Covariance Shrinkage for Dynamic Functional Connectivity

  • Nicolas HonnoratEmail author
  • Ehsan Adeli
  • Qingyu Zhao
  • Adolf Pfefferbaum
  • Edith V. Sullivan
  • Kilian Pohl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11848)


The tracking of dynamic functional connectivity (dFC) states in resting-state fMRI scans aims to reveal how the brain sequentially processes stimuli and thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by linear covariance shrinkage, a statistical method used for the estimation of large covariance matrices from small number of samples. We present a computationally efficient formulation of our approach that scales dFC analysis up to full resolution resting-state fMRI scans. Experiments on synthetic data demonstrate that our approach produces dFC estimates that are closer to the ground-truth than state-of-the-art estimation approaches. When comparing methods on the rs-fMRI scans of 162 subjects, we found that our approach is better at extracting functional networks and capturing differences in rs-fMRI acquisition and diagnosis.



This research work was funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) under the grants AA005965, AA013521, AA010723, and AA026762.


  1. 1.
    Biswal, B., Zerrin Yetkin, F., Haughton, V., Hyde, J.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (1995)CrossRefGoogle Scholar
  2. 2.
    Chang, C., Glover, G.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50(1), 81–98 (2010)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Wiesel, A., Eldar, Y.C., Hero, A.O.: Shrinkage algorithms for MMSE covariance estimation. IEEE Trans. Signal Process. 58(10), 5016–5029 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Hofmann, T., Schölkopf, B., Smola, A.: Kernel methods in machine learning. Ann. Stat. 36(3), 1171–1220 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ledoit, W.: Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empirical Finan. 10(5), 603–621 (2003)CrossRefGoogle Scholar
  6. 6.
    Lindquist, M., Xu, Y., Nebel, M., Caffo, B.: Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. NeuroImage 101, 531–546 (2014)CrossRefGoogle Scholar
  7. 7.
    Liu, X., Zhang, N., Chang, C., Duyn, J.: Co-activation patterns in resting-state fMRI signals. NeuroImage 180(Part B), 485–494 (2018)CrossRefGoogle Scholar
  8. 8.
    Pfefferbaum, A., et al.: Accelerated aging of selective brain structures in human immunodeficiency virus infection. Neurobiol. Aging 35(7), 1755–1768 (2014)CrossRefGoogle Scholar
  9. 9.
    Preti, M., Bolton, T., Van De Ville, D.: The dynamic functional connectome: state-of-the-art and perspectives. NeuroImage 160, 41–54 (2017)CrossRefGoogle Scholar
  10. 10.
    Rohlfing, T., Zahr, N., Sullivan, E., Pfefferbaum, A.: The SRI24 multichannel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2014)CrossRefGoogle Scholar
  11. 11.
    Xie, H., et al.: Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information. NeuroImage 188, 502–514 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicolas Honnorat
    • 1
    Email author
  • Ehsan Adeli
    • 2
  • Qingyu Zhao
    • 2
  • Adolf Pfefferbaum
    • 1
    • 2
  • Edith V. Sullivan
    • 2
  • Kilian Pohl
    • 1
    • 2
  1. 1.SRI InternationalMenlo ParkUSA
  2. 2.Department of Psychiatry and Behavioral SciencesStanford UniversityPalo AltoUSA

Personalised recommendations