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Author Notes:

maysam.nezafati@bme.gatech.edu; shella.keilholz@bme.gatech.edu

HT and MN performed the data preprocessing and analysis. SK and MN performed the results interpretation, comparison of obtained results with existing materials, preparation of document. All authors contributed to the article and approved the submitted version.

A sincere thanks to Behnaz Yousefi, Xiaodi Zhang, Anzar Abbas, Eric Maltbie and Wenju Pan for participating in lively discussions regarding this work.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Subjects:

Research Funding:

This work was supported by the National Science Foundation BCS INSPIRE 1533260, National Institutes of Health R01NS078095 and 1R01MH111416-01. 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.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neurosciences & Neurology
  • functional MRI
  • complexity
  • entropy
  • temporal analysis
  • resting state
  • computational neuroscience
  • neuro imaging
  • APPROXIMATE ENTROPY
  • BRAIN
  • CONNECTIVITY
  • OPTIMIZATION
  • REGISTRATION
  • RELIABILITY
  • ANATOMY
  • ROBUST
  • SERIES
  • FMRI

Functional MRI Signal Complexity Analysis Using Sample Entropy

Tools:

Journal Title:

FRONTIERS IN NEUROSCIENCE

Volume:

Volume 14

Publisher:

, Pages 700-700

Type of Work:

Article | Final Publisher PDF

Abstract:

Resting-state functional magnetic resonance imaging (rs-fMRI) is an immensely powerful method in neuroscience that uses the blood oxygenation level-dependent (BOLD) signal to record and analyze neural activity in the brain. We examined the complexity of brain activity acquired by rs-fMRI to determine whether it exhibits variation across brain regions. In this study the complexity of regional brain activity was analyzed by calculating the sample entropy of 200 whole-brain BOLD volumes as well as of distinct brain networks, cortical regions, and subcortical regions of these brain volumes. It can be seen that different brain regions and networks exhibit distinctly different levels of entropy/complexity, and that entropy in the brain significantly differs between brains at rest and during task performance.

Copyright information:

© 2020 Nezafati, Temmar and Keilholz.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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