Brain Imaging and Behavior

, Volume 8, Issue 2, pp 284–291 | Cite as

Multiple time scale complexity analysis of resting state FMRI

  • Robert X. SmithEmail author
  • Lirong Yan
  • Danny J. J. Wang
SI: Genetic Neuroimaging in Aging and Age-Related Diseases


The present study explored multi-scale entropy (MSE) analysis to investigate the entropy of resting state fMRI signals across multiple time scales. MSE analysis was developed to distinguish random noise from complex signals since the entropy of the former decreases with longer time scales while the latter signal maintains its entropy due to a “self-resemblance” across time scales. A long resting state BOLD fMRI (rs-fMRI) scan with 1000 data points was performed on five healthy young volunteers to investigate the spatial and temporal characteristics of entropy across multiple time scales. A shorter rs-fMRI scan with 240 data points was performed on a cohort of subjects consisting of healthy young (age 23 ± 2 years, n = 8) and aged volunteers (age 66 ± 3 years, n = 8) to investigate the effect of healthy aging on the entropy of rs-fMRI. The results showed that MSE of gray matter, rather than white matter, resembles closely that of f −1 noise over multiple time scales. By filtering out high frequency random fluctuations, MSE analysis is able to reveal enhanced contrast in entropy between gray and white matter, as well as between age groups at longer time scales. Our data support the use of MSE analysis as a validation metric for quantifying the complexity of rs-fMRI signals.


Complexity Self-resemblance Fractal Multi-scale entropy MSE Sample entropy Resting state fMRI Aging 



This study was supported by US National Institutes of Health grants R01-MH080892, R01-NS081077, R01-EB014922 and California Department of Public Health Grant Agreement No. 13-12008. The software used in this paper can be downloaded at:

Disclosure statement

The authors have no conflicts of interest to disclose.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Robert X. Smith
    • 1
    Email author
  • Lirong Yan
    • 1
  • Danny J. J. Wang
    • 1
  1. 1.Laboratory of Functional MRI Technology (LOFT) Department of NeurologyUCLALos AngelesUSA

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