Evidence map›Paper›PMID 26084901›Full record

ArticleJournal of neurophysiology2015

MATLAB-based automated patch-clamp system for awake behaving mice.

Niraj S Desai, Jennifer J Siegel, William Taylor, Raymond A Chitwood, Daniel Johnston

Open access · greenAbstract read
In one paragraph

Article in Journal of neurophysiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.5field-weighted citation impact, top 11% 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

21 citing papers in PubMed, 40 citations in OpenAlex.

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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

5 authors at 1 institution in 1 country.

Niraj S DesaiCenter for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas desai@utexas.edu.
Jennifer J SiegelCenter for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
William TaylorCenter for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
Raymond A ChitwoodCenter for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
Daniel JohnstonCenter for Learning and Memory, Department of Neuroscience, The University of Texas at Austin, Austin, Texas.
The University of Texas at Austin · US

Funding

Neuronal Mechanisms for Working Memory in Prefrontal CortexR01MH094839 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI JOHNSTON, DANIEL, MAUK, MICHAEL D · 2011 to 2015
$1.9M
Cellular Information Processing in the HippocampusR01NS084473 · NINDS · UNIVERSITY OF TEXAS AT AUSTIN · PI JOHNSTON, DANIEL · 2013 to 2017
$1.7M
NIMH NIH HHS MH-094839NIMH NIH HHS R01 MH094839NINDS NIH HHS NS-084473NINDS NIH HHS R01 NS084473
6 · The paper itself

Abstract

Automation has been an important part of biomedical research for decades, and the use of automated and robotic systems is now standard for such tasks as DNA sequencing, microfluidics, and high-throughput screening. Recently, Kodandaramaiah and colleagues (Nat Methods 9: 585-587, 2012) demonstrated, using anesthetized animals, the feasibility of automating blind patch-clamp recordings in vivo. Blind patch is a good target for automation because it is a complex yet highly stereotyped process that revolves around analysis of a single signal (electrode impedance) and movement along a single axis. Here, we introduce an automated system for blind patch-clamp recordings from awake, head-fixed mice running on a wheel. In its design, we were guided by 3 requirements: easy-to-use and easy-to-modify software; seamless integration of behavioral equipment; and efficient use of time. The resulting system employs equipment that is standard for patch recording rigs, moderately priced, or simple to make. It is written entirely in MATLAB, a programming environment that has an enormous user base in the neuroscience community and many available resources for analysis and instrument control. Using this system, we obtained 19 whole cell patch recordings from neurons in the prefrontal cortex of awake mice, aged 8-9 wk. Successful recordings had series resistances that averaged 52 ± 4 MΩ and required 5.7 ± 0.6 attempts to obtain. These numbers are comparable with those of experienced electrophysiologists working manually, and this system, written in a simple and familiar language, will be useful to many cellular electrophysiologists who wish to study awake behaving mice.

Indexed as

SoftwareAnimalsBehavior, AnimalEquipment DesignMaleMiceMice, Inbred C57BLNeuronsPatch-Clamp TechniquesPattern Recognition, AutomatedPrefrontal CortexRunningTime FactorsWakefulnessautomationawakebehavingmousepatch clamp

Identifiers

PMID26084901
PMCPMC4725114
OpenAlexW2200926199

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.