Region-of-interest-based analysis with application of cortical thickness variation of left planum temporale in schizophrenia and psychotic bipolar disorder

Authors

  • Anqi Qiu,

    Corresponding author
    1. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland
    2. Division of Bioengineering, National University of Singapore, Singapore
    3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
    • Divsion of Bioengineering, Faculty of Engineering, National University of Singapore, 1 Engineering Dr. 2, Block E1, #-05-19, Singapore 117576
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  • Marc Vaillant,

    1. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland
    2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
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  • Patrick Barta,

    1. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland
    2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
    3. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland
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  • J.Tilak Ratnanather,

    1. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland
    2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
    3. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
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  • Michael I. Miller

    1. Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland
    2. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
    3. Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
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Abstract

In neuroimaging studies, spatial normalization and multivariate testing are central problems in characterizing group variation of functions (e.g., cortical thickness, curvature, functional response) in an atlas coordinate system across clinical populations. We present a region-of-interest (ROI)-based analysis framework for detecting such a group variation. This framework includes two main techniques: ROI-based registration via large deformation diffeomorphic metric surface mapping and a multivariate testing using a Gaussian random field (GRF) model on the cortical surface constructed by the eigenfunctions of the Laplace–Beltramioperator. We compared our GRF statistical model with a pointwise hypothesis testing approach, whose P-value is corrected using false discovery rate or random field theory at several smoothness scales. As an illustration, we applied this framework to a clinical study of the cortical thickness of the left planum temporale (PT) in subjects with psychotic bipolar disorder, schizophrenia, and healthy comparison controls. Our results show that the anterior portion of the left PT is thinner in the psychotic bipolar and schizophrenic groups than in the healthy control group, and the posterior portion of the left PT shows the reversal finding. Moreover, there may be a greater thickness variation in the left PT in psychotic bipolar patients when compared with that in schizophrenic patients. Hum Brain Mapp, 2008. © 2007 Wiley-Liss, Inc.

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