A real‐time sleep scoring framework for closed‐loop sleep manipulation in mice
| Autor: | Farid Yaghouby, Bruce F. O'Hara, Sridhar Sunderam, A Ajwad, Dillon M. Huffman |
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| Rok vydání: | 2021 |
| Předmět: |
Male
Source code Computer science Cognitive Neuroscience media_common.quotation_subject Speech recognition Sleep REM Electroencephalography Mice 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine Classifier (linguistics) medicine Animals Hidden Markov model media_common Sleep restriction medicine.diagnostic_test Electromyography Eye movement General Medicine Mice Inbred C57BL 030228 respiratory system Female Sleep (system call) Sleep Closed loop Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Journal of Sleep Research. 30 |
| ISSN: | 1365-2869 0962-1105 |
| DOI: | 10.1111/jsr.13262 |
| Popis: | Subtle changes in sleep architecture can accompany and be symptomatic of many diseases or disorders. In order to probe and understand the complex interactions between sleep and health, the ability to model, track, and modulate sleep in preclinical animal models is vital. While various methods have been described for scoring experimental sleep recordings, few are designed to work in real time - a prerequisite for closed-loop sleep manipulation. In the present study, we have developed algorithms and software to classify sleep in real time and validated it on C57BL/6 mice (n = 8). Hidden Markov models of baseline sleep dynamics were fitted using an unsupervised algorithm to electroencephalogram (EEG) and electromyogram (EMG) data for each mouse, and were able to classify sleep in a manner highly concordant with manual scoring (Cohen's Kappa >75%) up to 3 weeks after model construction. This approach produced reasonably accurate estimates of common sleep metrics (proportion, mean duration, and number of bouts). After construction, the models were used to track sleep in real time and accomplish selective rapid eye movement (REM) sleep restriction by triggering non-invasive somatosensory stimulation. During REM restriction trials, REM bout duration was significantly reduced, and the classifier continued to perform satisfactorily despite the disrupted sleep patterns. The software can easily be tailored for use with other commercial or customised methods of sleep disruption (e.g. stir bar, optogenetic stimulation, etc.) and could serve as a robust platform to facilitate closed-loop experimentation. The source code and documentation are freely available upon request from the authors. |
| Databáze: | OpenAIRE |
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