Surface-based analysis on shape and fractional anisotropy of white matter tracts in Alzheimer's disease

PLoS One. 2010 Mar 22;5(3):e9811. doi: 10.1371/journal.pone.0009811.

Abstract

Background: White matter disruption has been suggested as one of anatomical features associated with Alzheimer's disease (AD). Diffusion tensor imaging (DTI), which has been widely used in AD studies, obtains new insights into the white matter structure.

Methods: We introduced surface-based geometric models of the deep white matter tracts extracted from DTI, allowing the characterization of their shape variations relative to an atlas as well as fractional anisotropy (FA) variations on the atlas surface through large deformation diffeomorphic metric mapping (LDDMM). We applied it to assess local shapes and FA variations of twenty-three deep white matter tracts in 13 patients with AD and 19 healthy control subjects.

Results: Our results showed regionally-specific shape abnormalities and FA reduction in the cingulum tract and the sagittal stratum tract in AD, suggesting that disruption in the white matter tracts near the temporal lobe may represent the secondary consequence of the medial temporal lobe pathology in AD. Moreover, the regionally-specific patterns of FA and shape of the white matter tracts were shown to be of sufficient sensitivity to robustly differentiate patients with AD from healthy comparison controls when compared with the mean FA and volumes within the regions of the white matter tracts. Finally, greater FA or deformation abnormalities of the white matter tracts were associated with lower MMSE scores.

Conclusion: The regionally-specific shape and FA patterns could be potential imaging markers for differentiating AD from normal aging.

Publication types

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

MeSH terms

  • Aged
  • Algorithms
  • Alzheimer Disease / pathology*
  • Anisotropy
  • Brain / pathology*
  • Brain Mapping / methods
  • Case-Control Studies
  • Diffusion Tensor Imaging / methods
  • Humans
  • Middle Aged
  • Models, Statistical
  • Principal Component Analysis
  • Reproducibility of Results
  • Surface Properties