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Fast construction of voxel-level functional connectivity graphs

Overview of attention for article published in BMC Neuroscience, June 2014
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Mentioned by

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2 tweeters

Citations

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5 Dimensions

Readers on

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38 Mendeley
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Title
Fast construction of voxel-level functional connectivity graphs
Published in
BMC Neuroscience, June 2014
DOI 10.1186/1471-2202-15-78
Pubmed ID
Authors

Kristian Loewe, Marcus Grueschow, Christian M Stoppel, Rudolf Kruse, Christian Borgelt

Abstract

Graph-based analysis of fMRI data has recently emerged as a promising approach to study brain networks. Based on the assessment of synchronous fMRI activity at separate brain sites, functional connectivity graphs are constructed and analyzed using graph-theoretical concepts. Most previous studies investigated region-level graphs, which are computationally inexpensive, but bring along the problem of choosing sensible regions and involve blurring of more detailed information. In contrast, voxel-level graphs provide the finest granularity attainable from the data, enabling analyses at superior spatial resolution. They are, however, associated with considerable computational demands, which can render high-resolution analyses infeasible. In response, many existing studies investigating functional connectivity at the voxel-level reduced the computational burden by sacrificing spatial resolution.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Switzerland 1 3%
United States 1 3%
Austria 1 3%
Unknown 34 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 37%
Professor > Associate Professor 6 16%
Student > Ph. D. Student 6 16%
Student > Master 2 5%
Student > Bachelor 2 5%
Other 8 21%
Readers by discipline Count As %
Neuroscience 8 21%
Engineering 7 18%
Unspecified 6 16%
Medicine and Dentistry 5 13%
Computer Science 5 13%
Other 7 18%

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 24 June 2014.
All research outputs
#2,669,260
of 5,036,385 outputs
Outputs from BMC Neuroscience
#373
of 679 outputs
Outputs of similar age
#61,675
of 123,978 outputs
Outputs of similar age from BMC Neuroscience
#19
of 33 outputs
Altmetric has tracked 5,036,385 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 679 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 33rd percentile – i.e., 33% 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 123,978 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.