Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference

Resting State Brain Network Modeling Based On Functional Magnetic Resonance Imaging

Authors
Ming Ke, Zhijing Li, Zhao Cao, Xiaoping Yang
Corresponding Author
Ming Ke
Available Online December 2015.
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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Volume Title
Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference
Series
Advances in Computer Science Research
Publication Date
December 2015
ISBN
978-94-6252-129-2
ISSN
2352-538X
DOI
10.2991/jimet-15.2015.72How to use a DOI?
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/).

Cite this article

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  -