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

  • Lei Nie
  • Xian Yang
  • Paul M. Matthews
  • Zhiwei Xu
  • Yike GuoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)


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.


fMRI Functional connectivity Partial correlation PC-algorithm 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lei Nie
    • 1
    • 2
  • Xian Yang
    • 1
  • Paul M. Matthews
    • 3
  • Zhiwei Xu
    • 2
  • Yike Guo
    • 1
    • 4
    Email author
  1. 1.Department of ComputingImperial College LondonLondonUK
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.Department of MedicineImperial College LondonLondonUK
  4. 4.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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