Evidence map›Paper›PMID 41292859›Full record

ArticlebioRxiv : the preprint server for biology2025

Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy.

Ahmadreza Attarpour, Misha Raffiee, Tony Xu, Jonas Osmann, Shruti Patel, Fengqing Yu, Braedyn Au, Andrew Clappison, Mahdi Biparva, Rui Zhu and 8 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

18 authors.

Ahmadreza AttarpourDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-5890-5740
Misha RaffieeDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Tony XuDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Jonas OsmannPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Shruti PatelPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Fengqing YuPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Braedyn AuHurvitz Brain Sciences, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
Andrew ClappisonPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Mahdi BiparvaPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Rui ZhuPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Ailey CrowDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Barsin Eshaghi GharagozDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Matthew RozakPhysical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.ORCID 0009-0001-5604-1405
Isabelle AubertBiological Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
JoAnne McLaurinBiological Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
Karl DeisserothDepartment of Bioengineering, Stanford University, Stanford, CA, USA.
Bojana StefanovicDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
Maged GoubranDepartment of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0001-5880-0818

Funding

Imaging the effects of transcranial alternatingcurrent stimulation in an experimental model of Alzheimer's DiseaseR01AG084681 · NIA · SUNNYBROOK RESEARCH INSTITUTE · PI Maged Goubran, Dewan Syed Fahmeed Hyder · 2024 to 2026
$1.7M
NIA NIH HHS R01 AG084681
6 · The paper itself

Abstract

Revealing how individual axons create a brain-wide connectome would be indispensable for understanding brain function and behavior, yet remains technically challenging. We introduce MAPL3, an end-to-end pipeline that integrates self-supervised learning with an innovative deep architecture to capture local and global brain-wide axonal projections. MAPL3 enables subject- and population-level quantitative laminar analysis, generalizes across experiments, and outperforms state-of-the-art methods. We showcase its ability to map the circuitry of the orbitofrontal cortex from single axons to whole-brain projectome.

Identifiers

PMID41292859
PMCPMC12642671

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.