Comparison of template registration methods for multi-site meta-analysis of brain morphometry

Faskowitz, Joshua, de Zubicaray, Greig I., McMahon, Katie L., Wright, Margaret J., Thompson, Paul M. and Jahanshad, Neda (2016). Comparison of template registration methods for multi-site meta-analysis of brain morphometry. In: Barjor Gimi and Andrzej Krol, Proceedings SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging. SPIE Biomedical Applications in Molecular, Structural and Functional Imaging Conference, San Diego, CA, United States, (978822-1-978822-14). 1-3 March 2016. doi:10.1117/12.2217370


Author Faskowitz, Joshua
de Zubicaray, Greig I.
McMahon, Katie L.
Wright, Margaret J.
Thompson, Paul M.
Jahanshad, Neda
Title of paper Comparison of template registration methods for multi-site meta-analysis of brain morphometry
Conference name SPIE Biomedical Applications in Molecular, Structural and Functional Imaging Conference
Conference location San Diego, CA, United States
Conference dates 1-3 March 2016
Proceedings title Proceedings SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging   Check publisher's open access policy
Journal name SPIE - International Society for Optical Engineering. Proceedings   Check publisher's open access policy
Place of Publication Bellingham, WA, United States
Publisher S P I E - International Society for Optical Engineering
Publication Year 2016
Year available 2016
Sub-type Fully published paper
DOI 10.1117/12.2217370
Open Access Status Not Open Access
ISBN 9781510600232
ISSN 0277-786X
1996-756X
Editor Barjor Gimi
Andrzej Krol
Volume 17
Issue 43
Start page 978822-1
End page 978822-14
Total pages 14
Collection year 2017
Language eng
Abstract/Summary Neuroimaging consortia such as ENIGMA can significantly improve power to discover factors that affect the human brain by pooling statistical inferences across cohorts to draw generalized conclusions from populations around the world. Voxelwise analyses such as tensor-based morphometry also allow an unbiased search for effects throughout the brain. Even so, such consortium-based analyses are limited by a lack of high-powered methods to harmonize voxelwise information across study populations and scanners. While the simplest approach may be to map all images to a single standard space, the benefits of cohort-specific templates have long been established. Here we studied methods to pool voxel-wise data across sites using templates customized for each cohort but providing a meaningful common space across all studies for voxelwise comparisons. As non-linear 3D MRI registrations represent mappings between images at millimeter resolution, we need to consider the reliability of these mappings. To evaluate these mappings, we calculated test-retest statistics on the volumetric maps of expansion and contraction. Further, we created study-specific brain templates for ten T1-weighted MRI datasets, and a common space from four study-specific templates. We evaluated the efficacy of using a two-step registration framework versus a single standard space. We found that the two-step framework more reliably mapped subjects to a common space.
Keyword Multi-site
Voxelwise
Tensor-based morphometry
Test-retest reliability
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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