Analysis of fMRI time-series by entropy measures.

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Pavol Mikoláš - , Charles University Prague (Author)
  • Jan Vyhnánek - , Charles University Prague (Author)
  • Antonín Škoch - , Institute for Clinical and Experimental Medicine (Author)
  • Jiří Horáček - , Charles University Prague (Author)

Abstract

Entropy is a measure of information content or complexity. Information-theoretic modeling has been successfully used in various biological data analyses including functional magnetic resonance (fMRI). Several studies have tested and evaluated entropy measures on simulated dataseis and real fMRI data. The efficiency of entropy algorithms has been compared to classical methods based on the linear model. Here we explain and summarize entropy algorithms that have been used in fMRI analysis, their advantages over classical methods and their potential use in event-related and block design fMRI.

Details

Original languageEnglish
Pages (from-to)471-476
Number of pages6
JournalNeuroendocrinology Letters
Volume33
Issue number5
Publication statusPublished - 2012
Peer-reviewedYes
Externally publishedYes

External IDs

Scopus 84869219113
PubMed 23090262

Keywords

Sustainable Development Goals

Keywords

  • Approximate entropy, Block design, Brain, Computer simulation, Event related design, Humans, Information entropy, Magnetic resonance imaging, Methods, Mutual information, Neuroimaging, Resting state