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Local Dimension-Reduced Dynamical Spatio-Temporal Models for Resting State Network Estimation

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Book cover Brain Informatics and Health (BIH 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8609))

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Abstract

Resting-state Functional Magnetic Resonance Imaging (FMRI) analysis has consistently shown the presence of specific spatial activation patterns. Independent component analysis (ICA) has been the analysis algorithm of choice even though its underlying assumptions preclude deeper connectivity analysis. By combining novel concepts of group sparsity with contiguity-constrained clusterization, we developed a new class of Local dimension-reduced Dynamical Spatio-Temporal Models (LDSTM) for estimating whole-brain dynamical models whereby the causal relationships between well localized spatial components can be identified. Experimental results of LDSTM on group resting-state FMRI data reveal physiologically plausible spatio-temporal brain connectivity patterns among participants.

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Author information

Authors and Affiliations

  1. Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, SP, 05508-050, Brazil

    Gilson Vieira & Edson Amaro Jr.

  2. Escola Politécnica, University of São Paulo, São Paulo, SP, 05508-900, Brazil

    Luiz A. Baccalá

Authors
  1. Gilson Vieira
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  2. Edson Amaro Jr.
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  3. Luiz A. Baccalá
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślȩzak

  2. School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore

    Ah-Hwee Tan

  3. Computational Intelligence Laboratory, ECE Department, University of Manitoba, R3T 5V6, Winnipeg, MB, Canada

    James F. Peters

  4. Institute of Computer Science, University of Rostock, Rostock, Germany

    Lars Schwabe

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© 2014 Springer International Publishing Switzerland

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Vieira, G., Amaro, E., Baccalá, L.A. (2014). Local Dimension-Reduced Dynamical Spatio-Temporal Models for Resting State Network Estimation. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-09891-3_40

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09890-6

  • Online ISBN: 978-3-319-09891-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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