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

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Medical Imaging and Augmented Reality (MIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4091))

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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.

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Authors and Affiliations

  1. WHC Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Hospital Annex, 531 Asbury Circle, Suite 305, Atlanta, GA, 30322, USA

    Gopikrishna Deshpande, Stephen LaConte, Scott Peltier & Xiaoping Hu

Authors
  1. Gopikrishna Deshpande
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  2. Stephen LaConte
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  3. Scott Peltier
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  4. Xiaoping Hu
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Editor information

Editors and Affiliations

  1. Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom

    Guang-Zhong Yang

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100080, Beijing, People’s Republic of China

    TianZi Jiang

  3. Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, 27510, Chapel Hill, NC

    Dinggang Shen

  4. Computer Science, Shanghai Jiaotong University, 800 Dongchuan Road, 200240, Shanghai, China

    Lixu Gu

  5. Institute of IP & PR, Shanghai Jiao Tong Univ., P.R. China

    Jie Yang

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© 2006 Springer-Verlag Berlin Heidelberg

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Deshpande, G., LaConte, S., Peltier, S., Hu, X. (2006). Connectivity Analysis of Human Functional MRI Data: From Linear to Nonlinear and Static to Dynamic. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_3

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  • DOI: https://doi.org/10.1007/11812715_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37220-2

  • Online ISBN: 978-3-540-37221-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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