On the non-uniform complexity of brain connectivity


Journal Article

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 dimensions/complexity 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. ©2008 IEEE.

Full Text

Duke Authors

Cited Authors

  • Haro, G; Lenglet, C; Sapiro, G; Thompson, P

Published Date

  • September 10, 2008

Published In

  • 2008 5th Ieee International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, Isbi

Start / End Page

  • 887 - 890

Digital Object Identifier (DOI)

  • 10.1109/ISBI.2008.4541139

Citation Source

  • Scopus