On the non-uniform complexity of brain connectivity

Gloria Haro, Christophe Lenglet, Guillermo Sapiro, Paul Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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 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.

Original languageEnglish (US)
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages887-890
Number of pages4
DOIs
StatePublished - Sep 10 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: May 14 2008May 17 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Other

Other2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryFrance
CityParis
Period5/14/085/17/08

Keywords

  • Clustering methods
  • Density measurement
  • Point processes
  • Poisson processes
  • Unsupervised learning

Fingerprint Dive into the research topics of 'On the non-uniform complexity of brain connectivity'. Together they form a unique fingerprint.

Cite this