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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Spatiotemporal Analysis of Developing Brain Networks
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, July 2018
|
| DOI | 10.3389/fninf.2018.00048 |
| Pubmed ID | |
| Authors |
Ping He, Xiaohua Xu, Han Zhang, Gang Li, Jingxin Nie, Pew-Thian Yap, Dinggang Shen |
| Abstract |
Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3-20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3-6, 7-12, and 13-20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 20% |
| Brazil | 1 | 20% |
| Switzerland | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 45% |
| Researcher | 4 | 36% |
| Student > Bachelor | 1 | 9% |
| Professor | 1 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 2 | 18% |
| Neuroscience | 2 | 18% |
| Business, Management and Accounting | 1 | 9% |
| Agricultural and Biological Sciences | 1 | 9% |
| Social Sciences | 1 | 9% |
| Other | 3 | 27% |
| Unknown | 1 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 11 August 2018.
All research outputs
#13,546,560
of 23,096,849 outputs
Outputs from Frontiers in Neuroinformatics
#431
of 757 outputs
Outputs of similar age
#167,595
of 329,832 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#17
of 23 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 757 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,832 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.