Abstract
We present a complexity-based approach for the analysis of fMRI time series, in which sample entropy (SampEn) is introduced as a quantification of the voxel complexity. Under this hypothesis the voxel complexity could be modulated in pertinent cognitive tasks, and it changes through experimental paradigms. We calculate the complexity of sequential fMRI data for each voxel in two distinct experimental paradigms and use a nonparametric statistical strategy, the Wilcoxon signed rank test, to evaluate the difference in complexity between them. The results are compared with the well known general linear model based Statistical Parametric Mapping package (SPM12), where a decided difference has been observed. This is because SampEn method detects brain complexity changes in two experiments of different conditions and the data-driven method SampEn evaluates just the complexity of specific sequential fMRI data. Also, the larger and smaller SampEn values correspond to different meanings, and the neutral-blank design produces higher predictability than threat-neutral. Complexity information can be considered as a complementary method to the existing fMRI analysis strategies, and it may help improving the understanding of human brain functions from a different perspective.
Highlights
While one surveys fMRI signals, a detail must not be ignored that these signals, like many other physiological time series, commonly exhibit extremely inhomogeneous and non-stationary fluctuations in an irregular and complex manner [4, 5]
We hypothesize that these complexity/regularity could be modulated in pertinent cognitive tasks, and they may PLOS ONE | DOI:10.1371/journal.pone
Participants underwent scanning while listening passively to (i): emotionally neutral word alternating with no word as the control condition, and (ii): threat-related words alternating with emotionally neutral word as the experimental condition
Summary
While one surveys fMRI signals, a detail must not be ignored that these signals, like many other physiological time series, commonly exhibit extremely inhomogeneous and non-stationary fluctuations in an irregular and complex manner [4, 5]. Since most fMRI data are recorded for 100 or so temporal volumes, ApEn and especially SampEn are especially attractive for fMRI time series complexity assessment [14, 15]. SampEn, which provides a mathematical quantification of regularity, was applied to voxel-based analysis of fMRI sequences from two block design dataset.
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