Independent component analysis of DTI reveals multivariate microstructural correlations of white matter in the human brain

Authors

  • Yi-Ou Li,

    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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    • Authors Yi-Ou Li and Fanpei G. Yang contributed equally to this work.

  • Fanpei G. Yang,

    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
    2. Department of Foreign Languages and Literatures, National Tsinghua University, Hsinchu, Taiwan
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    • Authors Yi-Ou Li and Fanpei G. Yang contributed equally to this work.

  • Christopher T. Nguyen,

    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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  • Shelly R. Cooper,

    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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  • Sara C. LaHue,

    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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  • Sandya Venugopal,

    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
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  • Pratik Mukherjee

    Corresponding author
    1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
    2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California
    • Center for Molecular and Functional Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry Street, Box 0946, San Francisco, California 94107-0946
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Abstract

It has recently been demonstrated that specific patterns of correlation exist in diffusion tensor imaging (DTI) parameters across white matter tracts in the normal human brain. These microstructural correlations are thought to reflect phylogenetic and functional similarities between different axonal fiber pathways. However, this earlier work was limited in three major respects: (1) the analysis was restricted to only a dozen selected tracts; (2) the DTI measurements were averaged across whole tracts, whereas metrics such as fractional anisotropy (FA) are known to vary considerably within single tracts; and (3) a univariate measure of correlation was used. In this investigation, we perform an automated multivariate whole-brain voxel-based study of white matter FA correlations using independent component analysis (ICA) of tract-based spatial statistics computed from 3T DTI in 53 healthy adult volunteers. The resulting spatial maps of the independent components show voxels for which the FA values within each map co-vary across individuals. The strongest FA correlations were found in anatomically recognizable tracts and tract segments, either singly or in homologous pairs. Hence, ICA of DTI provides an automated unsupervised decomposition of the normal human brain into multiple separable microstructurally correlated white matter regions, many of which correspond to anatomically familiar classes of white matter pathways. Further research is needed to determine whether whole-brain ICA of DTI represents a novel alternative to tractography for feature extraction in studying the normal microstructure of human white matter as well as the abnormal white matter microstructure found in neurological and psychiatric disorders. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.

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