Shukla, ; Calhoun et al, ; Dohmatob et al, ), the processing here complied with the baseline publication of NCANDA (Müller‐Oehring et al, ), which first aligned the mean BOLD image to the subject‐specific T1‐weighted MRI and then nonrigidly registered T1‐weighted MRI to the SRI atlas. Visual inspection insured that the quality of the registration between the mean BOLD image and the atlas space was high.…”