You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
The GridCAT: A Toolbox for Automated Analysis of Human Grid Cell Codes in fMRI
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, July 2017
|
| DOI | 10.3389/fninf.2017.00047 |
| Pubmed ID | |
| Authors |
Matthias Stangl, Jonathan Shine, Thomas Wolbers |
| Abstract |
Human functional magnetic resonance imaging (fMRI) studies examining the putative firing of grid cells (i.e., the grid code) suggest that this cellular mechanism supports not only spatial navigation, but also more abstract cognitive processes. Despite increased interest in this research, there remain relatively few human grid code studies, perhaps due to the complex analysis methods, which are not included in standard fMRI analysis packages. To overcome this, we have developed the Matlab-based open-source Grid Code Analysis Toolbox (GridCAT), which performs all analyses, from the estimation and fitting of the grid code in the general linear model (GLM), to the generation of grid code metrics and plots. The GridCAT, therefore, opens up this cutting-edge research area by allowing users to analyze data by means of a simple and user-friendly graphical user interface (GUI). Researchers confident with programming can edit the open-source code and use example scripts accompanying the GridCAT to implement their own analysis pipelines. Here, we review the current literature in the field of fMRI grid code research with particular focus on the different analysis options that have been implemented, which we describe in detail. Key features of the GridCAT are demonstrated via analysis of an example dataset, which is also provided online together with a detailed manual, so that users can replicate the results presented here, and explore the GridCAT's functionality. By making the GridCAT available to the wider neuroscience community, we believe that it will prove invaluable in elucidating the role of grid codes in higher-order cognitive processes. |
X Demographics
The data shown below were collected from the profiles of 35 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 8 | 23% |
| United Kingdom | 7 | 20% |
| United States | 3 | 9% |
| Spain | 2 | 6% |
| Israel | 2 | 6% |
| Australia | 1 | 3% |
| Switzerland | 1 | 3% |
| China | 1 | 3% |
| Netherlands | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 8 | 23% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 22 | 63% |
| Scientists | 11 | 31% |
| Practitioners (doctors, other healthcare professionals) | 2 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 1% |
| France | 1 | 1% |
| Unknown | 75 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 19% |
| Student > Ph. D. Student | 14 | 18% |
| Student > Master | 12 | 16% |
| Professor | 6 | 8% |
| Student > Bachelor | 3 | 4% |
| Other | 11 | 14% |
| Unknown | 16 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 19 | 25% |
| Psychology | 17 | 22% |
| Agricultural and Biological Sciences | 6 | 8% |
| Medicine and Dentistry | 5 | 6% |
| Engineering | 4 | 5% |
| Other | 4 | 5% |
| Unknown | 22 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 17 June 2019.
All research outputs
#1,949,369
of 25,382,250 outputs
Outputs from Frontiers in Neuroinformatics
#50
of 828 outputs
Outputs of similar age
#36,392
of 320,129 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#1
of 19 outputs
Altmetric has tracked 25,382,250 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 828 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done particularly well, scoring higher than 94% 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 320,129 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
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 has done particularly well, scoring higher than 99% of its contemporaries.