Evidence map›Paper›PMID 30618651›Full record

ArticleFrontiers in neuroanatomy2018

A Method for the Symbolic Representation of Neurons.

Jose Juan Aliaga Maraver, Susana Mata, Ruth Benavides-Piccione, Javier DeFelipe, Luis Pastor

Abstract read
In one paragraph

Article in Frontiers in neuroanatomy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. bioRxiv : the preprint server for biology · 2025
    Article
  3. Extensive Structural Remodeling of the Axonal Arbors of Parvalbumin Basket Cells during Development in Mouse Neocortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021
    Article
  4. Article
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

5 authors.

Jose Juan Aliaga MaraverDepartamento de Aeronaves y Vehículos Espaciales, Universidad Politécnica de Madrid (UPM), Madrid, Spain.
Susana MataDepartment of Computer Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
Ruth Benavides-PiccioneCajal Institute (CSIC), Madrid, Spain.
Javier DeFelipeCajal Institute (CSIC), Madrid, Spain.
Luis PastorDepartment of Computer Engineering, Universidad Rey Juan Carlos, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of neuroanatomy has progressed considerably in recent decades, thanks to the emergence of novel methods which provide new insights into the organization of the nervous system. These new methods have produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data. In other disciplines, such as in many engineering areas, scientists and engineers are dealing with increasingly complex systems, using hierarchical decompositions, graphical models and simplified schematic diagrams for analysis and design processes. This approach makes it possible for users to simultaneously combine global system views and very detailed representations of specific areas of interest, by selecting appropriate representations for each of these views. In this way, users can concentrate on specific details while also maintaining a general system overview - a capability that is essential for understanding structure and function whenever complexity is an issue. Following this approach, this paper focuses on a graphical tool designed to help neuroanatomists to better understand and detect morphological characteristics of neuronal cells. The method presented here, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has proven to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies. A software tool has been developed to generate graphical representations of neurons from 3D computer-aided reconstruction files.

Indexed as

neuron functional data visualizationneuron morphological and functional data visualizationneuroscience information visualizationpyramidal neuronsschematic representation of neuronssymbolic representations

Identifiers

PMID30618651
PMCPMC6305400

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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.