A Bayesian Longitudinal Network Analysis of Panic-Disorder Symptoms and Respiratory Biomarkers

Abstract

The network theory of psychopathology is gaining popularity as a conceptualization of psychological disorders that may aid the identification of mechanisms of therapeutic change. However, many existing networks do not consider other relevant variables beyond the symptoms themselves. We present a large-scale ( n = 1,873), longitudinal Bayesian network analysis of panic disorder using the symptom items from the Panic Disorder Severity Scale (PDSS) and two respiratory biomarkers (respiration rate and end-tidal CO2) collected during routine monitoring of a capnometry-guided respiratory intervention (CGRI). Our findings offer support for avoidance and fear of panic as drivers of subsequent panic-disorder symptoms over the 4-week course of treatment. Moreover, respiration rate but not end-tidal CO2 was associated with downstream PDSS symptoms. These findings provide further evidence supporting the role of respiratory biomarkers in the maintenance of panic disorder and some support for normalization of dysfunctional breathing as one therapeutic mechanism governing CGRI.