Detecting the Shape Differences of the Corpus Callosum in Behcet's Disease by Statistical Shape Analysis


ÇOLAK C., ERCAN İ. , Dogan M., Ozdemir S. T. , ŞENER S., Alkan A.

ANATOMICAL RECORD-ADVANCES IN INTEGRATIVE ANATOMY AND EVOLUTIONARY BIOLOGY, vol.294, no.5, pp.870-874, 2011 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 294 Issue: 5
  • Publication Date: 2011
  • Doi Number: 10.1002/ar.21373
  • Title of Journal : ANATOMICAL RECORD-ADVANCES IN INTEGRATIVE ANATOMY AND EVOLUTIONARY BIOLOGY
  • Page Numbers: pp.870-874

Abstract

The aim of this study was to assess the shape differences of the corpus callosum (CC) in patients with Behcet's disease using statistical shape analysis (SSA). Additionally, an attempt was made to investigate the changes in CC size according to disease duration. Twenty-five adults with clinically diagnosed Behcet's disease and 25 age-and gender-matched controls were examined by high-resolution structural magnetic resonance imaging. The data obtained from the coordinate of landmarks were analyzed with Euclidean distance matrix analysis and a thin-plate spline analysis. SSA and growth curve models were performed to investigate group differences and to fit the curves. A significant difference was determined between CC shape of Behcet patients and controls (P = 0.006). Based on the analysis, a decrease occurred in the CC size of the Behcet patients as the duration of disease increased. Maximum deformations were determined in the landmarks of interior notch of the splenium, inferior tip of the splenium, posterior-most point of the CC, and topmost point of the CC. Similarly, the landmark of anterior-most point of the CC was identified as having the minimum deformation. Behcet patients had significantly different CC shapes from control subjects. The results suggest that SSA is a promising tool for distinguishing Behcet patients from normal subjects, and that it can give useful information to assist clinicians. Additionally, SSA might be applied to detect shape differences in anatomical structures that are affected by a broad range of neurological diseases. Anat Rec, 294: 870-874, 2011. (C) 2011 Wiley-Liss, Inc.