Evidence map›Paper›PMID 41847379›Full record

ArticleBio-protocol2026

Reconstruction of Axonal Projections of Single Neurons Using PointTree.

Lin Cai, Xuzhong Qu, Junwei Wang, Yuan Shen, Tingwei Quan

Abstract read
In one paragraph

Article in Bio-protocol, 2026. 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

5 authors.

Lin CaiBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Xuzhong QuBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Junwei WangBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yuan ShenBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Tingwei QuanBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The morphology of single-neuron axonal projections is critical for deciphering neural circuitry and information flow in the brain. Yet, manually reconstructing these complex, long-range projections from high-throughput whole-brain imaging data remains an exceptionally labor-intensive and time-consuming task. Here, we developed a points assignment-based method for axonal reconstruction, named PointTree. PointTree enables the precise identification of the individual axons from densely packed axonal population using a minimal information flow tree model to suppress the snowball effect of reconstruction errors. In this protocol, we have elaborated on how to configure the required environment for PointTree software, prepare suitable data for it, and run the software. This protocol can assist neuroscience researchers in more easily and rapidly obtaining the reconstruction results of neuronal axons. Key features • Optimized for mapping long-range axons that connect distant brain regions in dense or crossover scenarios. • Enables high-fidelity (F1-score > 80%) reconstruction of hundreds of GB of large-volume imaging data. • Compatible with LSM, fMOST, and HD-fMOST systems for diverse neuroimaging datasets.

Indexed as

Axonal projectionsBrain circuit mappingDense reconstructionNeuron reconstructionPoints assignments

Identifiers

PMID41847379
PMCPMC12989286

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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.