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Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies

Overview of attention for article published in BMC Bioinformatics, September 2017
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  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

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4 tweeters

Citations

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7 Dimensions

Readers on

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23 Mendeley
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Title
Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies
Published in
BMC Bioinformatics, September 2017
DOI 10.1186/s12859-017-1788-4
Pubmed ID
Authors

Marwan Abdellah, Juan Hernando, Nicolas Antille, Stefan Eilemann, Henry Markram, Felix Schürmann

Abstract

We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. The somata of the neurons are reconstructed on a physically-plausible basis relying on the physics engine in Blender. Our pipeline is applied to create 55 exemplar neurons representing the various morphological types that are reconstructed from the somatsensory cortex of a juvenile rat. The pipeline is then used to reconstruct a volumetric slice of a cortical circuit model that contains ∼210,000 neurons. The applicability of our pipeline to create highly realistic volumetric models of neocortical circuits is demonstrated with an in silico imaging experiment that simulates tissue visualization with brightfield microscopy. The results were evaluated with a group of domain experts to address their demands and also to extend the workflow based on their feedback. A systematic workflow is presented to create large scale synthetic tissue models of the neocortical circuitry. This workflow is fundamental to enlarge the scale of in silico neuroscientific optical experiments from several tens of cubic micrometers to a few cubic millimeters. Modelling and Simulation.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 26%
Student > Bachelor 4 17%
Student > Doctoral Student 2 9%
Lecturer 2 9%
Student > Ph. D. Student 2 9%
Other 5 22%
Unknown 2 9%
Readers by discipline Count As %
Neuroscience 6 26%
Computer Science 4 17%
Engineering 4 17%
Agricultural and Biological Sciences 3 13%
Energy 1 4%
Other 3 13%
Unknown 2 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 03 November 2017.
All research outputs
#6,259,452
of 12,091,568 outputs
Outputs from BMC Bioinformatics
#2,174
of 4,401 outputs
Outputs of similar age
#102,315
of 265,517 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 98 outputs
Altmetric has tracked 12,091,568 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,401 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 47th percentile – i.e., 47% 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 265,517 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 60% of its contemporaries.
We're also able to compare this research output to 98 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 59% of its contemporaries.