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Minimum Partial Correlation: An Accurate and Parameter-Free Measure of Functional Connectivity in fMRI

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Brain Informatics and Health (BIH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9250))

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Abstract

Functional connectivity, a data-driven modelling of spontaneous fluctuations in activity in spatially segregated brain regions, has emerged as a promising approach to generate hypotheses and features for prediction. The most widely used method for inferring functional connectivity is full correlation, but it cannot differentiate direct and indirect effects. This disadvantage is often avoided by fully partial correlation, but this method suffers from Berkson’s paradox. Some advanced methods, such as regularised inverse covariance and Bayes nets, have been applied. However, the connectivity inferred by these methods usually depends on crucial parameters. This paper suggests minimum partial correlation as a parameter-free measure of functional connectivity in fMRI. An algorithm, called elastic PC-algorithm, is designed to approximately calculate minimum partial correlation. Our experimental results show that the proposed method is more accurate than full correlation, fully partial correlation, regularised inverse covariance, network deconvolution algorithm and global silencing algorithm in most cases.

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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Lei Nie, Xian Yang & Yike Guo

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Lei Nie & Zhiwei Xu

  3. Department of Medicine, Imperial College London, London, UK

    Paul M. Matthews

  4. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Yike Guo

Authors
  1. Lei Nie
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  2. Xian Yang
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  3. Paul M. Matthews
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  4. Zhiwei Xu
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  5. Yike Guo
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Corresponding author

Correspondence to Yike Guo .

Editor information

Editors and Affiliations

  1. Imperial College London , Data Science Institute, London, United Kingdom

    Yike Guo

  2. Institute of Cognitive Neuroscience, University College London, London, United Kingdom

    Karl Friston

  3. Imperial College London, London, United Kingdom

    Faisal Aldo

  4. Campus Biotech, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland

    Sean Hill

  5. Allen Institute for Brain Science, Seattle, USA

    Hanchuan Peng

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© 2015 Springer International Publishing Switzerland

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Nie, L., Yang, X., Matthews, P.M., Xu, Z., Guo, Y. (2015). Minimum Partial Correlation: An Accurate and Parameter-Free Measure of Functional Connectivity in fMRI. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-23344-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23343-7

  • Online ISBN: 978-3-319-23344-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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