| Title |
Behavioral and Health Correlates of Resting-State Metastability in the Human Connectome Project
|
|---|---|
| Published in |
Brain Topography, August 2018
|
| DOI | 10.1007/s10548-018-0672-5 |
| Pubmed ID | |
| Authors |
Won Hee Lee, Dominik Andreas Moser, Alex Ing, Gaelle Eve Doucet, Sophia Frangou |
| Abstract |
Metastability is currently considered a fundamental property of the functional configuration of brain networks. The present study sought to generate a normative reference framework for the metastability of the major resting-state networks (RSNs) (resting-state metastability dataset) and discover their association with demographic, behavioral, physical and cognitive features (non-imaging dataset) from 818 participants of the Human Connectome Project. Using sparse canonical correlation analysis, we found that the metastability and non-imaging datasets showed significant but modest interdependency. Notable associations between the metastability variate and the non-imaging features were observed for higher-order cognitive ability and indicators of physical well-being. The intra-class correlation coefficient between the sibling pairs in the sample was very low which argues against a significant familial influence on RSN metastability. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 33% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 33 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 3 | 9% |
| Student > Master | 3 | 9% |
| Lecturer | 2 | 6% |
| Student > Bachelor | 2 | 6% |
| Other | 8 | 24% |
| Unknown | 10 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 7 | 21% |
| Psychology | 5 | 15% |
| Business, Management and Accounting | 2 | 6% |
| Nursing and Health Professions | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 4 | 12% |
| Unknown | 13 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 19 October 2018.
All research outputs
#6,639,209
of 24,319,828 outputs
Outputs from Brain Topography
#116
of 506 outputs
Outputs of similar age
#110,041
of 337,569 outputs
Outputs of similar age from Brain Topography
#5
of 13 outputs
Altmetric has tracked 24,319,828 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 506 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done well, scoring higher than 77% of its peers.
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 337,569 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.