A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning
A mixed filter algorithm for sympathetic arousal tracking from skin conductance and heart rate measurements in Pavlovian fear conditioning
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DOI：
10.1371/journal.pone.0231659
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发表时间：
2020-04
期刊：
影响因子：
3.7
通讯作者：
D. S. Wickramasuriya;R. T. Faghih
中科院分区：
文献类型：
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作者：
D. S. Wickramasuriya;R. T. Faghih
病理性恐惧和焦虑症可能对患者个人和社会产生破坏性影响。已知恐惧学习和消退的神经回路在焦虑症的发展和维持中起着至关重要的作用。巴甫洛夫条件反射（Pavlovian conditioning），即受试者学习生物相关刺激和中性线索之间的联系，在指导治疗焦虑症的疗法开发方面发挥了重要作用。到目前为止，许多生理信号反应，如皮肤电导，心率，脑电图和脑血流量已被分析巴甫洛夫恐惧条件反射实验。然而，生理标记通常被单独检查，以深入了解恐惧获得的神经过程。我们提出了一种方法来跟踪一个单一的大脑相关的交感神经唤醒状态的生理信号特征在恐惧条件反射。我们开发了一个状态空间公式，概率相关的功能，从皮肤电导和心率的未观察到的交感神经唤醒状态。我们使用的期望最大化框架的状态估计和模型参数恢复。状态估计是通过贝叶斯滤波。我们评估我们的模型在一个跟踪恐惧条件反射实验中获得的模拟和实验数据。模拟数据上的结果表明，我们所提出的方法来估计一个未观察到的唤醒状态和恢复模型参数的能力。实验数据的结果是一致的皮肤电导测量，并提供良好的拟合作为一个二进制点过程的心跳建模。在状态空间模型中跟踪皮肤电导和心率唤醒的能力是可穿戴监视器开发的重要先导，可穿戴监视器可以帮助患者护理。焦虑和创伤相关的障碍通常伴随着交感神经紧张度升高，并且本文所述的方法可以在用于治疗目的的远程监测中找到临床应用。
Pathological fear and anxiety disorders can have debilitating impacts on individual patients and society. The neural circuitry underlying fear learning and extinction has been known to play a crucial role in the development and maintenance of anxiety disorders. Pavlovian conditioning, where a subject learns an association between a biologically-relevant stimulus and a neutral cue, has been instrumental in guiding the development of therapies for treating anxiety disorders. To date, a number of physiological signal responses such as skin conductance, heart rate, electroencephalography and cerebral blood flow have been analyzed in Pavlovian fear conditioning experiments. However, physiological markers are often examined separately to gain insight into the neural processes underlying fear acquisition. We propose a method to track a single brain-related sympathetic arousal state from physiological signal features during fear conditioning. We develop a state-space formulation that probabilistically relates features from skin conductance and heart rate to the unobserved sympathetic arousal state. We use an expectation-maximization framework for state estimation and model parameter recovery. State estimation is performed via Bayesian filtering. We evaluate our model on simulated and experimental data acquired in a trace fear conditioning experiment. Results on simulated data show the ability of our proposed method to estimate an unobserved arousal state and recover model parameters. Results on experimental data are consistent with skin conductance measurements and provide good fits to heartbeats modeled as a binary point process. The ability to track arousal from skin conductance and heart rate within a state-space model is an important precursor to the development of wearable monitors that could aid in patient care. Anxiety and trauma-related disorders are often accompanied by a heightened sympathetic tone and the methods described herein could find clinical applications in remote monitoring for therapeutic purposes.