Evidence map›Paper›PMID 40667305›Full record

ArticlebioRxiv : the preprint server for biology2025

A scalable, all-optical method for mapping synaptic connectivity with cell-type specificity.

Maria V Moya, William J Cunningham, Jack P Vincent, Tim Wang, Michael N Economo

Abstract readPreprint
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

5 authors.

Maria V MoyaDepartment of Biomedical Engineering, Boston University, Boston, MA.ORCID 0000-0003-2310-1990
William J CunninghamGraduate Program for Neuroscience, Boston University, Boston, MA.
Jack P VincentDepartment of Biomedical Engineering, Boston University, Boston, MA.ORCID 0000-0001-9066-2494
Tim WangAllen Institute for Neural Dynamics, Seattle, WA.
Michael N EconomoDepartment of Biomedical Engineering, Boston University, Boston, MA.ORCID 0000-0001-9893-1505

Funding

High-throughput mapping of synaptic connectivity between transcriptomically defined cell typesRF1MH126882 · NIMH · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI ECONOMO, MICHAEL NICHOLAS · 2022 to 2022
$1.4M
Uncovering the Molecular Determinants of Cell Type-Specific ConnectivityK99NS139312 · NINDS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI MOYA, MARIA VICTORIA · 2024 to 2025
$253k
A Scalable Method for Mapping Microconnectivity in Transcriptomically Distinct Neuron TypesF32MH129149 · NIMH · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI MOYA, MARIA VICTORIA · 2022 to 2024
$150k
NIMH NIH HHS F32 MH129149NIMH NIH HHS RF1 MH126882NINDS NIH HHS K99 NS139312
6 · The paper itself

Abstract

Single-cell transcriptomics has uncovered the enormous heterogeneity of cell types that compose each region of the mammalian brain, but describing how such diverse types connect to form functional circuits has remained challenging. Current methods for measuring the probability and strength of cell-type specific connectivity motifs principally rely on low-throughput whole-cell recording approaches. The recent development of optical tools for perturbing and observing neural circuit activity, now notably including genetically encoded voltage indicators, presents an exciting opportunity to employ optical methods to greatly increase the throughput with which circuit connectivity can be mapped physiologically. At the same time, advances in spatial transcriptomics now enable the identification of cell types

Identifiers

PMID40667305
PMCPMC12262474

What OpenQuestion 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 OpenQuestion graph.