Evidence map›Paper›PMID 32676020›Full record

ArticleFrontiers in computational neuroscience2020

Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis.

Stephan Grein, Guanxiao Qi, Gillian Queisser

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stephan GreinDepartment of Mathematics, Temple University, Philadelphia, PA, United States.
Guanxiao QiInstitute of Neuroscience and Medicine (INM-10), Research Centre Jülich, Jülich, Germany.
Gillian QueisserDepartment of Mathematics, Temple University, Philadelphia, PA, United States.

Funding

CRCNS: Collaboration toward an experimentally validated multiscale model of rTMSR01MH118930 · NIMH · TEMPLE UNIV OF THE COMMONWEALTH · PI QUEISSER, GILLIAN · 2018 to 2020
$497k
NIMH NIH HHS R01 MH118930
6 · The paper itself

Abstract

Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool is presented. The Density Visualization Pipeline (DVP) computes, visualizes and quantifies the density distribution, i.e., the "mass" of interneurons. We use the DVP to characterize and classify a set of GABAergic interneurons. Classification of GABAergic interneurons is of crucial importance to understand on the one hand their various functions and on the other hand their ubiquitous appearance in the neocortex. 3D density map visualization and projection to the one-dimensional x, y, z subspaces show a clear distinction between the studied cells, based on these metrics. The DVP can be coupled to computational studies of the behavior of neurons and networks, in which network topology information is derived from DVP information. The DVP reads common neuromorphological file formats, e.g., Neurolucida XML files,

Indexed as

barrel cortexdensity mapsdensity visualizationGABAergicinteractive data analysisneuronal morphologysynaptogenesisvisual programming

Identifiers

PMID32676020
PMCPMC7333680

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.