Literature DB >> 26742660

Recent developments in automatic scoring of rodent sleep.

Stefano Bastianini1, Chiara Berteotti, Alessandro Gabrielli, Viviana Lo Martire, Alessandro Silvani, Giovanna Zoccoli.   

Abstract

Sleep research carried out on rat and mouse model led to the publication of more than 5000 papers in the last 15 years, of which more than 500 in 2014. Wake-sleep scoring represents a crucial step of the work performed in pre- clinical sleep laboratories; it is a time consuming task and a potential source of errors affecting research outcomes. Several algorithms have been developed to perform automatic sleep scoring. Automatic scoring can accelerate the work of researchers substantially. Moreover, the use of sleep scoring algorithms facilitates the direct comparison of the results produced in different laboratories, with clear advantages from the viewpoint of the advancement of science and reduction of the number of animals used for research. The intent of this review is to provide the readers with the last developments in scoring in rodent sleep and to stress about the need of a cross-lab and cross-species validated algorithm.

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Year:  2015        PMID: 26742660     DOI: 10.12871/000398292015231

Source DB:  PubMed          Journal:  Arch Ital Biol        ISSN: 0003-9829            Impact factor:   1.000


  3 in total

1.  Spike-Based Functional Connectivity in Cerebral Cortex and Hippocampus: Loss of Global Connectivity Is Coupled to Preservation of Local Connectivity During Non-REM Sleep.

Authors:  Umberto Olcese; Jeroen J Bos; Martin Vinck; Jan V Lankelma; Laura B van Mourik-Donga; Friederike Schlumm; Cyriel M A Pennartz
Journal:  J Neurosci       Date:  2016-07-20       Impact factor: 6.167

2.  Accurate discrimination of the wake-sleep states of mice using non-invasive whole-body plethysmography.

Authors:  Stefano Bastianini; Sara Alvente; Chiara Berteotti; Viviana Lo Martire; Alessandro Silvani; Steven J Swoap; Alice Valli; Giovanna Zoccoli; Gary Cohen
Journal:  Sci Rep       Date:  2017-01-31       Impact factor: 4.379

3.  Automated scoring of pre-REM sleep in mice with deep learning.

Authors:  Niklas Grieger; Justus T C Schwabedal; Stefanie Wendel; Yvonne Ritze; Stephan Bialonski
Journal:  Sci Rep       Date:  2021-06-10       Impact factor: 4.379

  3 in total

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