PTSD Symptom dynamics after the great east japan earthquake: mapping the temporal structure using Dynamic Time Warping

Abstract

Background: After the Great East Japan Earthquake [GEJE], approximately 70,000 Japan Ground Self Defense Force [JGSDF] personnel were deployed, risking Post-Traumatic Stress Disorder [PTSD]. The network approach to psychopathology suggests that symptoms may cause and exacerbate each other, resulting in the emergence and maintenance of disorders, including PTSD. It is therefore important to further explore the temporal interplay between symptoms. Most studies assessing the factor structure of the Impact of Event Scale-Revised [IES-R] have used cross-sectional designs. In this study, the structure of the IES-R was re-evaluated while incorporating the temporal interplay between symptoms.Methods: Using Dynamic Time Warping [DTW] the distances between PTSD symptoms on the IES-R were modelled in 1120 JGSDF personnel. Highly correlated symptoms were clustered at the group level using Distatis three-way principal component analyses of the distance matrices. The resulting clusters were compared to the original three subscales of the IES-R using a Confirmatory Factor Analysis (CFA).Results: The DTW analysis yielded four symptom clusters: Intrusion (five items), Hyperarousal (six items), Avoidance (six items), and Dissociation (five items). CFA yielded better fit estimates for this four-factor solution (RMSEA = 0.084, CFI = 0.918, TLI = 0.906), compared to the original three subscales of the IES-R (RMSEA = 0.103, CFI = 0.873, TLI = 0.858).Conclusions: DTW offers a new method of modelling the temporal relationships between symptoms. It yielded four IES-R symptom clusters, which may facilitate understanding of PTSD as a complex dynamic system.

Keywords: Deformación dinámica del tiempo; Dinámica de síntomas; Disociación; Escala de Impacto del Evento revisada; PTSD; Redes; TEPT; dissociation; dynamic time warping; impact of event scale-revised; networks; symptom dynamics; 事件影响量表修订版; 动态时间规整; 症状动态; 网络; 解离.

Figure 1.

Explanation of Dynamic Time Warp algorithm. In panel A the (unstandardized) scores of these individual items 1, 6, and 9 are shown over time. We used the shape-based time-series clustering technique of DTW to yield the distance as a dissimilarity measure. The first step in DTW is creating a local cost matrix (CM), which in this case has 6 × 6 dimensions (as we included 6 assessments over time). In the second step, the DTW algorithm finds the path that minimizes the alignment between the two item scores by iteratively stepping through the LCM, starting at the lower left corner (i.e. LCMI1, 11) and finishing at the upper right corner (i.e. LCMI6, 61), while aggregating the total distance (i.e. ‘cost’). At each step, the algorithm takes the step in the direction in which the cost increases the least under the chosen constraint. The constraint was the Sakoe-Chiba window of size one, meaning one time-point before and after the current assessment. The way in which the algorithm traverses through the LCM is dictated by the chosen step pattern, in this case the default ‘symmetric2’ step pattern (B). Parts (C). (D), and (E) explain the calculations of DTW distances for the three symptom pairs, yielding 10, 8, and 1 as their respective distances. We can conclude that items 6 and 9 share a more similar trajectory over time (with a distance of only 1), compared to the trajectory of item 1 (with distances of 10 and 8).

Figure 2.

Hierarchical clustering procedure. Panel A shows the elbow and silhouette plots. The number of dimensions (symptom clusters) in the data was determined using the elbow plot, which was based on the eigenvalues in a downward curve based on three compromise factors, and the silhouette plot. Four dimensions yielded the highest average silhouette score and represented a slight curve in the elbow plot. Panel B shows a dendrogram of the hierarchical clustering procedure based on three compromise factors. Panels C and D show the compromise plots based on the Distatis analysis (three-way principal component analysis of the 1120 distance matrices). These represent the position of the 22 IES-R items in the compromise space using the first 2 compromise factors (panel C) and the first and the third compromise factor (panel D). The white horizontal and vertical error bars represent the 95% confidence intervals, estimated through bootstrapping with 500 resamples

Figure 3.

Undirected DTW symptom network. Items are represented as nodes and are colour-coded according to their cluster. Node sizes represent standardized centrality. Only statistically significant edges are shown, with a smaller average distance than that of other pairwise DTW distances (by t-test for independent sample; p < .05). Standardized centrality for each of the 22 items is presented in a bar chart.

Figure 4.

Directed DTW symptom network. Items are represented as nodes and are colour-coded according to their cluster. Node sizes represent the connectivity of that item (summing in- and out-strength centrality). Standardized in- and out-strength centrality for each of the 22 items is presented in a bar chart.