Evidence map›Paper›PMID 32844332›Full record

ArticleNeuroinformatics2021

GTree: an Open-source Tool for Dense Reconstruction of Brain-wide Neuronal Population.

Hang Zhou, Shiwei Li, Anan Li, Qing Huang, Feng Xiong, Ning Li, Jiacheng Han, Hongtao Kang, Yijun Chen, Yun Li and 8 more

Abstract read
PubMed Publisher
In one paragraph

Article in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
6.6field-weighted citation impact, top 3% of its field
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

31 citing papers in PubMed, 49 citations in OpenAlex.

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  18. Whole-brain mapping of histaminergic projections in mouse brain.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  19. Tracing weak neuron fibers.Bioinformatics (Oxford, England) · 2023
    Article
  20. 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

18 authors at 2 institutions in 1 country.

Hang Zhou *Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Shiwei Li *Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Anan Li *Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Qing HuangBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Feng XiongBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Ning LiBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Jiacheng HanBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Hongtao KangBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Yijun ChenBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Yun LiBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Huimin LinBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Yu-Hui ZhangBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Xiaohua LvBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Xiuli LiuBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Hui GongBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Qingming LuoBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Shaoqun ZengBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China.
Tingwei QuanBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong, University of Science and Technology, Hubei, Wuhan, 430074, China. quantingwei@hust.edu.cn.
Huazhong University of Science and Technology · CNWuhan National Laboratory for Optoelectronics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

Indexed as

NeuronsSoftwareAlgorithmsAnimalsAutomationBrainImaging, Three-DimensionalMaleMiceMice, Inbred C57BLMicroscopyNeuritesFast localization of errorsNeuronal morphologyNeuron reconstruction at brain-wide scale

Identifiers

PMID32844332
OpenAlexW3080232649

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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.