A deformation-based morphometry study of patients with early-stage Parkinson's disease

Eur J Neurol. 2010 Feb;17(2):314-20. doi: 10.1111/j.1468-1331.2009.02807.x. Epub 2009 Nov 11.


Background and purpose: Previous volumetric magnetic resonance imaging (MRI) studies of Parkinson's disease (PD) utilized primarily voxel-based morphometry (VBM), and investigated mostly patients with moderate- to late-stage disease. We now use deformation-based morphometry (DBM), a method purported to be more sensitive than VBM, to test for atrophy in patients with early-stage PD.

Methods: T1-weighted MRI images from 24 early-stage PD patients and 26 age-matched normal control subjects were compared using DBM. Two separate studies were conducted, where two minimally-biased nonlinear intensity-average were created; one for all subjects and another for just the PD patients. The DBM technique creates an average population-based MRI-average in an iterative hierarchical fashion. The nonlinear transformations estimated to match each subject to the MRI-average were then analysed.

Results: The DBM comparison between patients and controls revealed significant contraction in the left cerebellum, and non-significant trends towards frontal, temporal and cingulate sulcal expansions with frontal and temporal white matter contractions. Within the patient group, the unified PD rating scores were highly correlated with local expansions in or near sulci bordering on frontal and temporal cortex.

Conclusion: Our results suggest that DBM could be a sensitive method for detecting morphological changes in early-stage PD.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Atrophy
  • Brain / pathology*
  • Case-Control Studies
  • Disease Progression
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Neuropsychological Tests
  • Nonlinear Dynamics
  • Parkinson Disease / diagnosis
  • Parkinson Disease / drug therapy
  • Parkinson Disease / pathology*
  • Regression Analysis
  • Sensitivity and Specificity
  • Severity of Illness Index
  • Time Factors