Predictability of arousal in mouse slow wave sleep by accelerometer data.
Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5436652?pdf=render |
| _version_ | 1811213305898860544 |
|---|---|
| author | Gustavo Zampier Dos Santos Lima Sergio Roberto Lopes Thiago Lima Prado Bruno Lobao-Soares George C do Nascimento John Fontenele-Araujo Gilberto Corso |
| author_facet | Gustavo Zampier Dos Santos Lima Sergio Roberto Lopes Thiago Lima Prado Bruno Lobao-Soares George C do Nascimento John Fontenele-Araujo Gilberto Corso |
| author_sort | Gustavo Zampier Dos Santos Lima |
| collection | DOAJ |
| description | Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep. |
| first_indexed | 2024-04-12T05:44:28Z |
| format | Article |
| id | doaj.art-b6b4b74095b14701aea856e57e1bae36 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-12T05:44:28Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b6b4b74095b14701aea856e57e1bae362022-12-22T03:45:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01125e017676110.1371/journal.pone.0176761Predictability of arousal in mouse slow wave sleep by accelerometer data.Gustavo Zampier Dos Santos LimaSergio Roberto LopesThiago Lima PradoBruno Lobao-SoaresGeorge C do NascimentoJohn Fontenele-AraujoGilberto CorsoArousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep.http://europepmc.org/articles/PMC5436652?pdf=render |
| spellingShingle | Gustavo Zampier Dos Santos Lima Sergio Roberto Lopes Thiago Lima Prado Bruno Lobao-Soares George C do Nascimento John Fontenele-Araujo Gilberto Corso Predictability of arousal in mouse slow wave sleep by accelerometer data. PLoS ONE |
| title | Predictability of arousal in mouse slow wave sleep by accelerometer data. |
| title_full | Predictability of arousal in mouse slow wave sleep by accelerometer data. |
| title_fullStr | Predictability of arousal in mouse slow wave sleep by accelerometer data. |
| title_full_unstemmed | Predictability of arousal in mouse slow wave sleep by accelerometer data. |
| title_short | Predictability of arousal in mouse slow wave sleep by accelerometer data. |
| title_sort | predictability of arousal in mouse slow wave sleep by accelerometer data |
| url | http://europepmc.org/articles/PMC5436652?pdf=render |
| work_keys_str_mv | AT gustavozampierdossantoslima predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata AT sergiorobertolopes predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata AT thiagolimaprado predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata AT brunolobaosoares predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata AT georgecdonascimento predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata AT johnfontenelearaujo predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata AT gilbertocorso predictabilityofarousalinmouseslowwavesleepbyaccelerometerdata |