Evidence map›Paper›PMID 26582569›Full record

ArticleJournal of neuroscience methods2016

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor.

Farid Yaghouby, Kevin D Donohue, Bruce F O'Hara, Sridhar Sunderam

Abstract read
In one paragraph

Article in Journal of neuroscience methods, 2016. 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
1.8field-weighted citation impact, top 15% 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, 91 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

4 authors at 1 institution in 1 country.

Farid YaghoubyDepartment of Biomedical Engineering, University of Kentucky, 143 Graham Ave., Lexington, KY 40506-0108, United States.
Kevin D DonohueElectrical and Computer Engineering, University of Kentucky, Lexington, KY, United States.
Bruce F O'HaraDepartment of Biology, University of Kentucky, Lexington, KY, United States.
Sridhar SunderamDepartment of Biomedical Engineering, University of Kentucky, 143 Graham Ave., Lexington, KY 40506-0108, United States. Electronic address: ssu223@uky.edu.
University of Kentucky · US

Funding

SYSTEM FOR AUTOMATED NONINVASIVE MONITORING OF MOUSE SLEEP AND BEHAVIORR44NS083218 · NINDS · SIGNAL SOLUTIONS, LLC · PI LHAMON, MICHAEL E, SUNDERAM, SRIDHAR · 2015 to 2016
$995k
SYSTEM FOR AUTOMATED NONINVASIVE MONITORING OF MOUSE SLEEP AND BEHAVIORR43NS083218 · NINDS · SIGNAL SOLUTIONS, LLC · PI O'HARA, BRUCE F, SUNDERAM, SRIDHAR · 2013 to 2014
$343k
Towards a Behavioral Index of Seizure SusceptibilityR03NS065451 · NINDS · UNIVERSITY OF KENTUCKY · PI SUNDERAM, SRIDHAR · 2009 to 2010
$148k
NINDS NIH HHS NS065451NINDS NIH HHS NS083218NINDS NIH HHS R03 NS065451NINDS NIH HHS R43 NS083218NINDS NIH HHS R44 NS083218
6 · The paper itself

Abstract

backgroundChanges in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. NEW

methodVigilance state was scored manually in 4-s epochs for 24-h EEG/EMG recordings in 20 mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing, respectively. These states were hypothesized to correspond to Wake, NREM, and REM, respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis.

resultsUsing only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep - characterized by irregular breathing and moderate delta EEG power - with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM, respectively. COMPARISON WITH EXISTING

methodsUnlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM.

conclusionsThis approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

Indexed as

ActigraphyAnimalsElectroencephalographyElectromyographyMaleMiceMice, Inbred C57BLMotionSensitivity and SpecificitySleep, REMSleep StagesWakefulnessEEGGeneticsHidden Markov modelHigh-throughput screeningMouseNoninvasive sleep scoringNREMPiezoelectricREMSleepSupervisedUnsupervised

Identifiers

PMID26582569
PMCPMC4715949
OpenAlexW2272349904

What OpenQuestion holds

Textmetadata
LicenceTDM
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.