Functional MRI Signal Complexity Analysis Using Sample Entropy

• Type of Material: Article
• Authors: Maysam Nezafati, Emory University; Hisham Temmar, Emory University; Shella Keilholz, Emory University
• Language: English
• Date: 2020-07-02
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2020 Nezafati, Temmar and Keilholz.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fnins.2020.00700
• Title of Journal or Parent Work: FRONTIERS IN NEUROSCIENCE
• Volume: 14
• Start Page: 700
• End Page: 700
• Grant/Funding Information: 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: APPROXIMATE ENTROPY; Neurosciences; CONNECTIVITY; complexity; functional MRI; neuro imaging; REGISTRATION; SERIES; ROBUST; resting state; Science & Technology; BRAIN; temporal analysis; RELIABILITY; Life Sciences & Biomedicine; OPTIMIZATION; Neurosciences & Neurology; ANATOMY; computational neuroscience; entropy; FMRI
• Research Categories: Biology, Neuroscience; Health Sciences, Radiology; Biology, Anatomy

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.