Literature DB >> 26014662

Stress-free automatic sleep deprivation using air puffs.

Brooks A Gross1, William M Vanderheyden2, Lea M Urpa3, Devon E Davis2, Christopher J Fitzpatrick4, Kaustubh Prabhu2, Gina R Poe5.   

Abstract

BACKGROUND: Sleep deprivation via gentle handling is time-consuming and personnel-intensive. NEW
METHOD: We present here an automated sleep deprivation system via air puffs. Implanted EMG and EEG electrodes were used to assess sleep/waking states in six male Sprague-Dawley rats. Blood samples were collected from an implanted intravenous catheter every 4h during the 12-h light cycle on baseline, 8h of sleep deprivation via air puffs, and 8h of sleep deprivation by gentle handling days.
RESULTS: The automated system was capable of scoring sleep and waking states as accurately as our offline version (∼90% for sleep) and with sufficient speed to trigger a feedback response within an acceptable amount of time (1.76s). Manual state scoring confirmed normal sleep on the baseline day and sleep deprivation on the two manipulation days (68% decrease in non-REM, 63% decrease in REM, and 74% increase in waking). No significant differences in levels of ACTH and corticosterone (stress hormones indicative of HPA axis activity) were found at any time point between baseline sleep and sleep deprivation via air puffs. COMPARISON WITH EXISTING
METHOD: There were no significant differences in ACTH or corticosterone concentrations between sleep deprivation by air puffs and gentle handling over the 8-h period.
CONCLUSIONS: Our system accurately detects sleep and delivers air puffs to acutely deprive rats of sleep with sufficient temporal resolution during the critical 4-5h post learning sleep-dependent memory consolidation period. The system is stress-free and a viable alternative to existing sleep deprivation techniques.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Air puffs; Automated; EEG; EMG; MATLAB; Non-REM sleep; Power spectral density; REM sleep; Sleep deprivation; Waking

Mesh:

Substances:

Year:  2015        PMID: 26014662      PMCID: PMC4589302          DOI: 10.1016/j.jneumeth.2015.05.010

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  36 in total

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