Abstract
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.
Highlights
Human emotions represent complex processes within the nervous system
The model parameters were chosen based on prior experience with skin conductance and heart rate data
The weaker response to the conditioned stimulus (CS)+UStrials may be in part due to the use of the trace, rather than the delay, fear conditioning paradigm as we describe in the subsequent section
Summary
Human emotions represent complex processes within the nervous system. Changes in emotion manifest themselves through a number of physiological means. The physiological and biochemical changes that accompany these neural processes are observable, and provide a means for their estimation. This window to neural state estimation is one that can have important implications for wearable monitoring. Anxiety disorders often include symptoms of excessive fear and worry [6], and there is a heightened level of sympathetic nervous system activation in these patients [7]. Increased sympathetic drive gives rise to measurable biosignal changes, in response to certain stimuli/cues (e.g. elevated heart rate and facial electromyography [EMG] responses to trauma-related cues in PTSD patients [10]). An index of sympathetic arousal extracted conveniently from physiological signals could aid in the management of neuropsychiatric disorders involving pathological fear and anxiety
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