Analysis of fMRI time-series by entropy measures.
Research output: Contribution to journal › Review article › Contributed › peer-review
Contributors
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 language | English |
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Pages (from-to) | 471-476 |
Number of pages | 6 |
Journal | Neuroendocrinology Letters |
Volume | 33 |
Issue number | 5 |
Publication status | Published - 2012 |
Peer-reviewed | Yes |
Externally published | Yes |
External IDs
Scopus | 84869219113 |
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PubMed | 23090262 |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- Approximate entropy, Block design, Brain, Computer simulation, Event related design, Humans, Information entropy, Magnetic resonance imaging, Methods, Mutual information, Neuroimaging, Resting state