Exploiting Complexity Information for Brain Activation Detection

Yan Zhang; Jiali Liang; Qiang Lin; Zhenghui Hu

doi:10.1371/journal.pone.0152418

Exploiting Complexity Information for Brain Activation Detection

PLoS One. 2016 Apr 5;11(4):e0152418. doi: 10.1371/journal.pone.0152418. eCollection 2016.

Authors

Yan Zhang¹, Jiali Liang¹, Qiang Lin², Zhenghui Hu²

Affiliations

¹ College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
² Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Hangzhou 310023, China.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Algorithms
Brain / physiology*
Entropy
Female
Humans
Magnetic Resonance Imaging / methods*
Male

Grants and funding

This work is supported in part by the National Basic Research Program of China under Grant 2013CB329501, in part by the National High Technology Research and Development Program of China under Grant 2012AA011600, in part by the National Natural Science Foundation of China under Grant 81271645, in part by the Public Projects of Science Technology Department of Zhejiang Province under Grant 2013C33162, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY 12H18004, and in part by the Public Projects of Science Technology Department of Zhejiang Province under Grant 2015C31102.