Functional MRI Signal Complexity Analysis Using Sample Entropy

Maysam Nezafati; Hisham Temmar; Shella D Keilholz

doi:10.3389/fnins.2020.00700

Functional MRI Signal Complexity Analysis Using Sample Entropy

Front Neurosci. 2020 Jul 2:14:700. doi: 10.3389/fnins.2020.00700. eCollection 2020.

Authors

Maysam Nezafati¹, Hisham Temmar¹, Shella D Keilholz^{1

2}

Affiliations

¹ Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.
² Neuroscience Program, Graduate Division of Biological and Biomedical Sciences, Laney Graduate School, Emory University, Atlanta, GA, United States.

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.

Keywords: complexity; computational neuroscience; entropy; functional MRI; neuro imaging; resting state; temporal analysis.

Abstract

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