PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing
Mark Bastin, Jakub Piatkowksi, Amos Storkey, L.J. Brown, A.M. Maclullich and Jonathan D. Clayden
Neuroimage Volume 43, pp. 20-28, 2008.

Abstract

Understanding how ageing affects brain structure is an important challenge for medical science. By allowing segmentation of fasciculi-of-interest from diffusion magnetic resonance imaging (dMRI) data, tractography provides a promising tool for assessing white matter connectivity in old age. However, the output from tractography algorithms is usually strongly dependent on the subjective location of user-specified seed points, with the result that it can be both difficult and time consuming to identify the same tract reliably in cross-sectional studies. Here we investigate whether a novel method for automatic single seed point placement based on tract shape modelling, termed probabilistic model-based neighbourhood tractography (PNT), can reliably segment the same tract from subject to subject in a non-demented cohort aged over 65 years. For the fasciculi investigated (genu and splenium of corpus callosum, cingulum cingulate gyri, corticospinal tracts and uncinate fasciculi), PNT was able to provide anatomically plausible representations of the tract in question in 70 to 90% of subjects compared with 2.5 to 60% if single seed points were simply transferred directly from standard to native space. In corpus callosum genu there was a significant negative correlation between a PNT-derived measure of tract shape similarity to a young brain reference tract and age, and a trend towards a significant negative correlation between tract-averaged fractional anisotropy and age; results that are consistent with previous dMRI studies of normal ageing. These data show that it is possible automatically to segment comparable tracts in the brains of older subjects using single seed point tractography, if the seed point is carefully chosen.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4759
Deposited By:Amos Storkey
Deposited On:24 March 2009