Resting State Brain Network Modeling Based On Functional Magnetic Resonance Imaging
- 10.2991/jimet-15.2015.72How to use a DOI?
- Complex network, Partial least squares, Pearson correlation, Functional magnetic resonance imaging
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.
- © 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 -