Aix Marseille University, INSERM, Institut de Neurosciences des Systèmes (INS), Marseille, France.
Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, United States.
Front Neural Circuits. 2022 Mar 24;16:783768. doi: 10.3389/fncir.2022.783768. eCollection 2022.
longitudinal recordings require reliable means to automatically discriminate between distinct behavioral states, in particular between awake and sleep epochs. The typical approach is to use some measure of motor activity together with extracellular electrophysiological signals, namely the relative contribution of theta and delta frequency bands to the Local Field Potential (LFP). However, these bands can partially overlap with oscillations characterizing other behaviors such as the 4 Hz accompanying rodent freezing. Here, we first demonstrate how standard methods fail to discriminate between sleep and freezing in protocols where both behaviors are observed. Then, as an alternative, we propose to use the smoothed cortical spindle power to detect sleep epochs. Finally, we show the effectiveness of this method in discriminating between sleep and freezing in our recordings.
纵向记录需要可靠的方法来自动区分不同的行为状态，特别是区分清醒和睡眠阶段。典型的方法是使用一些运动活动的度量指标，以及细胞外电生理信号，即局部场电位（LFP）中θ和δ频段的相对贡献。然而，这些频段可能与其他行为的振荡部分重叠，例如伴随啮齿动物冻结的 4 Hz 振荡。在这里，我们首先展示了在观察到两种行为的方案中，标准方法如何无法区分睡眠和冻结。然后，作为替代方法，我们建议使用平滑的皮质纺锤波功率来检测睡眠阶段。最后，我们展示了该方法在我们的记录中区分睡眠和冻结的有效性。
