Characterization of disease-related covariance topographies with SSMPCA toolbox: Effects of spatial normalization and PET scanners

Authors

  • Shichun Peng,

    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    Search for more papers by this author
    • Shichun Peng and Phoebe G. Spetsieris contributed equally to this work.

  • Yilong Ma,

    Corresponding author
    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    • Room FI-4358, 350 Community Drive, Manhasset, NY 11030. E-mail: yma@nshs.edu

    Search for more papers by this author
  • Phoebe G. Spetsieris,

    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    Search for more papers by this author
    • Shichun Peng and Phoebe G. Spetsieris contributed equally to this work.

  • Paul Mattis,

    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    Search for more papers by this author
  • Andrew Feigin,

    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    Search for more papers by this author
  • Vijay Dhawan,

    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    Search for more papers by this author
  • David Eidelberg

    1. Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York
    Search for more papers by this author

Abstract

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [18F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras. Hum Brain Mapp 35:1801–1814, 2014. © 2013 Wiley Periodicals, Inc.

Ancillary