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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

Abstract

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.

Keywords

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

Notes

Acknowledgments

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: http://www.fil.ion.ucl.ac.uk/spm/ext/#Complexity

Disclosure statement

The authors have no conflicts of interest to disclose.

References

  1. Abasolo, D., Hornero, R., Espino, P., Poza, J., Sanchez, C., de la Rosa, R. (2005). Analysis of regularity in the eeg background activity of alzheimer’s disease patients with approximate entropy. Clinical Neurophysiology, 116, 1826–1834.PubMedCrossRefGoogle Scholar
  2. Biswal, B., Mennes, M., Zuo, X., Gohel, S., Kelly, C., Smith, S. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107, 4734–4739.PubMedCentralPubMedCrossRefGoogle Scholar
  3. Bullmore, E., Fadili, J., Maxim, V., Sendur, L., Whitcher, B., Suckling, J., et al. (2004). Wavelets and functional magnetic resonance imaging of the human brain. NeuroImage, 23.Google Scholar
  4. Chang, C., & Glover, G. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fmri. NeuroImage, 50, 81–98.PubMedCentralPubMedCrossRefGoogle Scholar
  5. Ciuciu, P., Varoquaux, G., Abry, P., Sadaghiani, S., Kleinschmidt, A. (2012). Scale-free and multifractal properties of fmri signals during rest and task. Frontiers in Physiology, 3(186).Google Scholar
  6. Costa, M., Goldberger, A., Peng, C. (2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89.Google Scholar
  7. Craik, F., & Salthouse, T. (2000). The handbook of aging and cognition. Evanston: Routledge.Google Scholar
  8. Damoiseaux, J.S., Beckmann, C.F., Arigita, E.J.S., Barkhof, F., Scheltens, P., Stam, C.J., et al. (2008). Reduced resting-state brain activity in the “default network” in normal aging. Cerebral Cortex, 18(8), 1856–1864.PubMedCrossRefGoogle Scholar
  9. Daselaar, S.M., Fleck, M.S., Dobbins, I.G., Madden, D.J., Cabeza, R. (2006). Effects of healthy aging on hippocampal and rhinal memory functions: an event-related fmri study. Cerebral Cortex, 16(12), 1771–1782.PubMedCentralPubMedCrossRefGoogle Scholar
  10. Feinberg, D., Moeller, S., Smith, S., Auerbach, E., Ramanna, S., Gunther, M., et al. (2010). Multiplexed echo planar imaging for sub-second whole brain fmri and fast diffusion imaging. PLoS One, 5.Google Scholar
  11. Glover, G., Li, T.-Q., Ress, D. (2000). Image-based method for retrospective correction of image-based method for retrospective correction of physiological motion effects in fmri: Retroicor. MRM, 44.Google Scholar
  12. Goldberger, A. (1996). Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet, 347, 1312–1314.PubMedCrossRefGoogle Scholar
  13. Goldberger, A., & West, B. (1987). Fractals in physiology and medicine. The Yale Journal of Biology and Medicine, 60, 421– 435.PubMedCentralPubMedGoogle Scholar
  14. Goldberger, A., Amaral, L., Hausdorff, J., Ivanov, P., Peng, C., Stanley, H. (2002). Fractal dynamics in physiology: alterations with disease and aging. Proceedings of the National Academy of Sciences of the United States of America, 99, 2466–2472.PubMedCentralPubMedCrossRefGoogle Scholar
  15. Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V. (2004). Default-mode network activity distinguishes alzheimer’s disease from healthy aging: evidence from functional mri. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642.PubMedCentralPubMedCrossRefGoogle Scholar
  16. He, B.J. (2011). Scale-free properties of the functional magnetic resonance imaging signal during rest and task. The Journal of Neuroscience, 31(39), 13786–13795.PubMedCentralPubMedCrossRefGoogle Scholar
  17. Kaplan, D., Furman, M., Pincus, S., Ryan, S., Lipsitz, L., Goldberger, A. (1991). Aging and the complexity of cardiovascular dynamics. Biophysical Journal, 59, 945–949.PubMedCentralPubMedCrossRefGoogle Scholar
