Evidence map›Paper›PMID 30256194›Full record

ArticleeLife2018

Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex.

Stephanie C Seeman, Luke Campagnola, Pasha A Davoudian, Alex Hoggarth, Travis A Hage, Alice Bosma-Moody, Christopher A Baker, Jung Hoon Lee, Stefan Mihalas, Corinne Teeter and 11 more

Open access · goldAbstract read
In one paragraph

Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed
10.2field-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

107 citing papers in PubMed, 218 citations in OpenAlex.

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  11. Critical scaling of novelty in the cortex.Nature communications · 2026
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47 more citing papers are in PubMed but not listed here.

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

21 authors at 3 institutions in 1 country.

Stephanie C Seeman *Allen Institute for Brain Science, Seattle, United States.
Luke Campagnola *Allen Institute for Brain Science, Seattle, United States.
Pasha A DavoudianAllen Institute for Brain Science, Seattle, United States.
Alex HoggarthAllen Institute for Brain Science, Seattle, United States.
Travis A HageAllen Institute for Brain Science, Seattle, United States.
Alice Bosma-MoodyAllen Institute for Brain Science, Seattle, United States.
Christopher A BakerAllen Institute for Brain Science, Seattle, United States.ORCID 0000-0002-0604-8449
Jung Hoon LeeAllen Institute for Brain Science, Seattle, United States.
Stefan MihalasAllen Institute for Brain Science, Seattle, United States.ORCID 0000-0002-2629-7100
Corinne TeeterAllen Institute for Brain Science, Seattle, United States.
Andrew L KoRegional Epilepsy Center at Harborview Medical Center, Seattle, United States.
Jeffrey G OjemannRegional Epilepsy Center at Harborview Medical Center, Seattle, United States.
Ryder P GwinnEpilepsy Surgery and Functional Neurosurgery, Swedish Neuroscience Institute, Seattle, United States.
Daniel L SilbergeldDepartment of Neurological Surgery, University of Washington School of Medicine, Seattle, United States.
Charles CobbsThe Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, United States.
John PhillipsAllen Institute for Brain Science, Seattle, United States.
Ed LeinAllen Institute for Brain Science, Seattle, United States.ORCID 0000-0001-9012-6552
Gabe MurphyAllen Institute for Brain Science, Seattle, United States.
Christof KochAllen Institute for Brain Science, Seattle, United States.
Hongkui ZengAllen Institute for Brain Science, Seattle, United States.ORCID 0000-0002-0326-5878
Tim JarskyAllen Institute for Brain Science, Seattle, United States.ORCID 0000-0002-4399-539X
Allen Institute for Brain Science · USUniversity of Washington · USSwedish Medical Center · US

Funding

Generation of novel cell type specific mouse genetic toolsU19MH114830 · NIMH · ALLEN INSTITUTE · PI ZENG, HONGKUI · 2017 to 2021
$64.7M
Establishing a Comprehensive and Standardized Cell Type Characterization PlatformU01MH105982 · NIMH · ALLEN INSTITUTE · PI ANDERSON, DAVID J, ZENG, HONGKUI · 2014 to 2016
$5.1M
Howard Hughes Medical InstituteNIH HHS U01MH105982NIMH NIH HHS U01 MH105982NIMH NIH HHS U19 MH114830
6 · The paper itself

Abstract

Generating a comprehensive description of cortical networks requires a large-scale, systematic approach. To that end, we have begun a pipeline project using multipatch electrophysiology, supplemented with two-photon optogenetics, to characterize connectivity and synaptic signaling between classes of neurons in adult mouse primary visual cortex (V1) and human cortex. We focus on producing results detailed enough for the generation of computational models and enabling comparison with future studies. Here, we report our examination of intralaminar connectivity within each of several classes of excitatory neurons. We find that connections are sparse but present among all excitatory cell classes and layers we sampled, and that most mouse synapses exhibited short-term depression with similar dynamics. Synaptic signaling between a subset of layer 2/3 neurons, however, exhibited facilitation. These results contribute to a body of evidence describing recurrent excitatory connectivity as a conserved feature of cortical microcircuits.

Indexed as

AdultAnimalsElectrophysiological PhenomenaEvoked PotentialsExcitatory Postsynaptic PotentialsFemaleHumansLimit of DetectionMaleMiceModels, NeurologicalNerve NetNeuronal PlasticityOptogeneticsPhotonsProbabilitycortical wiringelectrophysiologyhumanmouseneuroscienceshort term plasticity

Identifiers

PMID30256194
PMCPMC6158007
OpenAlexW2952921509

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.