Resting State Brain Network Modeling Based On Functional Magnetic Resonance Imaging

DOI

10.2991/jimet-15.2015.72How to use a DOI?

Keywords

Complex network, Partial least squares, Pearson correlation, Functional magnetic resonance imaging

Abstract

In this paper, the functional magnetic resonance imaging (fMRI) technique and complex network method were used to study the brain functional network of normal subjects. We used the partial least squares (PLS) regression modeling method to construct the normal human brain function network. The global statistical properties of the brain network revealed the brain functional network had small-world effect. Through the evaluation of centrality indices, the gyri callosus, the supramarginal gyrus gyri frontalis superior and the gyrus angularis were the key areas of the brain functional network in resting state. The result showed that compared with the Pearson correlation analysis method, the PLS algorithm was better to construct the brain network model. It is not only expressed in the brain network threshold is generally high, the "small world" attribute is more obvious, but also the key brain regions that were inferred are more accurate and more consistent with physiological results.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ming Ke
AU  - Zhijing Li
AU  - Zhao Cao
AU  - Xiaoping Yang
PY  - 2015/12
DA  - 2015/12
TI  - Resting State Brain Network Modeling Based On Functional Magnetic Resonance Imaging
BT  - Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
PB  - Atlantis Press
SP  - 389
EP  - 392
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimet-15.2015.72
DO  - 10.2991/jimet-15.2015.72
ID  - Ke2015/12
ER  -