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

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2007
Accession Number
ADA478579

Entities

People

  • Christophe Lenglet
  • Gloria Haro
  • Guillermo Sapiro
  • Paul M. Thompson

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Brain
  • Clustering
  • Computations
  • Diffusion
  • Estimators
  • Geometry
  • Intensity
  • Learning
  • Microstructure
  • Point Clouds
  • Probability
  • Probability Density Functions
  • Statistical Algorithms
  • Statistical Analysis
  • Stratification

Readers

  • Distributed Systems and Data Platform Development
  • Medical Imaging.
  • Theoretical Analysis.

Technology Areas

  • Space