Evidence map›Paper›PMID 32358193›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Mapping mesoscale axonal projections in the mouse brain using a 3D convolutional network.

Drew Friedmann, Albert Pun, Eliza L Adams, Jan H Lui, Justus M Kebschull, Sophie M Grutzner, Caitlin Castagnola, Marc Tessier-Lavigne, Liqun Luo

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed
11.5field-weighted citation impact, top 1% 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

59 citing papers in PubMed, 99 citations in OpenAlex.

  1. System-level synchronization and subtype-specific modules govern brain-wide serotonin axon innervation.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  4. Maternal experience alters brain-wide representation of infant cries.bioRxiv : the preprint server for biology · 2026
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  14. Chemoarchitectural studies of the rat hypothalamus and zona incerta.bioRxiv : the preprint server for biology · 2025
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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

9 authors at 2 institutions in 1 country.

Drew FriedmannDepartment of Biology, Stanford University, Stanford, CA 94305.ORCID 0000-0002-2138-8177
Albert PunDepartment of Biology, Stanford University, Stanford, CA 94305.
Eliza L AdamsDepartment of Biology, Stanford University, Stanford, CA 94305.ORCID 0000-0003-2800-2967
Jan H LuiDepartment of Biology, Stanford University, Stanford, CA 94305.ORCID 0000-0003-1646-3424
Justus M KebschullDepartment of Biology, Stanford University, Stanford, CA 94305.
Sophie M GrutznerDepartment of Biology, Stanford University, Stanford, CA 94305.
Caitlin CastagnolaDepartment of Biology, Stanford University, Stanford, CA 94305.ORCID 0000-0003-2513-1114
Marc Tessier-LavigneDepartment of Biology, Stanford University, Stanford, CA 94305.
Liqun LuoDepartment of Biology, Stanford University, Stanford, CA 94305; lluo@stanford.edu.ORCID 0000-0001-5467-9264
Howard Hughes Medical Institute · USStanford University · US

Funding

Recombinant Immunolabels for Nanoprecise Brain Mapping Across ScalesU24NS109113 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KARL Daniel MURRAY · 2018 to 2026
$9.9M
EPILEPSY TRAINING PROGRAMT32NS007280 · NINDS · STANFORD UNIVERSITY · PI HUGUENARD, JOHN R · 1985 to 2020
$3.6M
Neuromodulation of Brain StatesR01NS104698 · NINDS · STANFORD UNIVERSITY · PI LUO, LIQUN · 2018 to 2022
$3.3M
Molecular dissection of prefrontal cortex circuit architectureK01MH114022 · NIMH · STANFORD UNIVERSITY · PI LUI, JAN H · 2017 to 2020
$650k
Howard Hughes Medical InstituteNIMH NIH HHS K01 MH114022NINDS NIH HHS R01 NS104698NINDS NIH HHS T32 NS007280NINDS NIH HHS U24 NS109113
6 · The paper itself

Abstract

The projection targets of a neuronal population are a key feature of its anatomical characteristics. Historically, tissue sectioning, confocal microscopy, and manual scoring of specific regions of interest have been used to generate coarse summaries of mesoscale projectomes. We present here TrailMap, a three-dimensional (3D) convolutional network for extracting axonal projections from intact cleared mouse brains imaged by light-sheet microscopy. TrailMap allows region-based quantification of total axon content in large and complex 3D structures after registration to a standard reference atlas. The identification of axonal structures as thin as one voxel benefits from data augmentation but also requires a loss function that tolerates errors in annotation. A network trained with volumes of serotonergic axons in all major brain regions can be generalized to map and quantify axons from thalamocortical, deep cerebellar, and cortical projection neurons, validating transfer learning as a tool to adapt the model to novel categories of axonal morphology. Speed of training, ease of use, and accuracy improve over existing tools without a need for specialized computing hardware. Given the recent emphasis on genetically and functionally defining cell types in neural circuit analysis, TrailMap will facilitate automated extraction and quantification of axons from these specific cell types at the scale of the entire mouse brain, an essential component of deciphering their connectivity.

Indexed as

AxonsAnimalsBrainBrain MappingImage Processing, Computer-AssistedImaging, Three-DimensionalMiceMice, Inbred C57BLNerve NetNeural Networks, ComputerNeural PathwaysNeuronsaxonslight-sheet microscopyneural networkstissue clearingwhole-brain

Identifiers

PMID32358193
PMCPMC7245124
OpenAlexW3022214373

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.