  18. Lipsitz, L. (2004). Physiological complexity, aging, and the path to frailty. Science of Aging Knowledge Environment, 16.Google Scholar
  19. Liu, C., Krishnan, A., Yan, L., Smith, R., Kilroy, E., Alger, J., et al. (2012). Complexity and synchronicity of resting state blood oxygenation level-dependent (BOLD) functional MRI in normal aging and cognitive decline. Journal of Magnetic Resonance Imaging, 38, 36–45.PubMedCrossRefGoogle Scholar
  20. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A. (2001). Neurophysiological investigation Q5 of the basis of the fmri signal. Nature, 412, 150–157.PubMedCrossRefGoogle Scholar
  21. Mandelbrot, B. (1982). The fractal geometry of nature. San Francisco: Freeman.Google Scholar
  22. Moeller, S., Yacoub, E., Olman, C., Auerbach, E., Strupp, J., Harel, N., et al. (2010). Multiband multislice ge-epi at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fmri. Magnetic Resonance in Medicine, 63, 1144–1153.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Peng, C., Mietus, J., Liu, Y., Lee, C., Hausdorff, J., Stanley, H., et al. (2002). Quantifying fractal dynamics of human respiration: age and gender effects. Annals of Biomedical Engineering, 30, 683–692.PubMedCrossRefGoogle Scholar
  24. Pincus, S. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America, 88, 2297–2301.PubMedCentralPubMedCrossRefGoogle Scholar
  25. Pincus, S. (2006). Approximate entropy as a measure of irregularity for psychiatric serial metrics. Bipolar Disorders, 8, 430–440.PubMedCrossRefGoogle Scholar
  26. Pincus, S., & Keefe, D. (1992). Quantification of hormone pulsatility via an approximate entropy algorithm. The American Journal of Physiology, 262, E741–E754.PubMedGoogle Scholar
  27. Richman, J., & Moorman, J. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, Heart and Circulatory Physiology, 278, H2039–H2049.Google Scholar
  28. Ryan, S., Goldberger, A., Pincus, S., Mietus, J., Lipsitz, L. (1994). Gender- and age-related differences in heart rate dynamics: are women more complex than men? Journal of the American College of Cardiology, 24, 1700–1707.PubMedCrossRefGoogle Scholar
  29. Schuckers, S., & Raphisak, P. (1999). Distinction of arrhythmias with the use of approximate entropy. Computers in Cardiology, 26, 347–350.Google Scholar
  30. Smith, R., Yan, L., Wang, D. (2013). Multiple time scale entropy of resting state fMRI. In Proceedings of the international society of magnetic resonance in medicine, Oral Presentation.Google Scholar
  31. Sokunbi, M., Staff, R., Waiter, G., Ahearn, T., Fox, H., Deary, I., et al. (2011). Inter-individual differences in fMRI entropy measurements in old ag. IEEE Transactions on Bio-medical Engineering, 58, 3206–3214.PubMedCrossRefGoogle Scholar
  32. Wang, J., Aguirre, G., Kimberg, D., Roc, A., Li, L., Detre, J. (2003). Arterial spin labeling perfusion fMRI with very low task frequency. Magnetic Resonance in Medicine, 49, 796–802.PubMedCrossRefGoogle Scholar
  33. Wang, Q., Xu, X., Zhang, M. (2010). Normal aging in the basal ganglia evaluated by eigenvalues of diffusion tensor imaging. American Journal of Neuroradiology, 31(3), 516– 520.PubMedCrossRefGoogle Scholar
  34. Yang, H., Long, X., Yang, Y., Yan, H., Zhu, C., Zhou, X. (2007). Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. NeuroImage, 36, 144–152.PubMedCrossRefGoogle Scholar
  35. Yang, A., Huang, C., Yeh, H., Liu, M., Hong, C., Tu, P., et al. (2013). Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis. Neurobiology of Aging, 34, 428–438.PubMedCrossRefGoogle Scholar
  36. Zarahn, E., Aguirre, G., D’Esposito, M. (1997). Empirical analyses of BOLD fMRI statistics. I. Spatially unsmoothed data collected under null-hypothesis conditions. NeuroImage, 5, 179–197.PubMedCrossRefGoogle Scholar

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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