The Identification of Default Mode Network in Rhesus Macaque Using Resting-State fMRI
- DOI
- 10.2991/bst-17.2018.13How to use a DOI?
- Keywords
- fMRI, Resting-State, Rhesus macaques, Default mode network, Independent component analysis.
- Abstract
To investigate Default Mode Network (DMN) in healthy rhesus monkey brain using Resting-State functional Magnetic Resonance Imaging (RS-fMRI). Under anesthesia, two healthy rhesus macaques underwent RS-fMRI at 7.0T using equal imaging parameters. The functional images were first spatially normalized to the standard rhesus monkey template 112SM-RL-T1, and the GIFT software was utilized to carry out group-level Independent Component Analysis (group-ICA) on all preprocessed functional images. The results demonstrated that our method can obtain functional connectivity maps of the resting-state networks. Among them, the DMN bilaterally includes posterior cingulate cortex, anterior cingulate cortex, medial parietal cortex, retrosplenial cortex, arcuate sulcus, ventral intraparietal area, temporo-parietal area, superior temporal sulcus dorsal bank and a portion of visual cortex. With the help of cutting-edge 7.0T fMRI technology, our result confirms that the DMN of the monkey brain highly resembles the ones in human; it supports the notion that non-primates are useful models for neuropharmacological and neurocognitive studies.
- Copyright
- © 2018, 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 - Xiao-Wei Fu AU - Cheng-Zhen Guo AU - Peng-Cheng Li AU - Dan-Zhou Yang AU - Ying Zhang PY - 2018/02 DA - 2018/02 TI - The Identification of Default Mode Network in Rhesus Macaque Using Resting-State fMRI BT - Proceedings of the 2017 2nd International Conference on Biological Sciences and Technology (BST 2017) PB - Atlantis Press SP - 80 EP - 86 SN - 2468-5747 UR - https://doi.org/10.2991/bst-17.2018.13 DO - 10.2991/bst-17.2018.13 ID - Fu2018/02 ER -