Accession Number:

ADA478579

Title:

On the Non-Uniform Complexity of Brain Connectivity (PREPRINT)

Descriptive Note:

Corporate Author:

MINNESOTA UNIV MINNEAPOLIS INST FOR MATHEMATICS AND ITS APPLICATIONS

Report Date:

2007-12-01

Pagination or Media Count:

6.0

Abstract:

A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging HARDI data is introduced in this paper. HARDI data provides high-dimensional signals measuring the complex microstructure of biological tissues, such as the cerebral white matter. We show that these high-dimensional spaces may be understood as unions of manifolds of varying dimensionscomplexity and densities. With such analysis, we use clustering to characterize the structural complexity of the white matter. We briefly present the underlying framework and numerical experiments illustrating this original and promising approach.

Subject Categories:

  • Anatomy and Physiology
  • Numerical Mathematics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE