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The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System

Overview of attention for article published in Frontiers in Systems Neuroscience, January 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user
facebook
1 Facebook page

Citations

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83 Dimensions

Readers on

mendeley
221 Mendeley
citeulike
1 CiteULike
Title
The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System
Published in
Frontiers in Systems Neuroscience, January 2010
DOI 10.3389/fnsys.2010.00030
Pubmed ID
Authors

Jürgen Rybak, Anja Kuß, Hans Lamecker, Stefan Zachow, Hans-Christian Hege, Matthias Lienhard, Jochen Singer, Kerstin Neubert, Randolf Menzel

Abstract

The honeybee standard brain (HSB) serves as an interactive tool for relating morphologies of bee brain neurons and provides a reference system for functional and bibliographical properties (http://www.neurobiologie.fu-berlin.de/beebrain/). The ultimate goal is to document not only the morphological network properties of neurons collected from separate brains, but also to establish a graphical user interface for a neuron-related data base. Here, we review the current methods and protocols used to incorporate neuronal reconstructions into the HSB. Our registration protocol consists of two separate steps applied to imaging data from two-channel confocal microscopy scans: (1) The reconstruction of the neuron, facilitated by an automatic extraction of the neuron's skeleton based on threshold segmentation, and (2) the semi-automatic 3D segmentation of the neuropils and their registration with the HSB. The integration of neurons in the HSB is performed by applying the transformation computed in step (2) to the reconstructed neurons of step (1). The most critical issue of this protocol in terms of user interaction time - the segmentation process - is drastically improved by the use of a model-based segmentation process. Furthermore, the underlying statistical shape models (SSM) allow the visualization and analysis of characteristic variations in large sets of bee brain data. The anatomy of neural networks composed of multiple neurons that are registered into the HSB are visualized by depicting the 3D reconstructions together with semantic information with the objective to integrate data from multiple sources (electrophysiology, imaging, immunocytochemistry, molecular biology). Ultimately, this will allow the user to specify cell types and retrieve their morphologies along with physiological characterizations.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 221 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 11 5%
United States 3 1%
United Kingdom 2 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 203 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 54 24%
Researcher 42 19%
Student > Master 27 12%
Student > Bachelor 20 9%
Professor 11 5%
Other 35 16%
Unknown 32 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 115 52%
Neuroscience 22 10%
Biochemistry, Genetics and Molecular Biology 17 8%
Computer Science 10 5%
Engineering 6 3%
Other 15 7%
Unknown 36 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 04 October 2016.
All research outputs
#3,921,401
of 22,716,996 outputs
Outputs from Frontiers in Systems Neuroscience
#362
of 1,339 outputs
Outputs of similar age
#22,933
of 163,657 outputs
Outputs of similar age from Frontiers in Systems Neuroscience
#5
of 23 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,339 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has gotten more attention than average, scoring higher than 72% 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 163,657 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 85% of its contemporaries.
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 has done well, scoring higher than 78% of its contemporaries.