Department of Mathematics, University of Texas at Arlington, 411 S Nedderman Dr, Arlington, TX, 76019, USA.
Department of Psychology, University of Texas at Arlington, 501 S Nedderman Dr, Arlington, TX, 76019, USA.
Sci Rep. 2024 May 28;14(1):12261. doi: 10.1038/s41598-024-61706-y.
We accurately reconstruct the Local Field Potential time series obtained from anesthetized and awake rats, both before and during CO euthanasia. We apply the Eigensystem Realization Algorithm to identify an underlying linear dynamical system capable of generating the observed data. Time series exhibiting more intricate dynamics typically lead to systems of higher dimensions, offering a means to assess the complexity of the brain throughout various phases of the experiment. Our results indicate that anesthetized brains possess complexity levels similar to awake brains before CO administration. This resemblance undergoes significant changes following euthanization, as signals from the awake brain display a more resilient complexity profile, implying a state of heightened neuronal activity or a last fight response during the euthanasia process. In contrast, anesthetized brains seem to enter a more subdued state early on. Our data-driven techniques can likely be applied to a broader range of electrophysiological recording modalities.
我们准确地重建了麻醉和清醒大鼠在 CO2 安乐死前后获得的局部场电位时间序列。我们应用特征系统实现算法来识别一个潜在的线性动力系统，该系统能够生成观测数据。表现出更复杂动力学的时间序列通常会导致更高维的系统，为评估大脑在实验的各个阶段的复杂性提供了一种手段。我们的结果表明，麻醉状态下的大脑在 CO2 给药前具有与清醒大脑相似的复杂水平。安乐死后，这种相似性发生了显著变化，因为来自清醒大脑的信号显示出更具弹性的复杂特征，这意味着在安乐死过程中神经元活动增强或处于最后挣扎状态。相比之下，麻醉状态下的大脑似乎很早就进入了一种更为平静的状态。我们的数据驱动技术可能可以应用于更广泛的电生理记录模式。
