Mouse activity across time scales: fractal scenarios.
Mouse activity across time scales: fractal scenarios.
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DOI：
10.1371/journal.pone.0105092
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Corso G
中科院分区：
文献类型：
--
作者：
Lima GZ;Lobão-Soares B;do Nascimento GC;França AS;Muratori L;Ribeiro S;Corso G
在这项工作中，我们设计了一个分类的鼠标活动模式的基础上加速度计数据使用去趋势波动分析。在这项研究中，我们使用两种典型的小鼠行为状态作为基准：自由活动中的清醒和慢波睡眠（SWS）。在这两种情况下，我们发现大致相同的模式：对于短时间间隔，我们观察到活动的高相关性-典型的1/f复杂模式-而对于大时间间隔，存在反相关性。短时间间隔的高度相关性（到：清醒状态和到：SWS）与高度协调的肌肉活动有关。在清醒状态下，我们将高相关性与肌肉活动和小鼠定型运动（梳理，清醒等）相关联。另一方面，观察到的反相关性在大的时间尺度（到：清醒状态和：SWS）在SWS出现相关的反馈自主反应。在SWS期间，从短尺度的相关制度到大尺度的反相关制度的过渡由呼吸周期间隔给出，而在清醒状态期间，这种过渡发生在与刻板小鼠运动的持续时间相对应的时间尺度上。此外，我们发现，清醒状态的特点是更长的时间尺度比SWS和一个软的过渡从相关到反相关。此外，这种在清醒状态下的软过渡包含了一个行为时间尺度窗口，它产生了多重分形图案。我们认为，在小鼠活动中观察到的多重分形是由几个刻板的运动，每一个具有特征的时间相关性的整合形成的。最后，我们比较了身体加速度波动时间序列在睡眠和清醒期间的健康小鼠的标度特性。有趣的是，睡眠和清醒之间的标度指数的差异与以前关于人类心跳的工作相当。作为补充，这些睡眠-觉醒动力学的性质可以更好地理解神经自主调节机制。
In this work we devise a classification of mouse activity patterns based on accelerometer data using Detrended Fluctuation Analysis. We use two characteristic mouse behavioural states as benchmarks in this study: waking in free activity and slow-wave sleep (SWS). In both situations we find roughly the same pattern: for short time intervals we observe high correlation in activity - a typical 1/f complex pattern - while for large time intervals there is anti-correlation. High correlation of short intervals ( to : waking state and to : SWS) is related to highly coordinated muscle activity. In the waking state we associate high correlation both to muscle activity and to mouse stereotyped movements (grooming, waking, etc.). On the other side, the observed anti-correlation over large time scales ( to : waking state and to : SWS) during SWS appears related to a feedback autonomic response. The transition from correlated regime at short scales to an anti-correlated regime at large scales during SWS is given by the respiratory cycle interval, while during the waking state this transition occurs at the time scale corresponding to the duration of the stereotyped mouse movements. Furthermore, we find that the waking state is characterized by longer time scales than SWS and by a softer transition from correlation to anti-correlation. Moreover, this soft transition in the waking state encompass a behavioural time scale window that gives rise to a multifractal pattern. We believe that the observed multifractality in mouse activity is formed by the integration of several stereotyped movements each one with a characteristic time correlation. Finally, we compare scaling properties of body acceleration fluctuation time series during sleep and wake periods for healthy mice. Interestingly, differences between sleep and wake in the scaling exponents are comparable to previous works regarding human heartbeat. Complementarily, the nature of these sleep-wake dynamics could lead to a better understanding of neuroautonomic regulation mechanisms.
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DOI：
10.1103/physreve.79.041920
发表时间：
2009-04
期刊：
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子：
--
作者：
Ivanov PCh;Ma QD;Bartsch RP;Hausdorff JM;Nunes Amaral LA;Schulte-Frohlinde V;Stanley HE;Yoneyama M
通讯作者：
Yoneyama M
影响因子：
3.3
作者：
Hu, K.;Scheer, F. A. J. L.;Shea, S. A.
通讯作者：
Shea, S. A.
影响因子：
2.9
作者：
Anteneodo, C.;Chialvo, D. R.
通讯作者：
Chialvo, D. R.
影响因子：
2.5
作者：
Beck, Travis W.;Housh, Terry J.;Malek, Moh H.
通讯作者：
Malek, Moh H.
DOI：
10.1016/j.physa.2004.01.042
发表时间：
2004-06-01
影响因子：
3.3
作者：
Hu, K;Ivanov, PC;Shea, SA
通讯作者：
Shea, SA