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Connectivity Analysis of Human Functional MRI Data: From Linear to Nonlinear and Static to Dynamic

  • Gopikrishna Deshpande
  • Stephen LaConte
  • Scott Peltier
  • Xiaoping Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)

Abstract

In this paper, we describe approaches for analyzing functional MRI data to assess brain connectivity. Using phase-space embedding, bivariate embedding dimensions and delta-epsilon methods are introduced to characterize nonlinear connectivity in fMRI data. The nonlinear approaches were applied to resting state data and continuous task data and their results were compared with those obtained from the conventional approach of linear correlation. The nonlinear methods captured couplings not revealed by linear correlation and was found to be more selective in identifying true connectivity. In addition to the nonlinear methods, the concept of Granger causality was applied to infer directional information transfer among the connected brain regions. Finally, we demonstrate the utility of moving window connectivity analysis in understanding temporally evolving neural processes such as motor learning.

Keywords

Functional Magnetic Resonance Imaging Nonlinear Dynamics Connectivity Analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gopikrishna Deshpande
    • 1
  • Stephen LaConte
    • 1
  • Scott Peltier
    • 1
  • Xiaoping Hu
    • 1
  1. 1.WHC Department of Biomedical EngineeringGeorgia Institute of Technology and Emory University, Hospital AnnexAtlantaUSA

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