The Potential of Personalized Post-Traumatic Stress Disorder Networks.

Abstract

Addressing the spectrum of mental health requires innovative methods. Network theory views psychopathological symptoms as complex dynamic systems, potentially allowing for the identification of better monitoring and intervention targets. This article advocates for the Dynamic Time Warping (DTW) algorithm to construct symptom networks, building on two recent studies on Post-Traumatic Stress Disorder (PTSD). The studies used a cohort of 55,632 Japan Ground Self-Defense Force personnel who completed the Impact of Event Scale-Revised annually from 2013 to 2018. The first study applied DTW to create symptom networks for individuals with significant PTSD symptoms (IES-R ≥ 25, n = 1,120). The second study analyzed dynamic symptom networks in four PTSD symptom trajectories (cumulative IES-R > 5, n = 10,211), generating temporal lead and -lag profiles to reflect symptom improvement and worsening. The first study identified four PTSD symptom clusters, yielding evidence for a new dissociation cluster. In the second study, lower network density in undirected DTW analyses was associated with chronic PTSD. Directed analyses showed that dissociation symptoms decreased first during recovery, while emotional reactivity persisted. Conversely, in worsening PTSD avoidance symptoms escalated first, while dissociation symptoms intensified last. These findings demonstrate the potential of DTW as a tool for constructing interpretable networks that capture the complex dynamics of psychological processes. This approach could enhance our understanding and treatment of a wide range of mental health conditions. Future research should further explore its applications to enable more personalized and effective mental health interventions.