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

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

Florentine H S van der Does et al. Eur J Psychotraumatol. 2023.

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.

Antecedentes: Después del Gran Terremoto del Este de Japón (GEJE, por sus siglas en inglés), se desplegaron aproximadamente 70,000 miembros de la Fuerza Terrestre de Autodefensa de Japón (JGSDF, por sus siglas en inglés), con el riesgo de sufrir un trastorno de estrés postraumático (TEPT). El enfoque de red de la psicopatología sugiere que los síntomas pueden causarse y exacerbarse entre sí, lo que da como resultado la aparición y el mantenimiento de trastornos, incluido el TEPT. Por lo tanto, es importante explorar más a fondo la interacción temporal entre los síntomas. La mayoría de los estudios que evalúan la estructura factorial de la escala Impact of Event Scale-Revised (IES-R) han utilizado diseños transversales. En este estudio, se reevaluó la estructura de la IES-R mientras se incorporaba la interacción temporal entre los síntomas.

Métodos: Usando la Deformación Dinámica del Tiempo (DTW por sus siglas en inglés), las distancias entre los síntomas de TEPT en la IES-R se modelaron en 1120 miembros del personal de la JGSDF. Los síntomas altamente correlacionados se agruparon a nivel de grupo utilizando análisis de componentes principales de tres vías DiSTATIS de las matrices de distancia. Los grupos resultantes se compararon con las tres subescalas originales de la IES-R utilizando un Análisis Factorial Confirmatorio (CFA).

Resultados: El análisis de DTW arrojó cuatro grupos de síntomas: intrusión (cinco elementos), hiperexcitación (seis elementos), evitación (seis elementos) y disociación (cinco elementos). El CFA produjo mejores estimaciones de ajuste para esta solución de cuatro factores (RMSEA = 0,084, CFI = 0,918, TLI = 0,906), en comparación con las tres subescalas originales de la IES-R (RMSEA = 0,103, CFI = 0,873, TLI = 0,858).

Conclusiones: La DTW ofrece un nuevo método para modelar las relaciones temporales entre los síntomas. Produjo cuatro grupos de síntomas de la IES-R, lo que puede facilitar la comprensión del TEPT como un sistema dinámico complejo.

背景 ：东日本大地震（GEJE）后，部署了大约 70,000 名日本陆上自卫队人员 (JGSDF），冒着患上创伤后应激障碍（PTSD）的风险。心理病理学网络方法表明，症状可能相互引发和加剧，导致包括创伤后应激障碍（PTSD）在内的疾病的出现和维持。因此，进一步探讨症状之间的时间相互作用非常重要。 大多数评估事件影响量表修订版（IES-R）因子结构的研究都使用了横截面设计。 在本研究中，重新评估了 IES-R 的结构，同时纳入了症状之间的时间相互作用。

方法：使用动态时间规整（DTW），在 1120 名JGSDF中对 IES-R 上的 PTSD 症状之间的距离进行建模。使用距离矩阵的 Distatis 三向主成分分析，将高度相关的症状群体级别进行聚类。使用验证性因子分析 (CFA) 将所得聚类与 IES-R 的三个原始子量表进行比较。

结果 ：DTW 分析得出四个症状簇：闯入（五个条目）、高唤起（六个条目）、回避（六个条目）和解离（五个条目）。 与 IES-R 的三个原始子量表（RMSEA = 0.103、CFI = 0.873、TLI = 0.858）相比，CFA 对该四因素解决方案产生了更好的拟合估计值（RMSEA = 0.084、CFI = 0.918、TLI = 0.906）。

结论：DTW 提供了一种对症状之间时间关系进行建模的新方法。它产生了四个 IES-R 症状簇，这可能有助于理解作为一个复杂动态系统的PTSD。

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.

Plain language summary

Personnel from the Japan Ground Self-Defense Force responded to the aftermath of the 2011 Great East Japan Earthquake, putting them at increased risk of developing symptoms of Post-Traumatic Stress Disorder.In recent years, psychological research has focused increasingly on methods to map the ways in which symptoms of psychopathology cause and exacerbate each other.The Dynamic Time Warping algorithm seems to be an appropriate and useful tool to analyse the interaction between post-traumatic stress symptoms over time, especially if these are not instantaneous or linear. This can improve our understanding of psychopathology and help move towards personalized medicine.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

Figure 1.
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.
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.
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.
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.

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References

    1. Aach, J., & Church, G. M. (2001). Aligning gene expression time series with time warping algorithms. Bioinformatics (Oxford, England), 17(6), 495–508. 10.1093/bioinformatics/17.6.495 - DOI - PubMed
    1. Abdi, H., O'Toole, A. J., Valentin, D., & Edelman, B. (2005). DISTATIS: The analysis of multiple distance matrices. Paper Presented at the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops.
    1. Abdi, H., Williams, L. J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multitable principal component analysis and three way metric multidimensional scaling. WIRES Computational Statistics, 4(2), 124–167. 10.1002/wics.198 - DOI
    1. American Psychiatric Association . (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). 10.1176/appi.books.9780890425596 - DOI
    1. Amin, T. B., & Mahmood, I. (2008). Speech recognition using dynamic time warping. Paper Presented at the 2008 2nd International Conference on Advances in Space technologies.

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