Automatic classification of early Parkinson's disease with multi-modal MR imaging

PLoS One. 2012;7(11):e47714. doi: 10.1371/journal.pone.0047714. Epub 2012 Nov 9.


Background: In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of early PD by integrating the advantages of various modals.

Materials and methods: Nineteen early PD patients and twenty-seven normal volunteers participated in this study. For each subject, we collected resting-state functional magnetic resonance imaging (rsfMRI) and structural images. For the rsfMRI images, we extracted the characteristics at three different levels: ALFF (amplitude of low-frequency fluctuations), ReHo (regional homogeneity) and RFCS (regional functional connectivity strength). For the structural images, we extracted the volume characteristics from the gray matter (GM), the white matter (WM) and the cerebrospinal fluid (CSF). A two-sample t-test was used for the feature selection, and then the remaining features were fused for classification. Finally a classifier for early PD patients and normal control subjects was identified from support vector machine training. The performance of the classifier was evaluated using the leave-one-out cross-validation method.

Results: Using the proposed methods to classify the data set, good results (accuracy = 86.96%, sensitivity = 78.95%, specificity = 92.59%) were obtained.

Conclusions: This method demonstrates a promising diagnosis performance by the integration of information from a variety of imaging modalities, and it shows potential for improving the clinical diagnosis and treatment of PD.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Brain / pathology
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Magnetic Resonance Imaging*
  • Male
  • Middle Aged
  • Parkinson Disease / classification
  • Parkinson Disease / diagnosis*
  • Reproducibility of Results

Grant support

This work was supported by the Key Projects in the National Science & Technology Pillar Program during the Twelfth Five-Year Plan Period (grant 2012BAI10B04), National Natural Science Foundation of China (grant 30570536 and 30900358), Medical Scientific Research Foundation of Zhejiang Province (grant 2009QN005) and Scientific Research Project of Zhejiang Province (grant 2011C37025). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.