The Potential of Personalized Post-Traumatic Stress Disorder Networks
Floor van der Does1, Masanori Nagamine2, Masato Kitano2
1Department of Psychiatry, Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Psychiatry and Clinical Psychopharmacology
|August 28, 2025
Summary
Dynamic Time Warping (DTW) algorithm creates symptom networks to understand mental health dynamics. This method identified new PTSD symptom clusters and recovery patterns, offering personalized intervention potential.
Area of Science:
- Psychiatry and Computational Neuroscience
- Network Science in Mental Health
Background:
- Mental health treatment requires innovative monitoring and intervention strategies.
- Network theory offers a dynamic systems approach to understanding psychopathological symptoms.
- Previous research has explored symptom networks but lacked dynamic temporal analysis.
Purpose of the Study:
- To advocate for and demonstrate the utility of the Dynamic Time Warping (DTW) algorithm in constructing dynamic symptom networks.
- To analyze symptom dynamics and temporal relationships in Post-Traumatic Stress Disorder (PTSD).
- To identify potential targets for improved mental health monitoring and interventions.
Main Methods:
- Applied the DTW algorithm to construct symptom networks from longitudinal data of 55,632 Japan Ground Self-Defense Force personnel (2013-2018).
- Analyzed symptom networks in individuals with significant PTSD symptoms (IES-R ≥ 25) and across different PTSD trajectories (cumulative IES-R > 5).
- Generated temporal lead-lag profiles to examine symptom improvement and worsening dynamics.
Main Results:
- DTW identified four PTSD symptom clusters, including a novel dissociation cluster.
- Lower network density in undirected DTW analyses correlated with chronic PTSD.
- Directed analyses revealed distinct temporal patterns: dissociation decreased first in recovery, while avoidance escalated first in worsening PTSD.
Conclusions:
- DTW is a valuable tool for creating interpretable dynamic symptom networks.
- This approach enhances understanding of the complex temporal dynamics in mental health conditions like PTSD.
- Findings support the potential for DTW-based network analysis in developing personalized mental health interventions.
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