title: Analysis of fMRI time-series by entropy measures.
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 datasets 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.
authors.affiliation: Prague Psychiatric Centre, 3rd Faculty of Medicine, Charles University, Prague, Czech Republic. mikolas@pcp.lf3.cuni.cz
authors.forename: Pavol
authors.initials: P
authors.lastname: Mikoláš
authors.forename: Jan
authors.initials: J
authors.lastname: Vyhnánek
authors.forename: Antonín
authors.initials: A
authors.lastname: Škoch
authors.forename: Jiří
authors.initials: J
authors.lastname: Horáček
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D019277:Entropy; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D059629:Signal-To-Noise Ratio