AbstractDefense responses are a highly conserved behavioral response set across species. Defense responses motivate organisms to detect and react to threats and potential danger as a precursor to anxiety. Accurate measurement of temporal defense responses is important for understanding clinical anxiety and mood disorders, such as post-traumatic stress disorder, obsessive compulsive disorder, and generalized anxiety disorder. Within these conditions, anxiety is defined as a state of prolonged defense response elicitation to a threat that is ambiguous or unspecific. In this study, we aimed to develop a data-driven approach to capture temporal defense response elicitation through a multi-modality data analysis of physiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and eye-tracking information. A fear conditioning paradigm was adopted to develop a defense response classification model. From a classification model based on 42 feature sets, a higher order crossing feature set-based model was chosen for further analysis with cross-validation loss of 0.0462 (SEM: 0.0077). To validate our model, we compared predicted defense response occurrence ratios from a comprehensive situation that generates defense responses by watching movie clips with fear awareness and threat existence predictability, which have been reported to correlate with defense response elicitation in previous studies. We observed that defense response occurrence ratios are correlated with threat existence predictability, but not with fear awareness. These results are similar to those of previous studies using comprehensive situations. Our study provides insight into measurement of temporal defense responses via a novel approach, which can improve understanding of anxiety and related clinical disorders for neurobiological and clinical researchers.
Diagnosing Systems through Approximated Information
