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Brain Entropy Mapping Using fMRI

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  • Ze Wang
  • Yin Li
  • Anna Rose Childress
  • John A Detre

Abstract

Entropy is an important trait for life as well as the human brain. Characterizing brain entropy (BEN) may provide an informative tool to assess brain states and brain functions. Yet little is known about the distribution and regional organization of BEN in normal brain. The purpose of this study was to examine the whole brain entropy patterns using a large cohort of normal subjects. A series of experiments were first performed to validate an approximate entropy measure regarding its sensitivity, specificity, and reliability using synthetic data and fMRI data. Resting state fMRI data from a large cohort of normal subjects (n = 1049) from multi-sites were then used to derive a 3-dimensional BEN map, showing a sharp low-high entropy contrast between the neocortex and the rest of brain. The spatial heterogeneity of resting BEN was further studied using a data-driven clustering method, and the entire brain was found to be organized into 7 hierarchical regional BEN networks that are consistent with known structural and functional brain parcellations. These findings suggest BEN mapping as a physiologically and functionally meaningful measure for studying brain functions.

Suggested Citation

  • Ze Wang & Yin Li & Anna Rose Childress & John A Detre, 2014. "Brain Entropy Mapping Using fMRI," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-8, March.
    • Handle: RePEc:plo:pone00:0089948
    DOI: 10.1371/journal.pone.0089948
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    References listed on IDEAS

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    1. Singer, Wolf, 2009. "The Brain, a Complex Self-organizing System," European Review, Cambridge University Press, vol. 17(2), pages 321-329, May.
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    Cited by:

    1. Yan Zhang & Jiali Liang & Qiang Lin & Zhenghui Hu, 2016. "Exploiting Complexity Information for Brain Activation Detection," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-9, April.
    2. Glenn N Saxe & Daniel Calderone & Leah J Morales, 2018. "Brain entropy and human intelligence: A resting-state fMRI study," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.

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    1. Yan Zhang & Jiali Liang & Qiang Lin & Zhenghui Hu, 2016. "Exploiting Complexity Information for Brain Activation Detection," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-9, April.

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