Correlation Between Fractional Anisotropy and Motor Outcomes in One-Year-Old Infants With Periventricular Brain Injury

J Magn Reson Imaging. 2014 Apr;39(4):949-57. doi: 10.1002/jmri.24256. Epub 2013 Oct 17.

Abstract

Purpose: To determine whether motor outcomes of an exercise intervention beginning at 2 months corrected age (CA) in children with periventricular brain injury (PBI) are correlated with fractional anisotropy (FA) measures derived from diffusion tensor imaging (DTI) at 12 months CA.

Materials and methods: DTI was performed in eight infants with PBI who were randomly assigned to kicking and treadmill stepping exercise or a no-training condition. Development was assessed using the Alberta Infant Motor Scale (AIMS) and the Gross Motor Function Classification System (GMFCS). FA values were derived from regions of interest (ROIs) in the middle third of the posterior limb of the internal capsule (PLIC) and the posterior thalamic radiation (PTR).

Results: Significant correlations were observed between motor development and FA measures. For PLIC, the correlation coefficients were 0.82 between FA and AIMS, and -0.92 between FA and GMFCS, while for PTR the corresponding correlation coefficients were 0.73 and -0.80, respectively.

Conclusion: Results of this study suggest that quantitative evaluation of white matter tracts using DTI at 12 months CA may be useful for assessment of brain plasticity in children.

Keywords: DTI; fractional anisotropy (FA); infants; periventricular brain injury; treadmill exercise.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anisotropy
  • Brain Injuries / complications*
  • Brain Injuries / pathology*
  • Cerebral Ventricles / injuries*
  • Cerebral Ventricles / pathology*
  • Diffusion Tensor Imaging / methods*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Infant
  • Male
  • Movement Disorders / diagnosis*
  • Movement Disorders / etiology*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Statistics as Topic