title: Automatic classification of early Parkinson's disease with multi-modal MR imaging.
abstract: 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.
  
  
  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.
authors.affiliation: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Dan
authors.initials: D
authors.lastname: Long
authors.forename: Jinwei
authors.initials: J
authors.lastname: Wang
authors.forename: Min
authors.initials: M
authors.lastname: Xuan
authors.forename: Quanquan
authors.initials: Q
authors.lastname: Gu
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Xu
authors.forename: Dexing
authors.initials: D
authors.lastname: Kong
authors.forename: Minming
authors.initials: M
authors.lastname: Zhang
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000145:classification / Q000175:diagnosis*; D015203:Reproducibility of Results