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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Efficient Computation of Functional Brain Networks: toward Real-Time Functional Connectivity
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, February 2017
|
| DOI | 10.3389/fninf.2017.00008 |
| Pubmed ID | |
| Authors |
Juan García-Prieto, Ricardo Bajo, Ernesto Pereda |
| Abstract |
Functional Connectivity has demonstrated to be a key concept for unraveling how the brain balances functional segregation and integration properties while processing information. This work presents a set of open-source tools that significantly increase computational efficiency of some well-known connectivity indices and Graph-Theory measures. PLV, PLI, ImC, and wPLI as Phase Synchronization measures, Mutual Information as an information theory based measure, and Generalized Synchronization indices are computed much more efficiently than prior open-source available implementations. Furthermore, network theory related measures like Strength, Shortest Path Length, Clustering Coefficient, and Betweenness Centrality are also implemented showing computational times up to thousands of times faster than most well-known implementations. Altogether, this work significantly expands what can be computed in feasible times, even enabling whole-head real-time network analysis of brain function. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 94 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 31 | 32% |
| Researcher | 16 | 17% |
| Student > Doctoral Student | 10 | 10% |
| Student > Master | 8 | 8% |
| Lecturer | 5 | 5% |
| Other | 16 | 17% |
| Unknown | 10 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 23 | 24% |
| Engineering | 20 | 21% |
| Computer Science | 10 | 10% |
| Medicine and Dentistry | 7 | 7% |
| Psychology | 7 | 7% |
| Other | 11 | 11% |
| Unknown | 18 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 23 February 2017.
All research outputs
#14,324,882
of 22,947,506 outputs
Outputs from Frontiers in Neuroinformatics
#485
of 751 outputs
Outputs of similar age
#229,129
of 420,371 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#15
of 19 outputs
Altmetric has tracked 22,947,506 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 751 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 31st percentile – i.e., 31% 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 420,371 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.