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Neuroimage. 2014 Oct 15;100:75-90. doi: 10.1016/j.neuroimage.2014.04.048. Epub 2014 May 9.

Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics.

Author information

1
Imaging Genetics Center, University of Southern California, Los Angeles, CA 90089, USA; Institute for Neuroimaging & Informatics, Departments of Neurology, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: yjinz@ucla.edu.
2
Institute for Neuroimaging & Informatics, Departments of Neurology, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: yonggans@usc.edu.
3
Imaging Genetics Center, University of Southern California, Los Angeles, CA 90089, USA; Institute for Neuroimaging & Informatics, Departments of Neurology, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: zhan.liang@gmail.com.
4
Imaging Genetics Center, University of Southern California, Los Angeles, CA 90089, USA; Institute for Neuroimaging & Informatics, Departments of Neurology, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: bgutman@gmail.com.
5
School of Psychology, University of Queensland, St. Lucia, QLD 4072, Australia. Electronic address: greig.dezubicaray@uq.edu.au.
6
Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD 4072, Australia. Electronic address: katie.mcmahon@cai.uq.edu.au.
7
QIMR Berghofer Institute of Medical Research, Herston, QLD 4029, Australia. Electronic address: margie.wright@qimr.edu.au.
8
Institute for Neuroimaging & Informatics, Departments of Neurology, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: toga@usc.edu.
9
Imaging Genetics Center, University of Southern California, Los Angeles, CA 90089, USA; Institute for Neuroimaging & Informatics, Departments of Neurology, University of Southern California, Los Angeles, CA 90089, USA; Department of Psychiatry, Pediatrics, Engineering, Radiology, and Ophthalmology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: pthomp@usc.edu.

Abstract

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion--a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

KEYWORDS:

Fiber clustering; Genetic heritability; HARDI; Label fusion; Tractography

PMID:
24821529
PMCID:
PMC4255631
DOI:
10.1016/j.neuroimage.2014.04.048
[Indexed for MEDLINE]
Free PMC Article

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