Advertisement

Mapping the Spatio-Temporal Functional Coherence in the Resting Brain

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)

Abstract

Human brain functions are underlined by spatially and temporally coherent activity. Characterizing the spatio-temporal coherence (STC) of brain activity is then important to understand brain function, which however is still elusive in the literature. In this study, we proposed a new method to measure the spatio-temporal incoherence (STIC) by segmenting the fMRI time series of neighboring voxels into a series of continuous 4-dimensional elements and recording the similarity of every element to all others. STIC was then calculated as the log differential of the similarity sum of all elements and that when the time window is increased by 1 – a process similar to and directly extended from the time-embedding based approximate entropy calculation. Experiment results showed that STIC revealed the correct irregularity difference between random noise and spatio-temporally coherent signal. STIC was less sensitive to noise than a multi-variate entropy measure. When applied to 917 young health subjects’ resting-state fMRI, we identified highly replicable STIC maps with very fine cortical structures. Females and more matured brain (older here) had higher STIC. Higher STIC in putamen showed a trend of correlations with better mental examination outcome. These data showed STIC as a potential functional brain marker.

Keywords

Spatio-temporal coherence Long-range coherence Brain entropy mapping 

Notes

Acknowledgement

HCP data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

References

  1. 1.
    Biswal, B., et al.: 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.
    Zang, Y., et al.: Regional homogeneity approach to fMRI data analysis. Neuroimage 22(1), 394–400 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    McKeown, M.J., Sejnowski, T.J.: Independent component analysis of fMRI data: examining the assumptions. Hum. Brain Mapp. 6(5–6), 368–372 (1998)CrossRefGoogle Scholar
  4. 4.
    Hyvärinen, A.: New approximations of differential entropy for independent component analysis and projection pursuit. In: Jordan, M.I., Kearns, M.J., Solla, S.A. (eds.) Advances in Neural Information Processing Systems, pp. 273–279. MIT Press, Cambridge (1998)Google Scholar
  5. 5.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Hear. Circ. Physiol. 278(6), H2039–H2049 (2000)Google Scholar
  6. 6.
    Wang, Z.: Characterizing resting brain information using voxel-based brain information mapping (BIM). In: 2012 Annual Meeting of the Organization for Human Brain Mapping, Beijing, China (2012)Google Scholar
  7. 7.
    Ze Wang, A.M., Raichle, M., Childress, A.R., Detre, J.A.: Mapping brain entropy using resting state fMRI. In: 2013 Annual Meeting of International Society of Magnetic Resonance in Medicine, Salt Lake City, USA, p. 4861 (2013)Google Scholar
  8. 8.
    Schütze, H., Martinetz, T., Anders, S., Madany Mamlouk, A.: A multivariate approach to estimate complexity of FMRI time series. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012. LNCS, vol. 7553, pp. 540–547. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33266-1_67CrossRefGoogle Scholar
  9. 9.
    Ahmed, M.U., et al.: Multivariate multiscale entropy for brain consciousness analysis. In: Conference Proceedings of the IEEE Engineering in Medicine and Biology Society 2011, pp. 810–813 (2011)Google Scholar
  10. 10.
    Van Essen, D.C., et al.: The WU-minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)CrossRefGoogle Scholar
  11. 11.
    Moeller, S., et al.: 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. Magn. Reson. Med. 63(5), 1144–1153 (2010)CrossRefGoogle Scholar
  12. 12.
    Shrout, P., Fleiss, J.: Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86(2), 420–428 (1979)CrossRefGoogle Scholar
  13. 13.
    Ingalhalikar, M., et al.: Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. U. S. A. 111(2), 823–828 (2014)CrossRefGoogle Scholar
  14. 14.
    Gur, R.E., Gur, R.C.: Sex differences in brain and behavior in adolescence: findings from the Philadelphia Neurodevelopmental Cohort. Neurosci. Biobehav. Rev. 70, 159–170 (2016)CrossRefGoogle Scholar
  15. 15.
    Donghui Song, D.C., Zhang, J., Ge, Q., Zang, Y.-F., Wang, Z.: Associations of brain entropy (BEN) to cerebral blood flow and fractional amplitude of low-frequency fluctuations in the resting brain. Brain Imaging Behav. 13, 1486–1495 (2019)CrossRefGoogle Scholar
  16. 16.
    Nichols, T.E., Hayasaka, S.: Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat. Methods Med. Res. 12, 419–446 (2003)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Folstein, M.F., Folstein, S.E., McHugh, P.R.: “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12(3), 189–198 (1975)CrossRefGoogle Scholar
  18. 18.
    Li, Z., et al.: Hyper-resting brain entropy within chronic smokers and its moderation by Sex. Sci. Rep. 6, 29435 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreUSA

Personalised recommendations