Sensitive diffusion tensor imaging quantification method to identify language pathway abnormalities in children with developmental delay

J Pediatr. 2012 Jan;160(1):147-51. doi: 10.1016/j.jpeds.2011.06.036. Epub 2011 Aug 11.

Abstract

Objective: To investigate whether abnormal regional white matter architecture in the perisylvian region could be used as an easy and sensitive quantitative method to demonstrate language pathway abnormalities in children with developmental delay (DD).

Study design: We performed diffusion tensor imaging in 15 DD subjects (age, 61.1 ± 20.9 months) and 15 age-matched typically developing (TD) children (age, 68.4 ± 19.2 months). With diffusion tensor imaging color-coded orientation maps, we quantified the fraction of fibers in the perisylvian region that are oriented in anteroposterior (AP) and mediolateral (ML) directions, and their ratio (AP/ML) was calculated.

Results: The AP/ML ratio was more sensitive than tractography in characterizing perisylvian regional abnormalities in DD children. The AP/ML ratio of the left perisylvian region was significantly lower in DD children compared with TD children (P = .03). The ML component of bilateral perisylvian regions was significantly higher in DD children compared with TD children (P = .01 [left] and P = .004 [right]). No significant difference was found in the AP component in the two groups. A significant negative correlation of the left ML component with Vineland communication skills was observed (r = -0.657, P = .011).

Conclusions: The AP/ML ratio appears to be a sensitive indicator of regional white matter architectural abnormalities in the perisylvian region of DD children.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Brain Mapping / methods*
  • Child
  • Child, Preschool
  • Developmental Disabilities / complications*
  • Diffusion Tensor Imaging*
  • Female
  • Humans
  • Language Development Disorders / diagnosis*
  • Language Development Disorders / etiology*
  • Male
  • Neuropsychological Tests
  • Sensitivity and Specificity