On the non-uniform complexity of brain connectivity

A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging (HARDI) data is introduced in this paper. HARDI data provides highdimensional 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. Key words: Stratification and manifold learning, DTI, HARDI, complexity, white matter connectivity.

• Type: Conference
• Year: 2008
• Where: ISBI
• Authors: Gloria Haro, Christophe Lenglet, Guillermo Sapiro, Paul M. Thompson
• Keywords: Cerebral White Matter, ISBI 2008, Manifold Learning Approach, Medical Imaging, White Matter