Evidence map›Paper›PMID 35027889›Full record

ArticleFrontiers in neuroinformatics2021

SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies.

Sergio E Galindo, Pablo Toharia, Oscar D Robles, Luis Pastor

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Sergio E GalindoCiencias de la Computación, Arquitectura de Computado, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Esc. Tec. Sup. de Ingeniería Informática, Rey Juan Carlos University, Madrid, Spain.
Pablo TohariaDATSI, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain.
Oscar D RoblesCiencias de la Computación, Arquitectura de Computado, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Esc. Tec. Sup. de Ingeniería Informática, Rey Juan Carlos University, Madrid, Spain.
Luis PastorCiencias de la Computación, Arquitectura de Computado, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Esc. Tec. Sup. de Ingeniería Informática, Rey Juan Carlos University, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain complexity has traditionally fomented the division of neuroscience into somehow separated compartments; the coexistence of the anatomical, physiological, and connectomics points of view is just a paradigmatic example of this situation. However, there are times when it is important to combine some of these standpoints for getting a global picture, like for fully analyzing the morphological and topological features of a specific neuronal circuit. Within this framework, this article presents SynCoPa, a tool designed for bridging gaps among representations by providing techniques that allow combining detailed morphological neuron representations with the visualization of neuron interconnections at the synapse level. SynCoPa has been conceived for the interactive exploration and analysis of the connectivity elements and paths of simple to medium complexity neuronal circuits at the connectome level. This has been done by providing visual metaphors for synapses and interconnection paths, in combination with the representation of detailed neuron morphologies. SynCoPa could be helpful, for example, for establishing or confirming a hypothesis about the spatial distributions of synapses, or for answering questions about the way neurons establish connections or the relationships between connectivity and morphological features. Last, SynCoPa is easily extendable to include functional data provided, for example, by any of the morphologically-detailed simulators available nowadays, such as Neuron and Arbor, for providing a deep insight into the circuits features prior to simulating it, in particular any analysis where it is important to combine morphology, network topology, and physiology.

Indexed as

bioinformatics visualizationjoint neuron morphology and connectivity visualizationneuron network connectivity visual analyticsscientific and data visualization in neurosciencevisual analytics in neuroscience

Identifiers

PMID35027889
PMCPMC8751653

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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.