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Proceedings Paper

The heritability of the functional connectome is robust to common nonlinear registration methods
Author(s): George W. Hafzalla; Gautam Prasad; Vatche G. Baboyan; Joshua Faskowitz; Neda Jahanshad; Katie L. McMahon; Greig I. de Zubicaray; Margaret J. Wright; Meredith N. Braskie; Paul M. Thompson
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Paper Abstract

Nonlinear registration algorithms are routinely used in brain imaging, to align data for inter-subject and group comparisons, and for voxelwise statistical analyses. To understand how the choice of registration method affects maps of functional brain connectivity in a sample of 611 twins, we evaluated three popular nonlinear registration methods: Advanced Normalization Tools (ANTs), Automatic Registration Toolbox (ART), and FMRIB's Nonlinear Image Registration Tool (FNIRT). Using both structural and functional MRI, we used each of the three methods to align the MNI152 brain template, and 80 regions of interest (ROIs), to each subject's T1-weighted (T1w) anatomical image. We then transformed each subject's ROIs onto the associated resting state functional MRI (rs-fMRI) scans and computed a connectivity network or functional connectome for each subject. Given the different degrees of genetic similarity between pairs of monozygotic (MZ) and same-sex dizygotic (DZ) twins, we used structural equation modeling to estimate the additive genetic influences on the elements of the function networks, or their heritability. The functional connectome and derived statistics were relatively robust to nonlinear registration effects.

Paper Details

Date Published: 21 March 2016
PDF: 8 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841R (21 March 2016); doi: 10.1117/12.2217376
Show Author Affiliations
George W. Hafzalla, The Univ. of Southern California (United States)
Gautam Prasad, The Univ. of Southern California (United States)
Vatche G. Baboyan, The Univ. of Southern California (United States)
Joshua Faskowitz, The Univ. of Southern California (United States)
Neda Jahanshad, The Univ. of Southern California (United States)
Katie L. McMahon, The Univ. of Queensland (Australia)
Greig I. de Zubicaray, Queensland Univ. of Technology (Australia)
Margaret J. Wright, QIMR Berghofer Medical Research Institute (Australia)
Meredith N. Braskie, The Univ. of Southern California (United States)
Paul M. Thompson, The Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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