| Title |
A review of structural and functional brain networks: small world and atlas
|
|---|---|
| Published in |
Brain Informatics, February 2015
|
| DOI | 10.1007/s40708-015-0009-z |
| Pubmed ID | |
| Authors |
Zhijun Yao, Bin Hu, Yuanwei Xie, Philip Moore, Jiaxiang Zheng |
| Abstract |
Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases. |
Mendeley readers
The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | <1% |
| Netherlands | 1 | <1% |
| United States | 1 | <1% |
| Poland | 1 | <1% |
| Unknown | 135 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 21% |
| Student > Master | 26 | 19% |
| Researcher | 24 | 17% |
| Student > Bachelor | 15 | 11% |
| Professor > Associate Professor | 7 | 5% |
| Other | 15 | 11% |
| Unknown | 23 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 27 | 19% |
| Engineering | 20 | 14% |
| Psychology | 16 | 12% |
| Computer Science | 14 | 10% |
| Medicine and Dentistry | 8 | 6% |
| Other | 28 | 20% |
| Unknown | 26 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2015.
All research outputs
#20,283,046
of 22,817,213 outputs
Outputs from Brain Informatics
#93
of 103 outputs
Outputs of similar age
#302,972
of 359,588 outputs
Outputs of similar age from Brain Informatics
#1
of 1 outputs
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So far Altmetric has tracked 103 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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