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Distinction of Neurons, Glia and Endothelial Cells in the Cerebral Cortex: An Algorithm Based on Cytological Features

Overview of attention for article published in Frontiers in Neuroanatomy, November 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

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Title
Distinction of Neurons, Glia and Endothelial Cells in the Cerebral Cortex: An Algorithm Based on Cytological Features
Published in
Frontiers in Neuroanatomy, November 2016
DOI 10.3389/fnana.2016.00107
Pubmed ID
Authors

Miguel Á. García-Cabezas, Yohan J. John, Helen Barbas, Basilis Zikopoulos

Abstract

The estimation of the number or density of neurons and types of glial cells and their relative proportions in different brain areas are at the core of rigorous quantitative neuroanatomical studies. Unfortunately, the lack of detailed, updated, systematic and well-illustrated descriptions of the cytology of neurons and glial cell types, especially in the primate brain, makes such studies especially demanding, often limiting their scope and broad use. Here, following an extensive analysis of histological materials and the review of current and classical literature, we compile a list of precise morphological criteria that can facilitate and standardize identification of cells in stained sections examined under the microscope. We describe systematically and in detail the cytological features of neurons and glial cell types in the cerebral cortex of the macaque monkey and the human using semithin and thick sections stained for Nissl. We used this classical staining technique because it labels all cells in the brain in distinct ways. In addition, we corroborate key distinguishing characteristics of different cell types in sections immunolabeled for specific markers counterstained for Nissl and in ultrathin sections processed for electron microscopy. Finally, we summarize the core features that distinguish each cell type in easy-to-use tables and sketches, and structure these key features in an algorithm that can be used to systematically distinguish cellular types in the cerebral cortex. Moreover, we report high inter-observer algorithm reliability, which is a crucial test for obtaining consistent and reproducible cell counts in unbiased stereological studies. This protocol establishes a consistent framework that can be used to reliably identify and quantify cells in the cerebral cortex of primates as well as other mammalian species in health and disease.

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The data shown below were collected from the profiles of 13 X users 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 267 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 267 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 49 18%
Researcher 47 18%
Student > Ph. D. Student 45 17%
Student > Master 26 10%
Student > Doctoral Student 12 4%
Other 36 13%
Unknown 52 19%
Readers by discipline Count As %
Neuroscience 87 33%
Biochemistry, Genetics and Molecular Biology 32 12%
Agricultural and Biological Sciences 30 11%
Medicine and Dentistry 16 6%
Psychology 5 2%
Other 34 13%
Unknown 63 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 13 November 2021.
All research outputs
#4,961,681
of 24,748,616 outputs
Outputs from Frontiers in Neuroanatomy
#330
of 1,235 outputs
Outputs of similar age
#77,200
of 317,911 outputs
Outputs of similar age from Frontiers in Neuroanatomy
#6
of 33 outputs
Altmetric has tracked 24,748,616 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,235 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has gotten more attention than average, scoring higher than 73% 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 317,911 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 75% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.