Abstract
Posttraumatic stress disorder (PTSD) is a mental health condition that may develop following exposure to trauma, with diverse and complex longitudinal trajectories of symptoms during the days to months after a traumatic event. To supplement mainstream chronic PTSD research, advancing our understanding of early post-trauma longitudinal trajectories of PTSD symptoms is warranted. In the current study, we aimed to demonstrate functional data analysis (FDA), a non-parametric method which has flexibility to capture complex non-linear patterns, as a potential superior analytic tool to comprehensively examine early post-trauma longitudinal interactions among PTSD symptoms, behavioral, brain structural, and other factors. First, data from two existing longitudinal acute trauma studies were pooled. Then, trajectories of PTSD symptom, depressive symptom, and right lateral orbital frontal gyrus thickness were estimated using functional principal component analysis. Last, the temporal associations among these measures were revealed using functional regression analysis. Results showed that both cortical thickness and depressive symptoms negatively associated with PTSD symptoms post-trauma, with dynamically changing on the strength of association. These findings demonstrated FDA as a useful tool to contribute to better understanding of PTSD development and thus may improve the efficacy of individualized PTSD preventative interventions.
C.-H. Shih and M. Premathilaka—Contributed equally.
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Shih, CH., Premathilaka, M., Xie, H., Wang, X., Liu, R. (2023). Estimating Dynamic Posttraumatic Stress Symptom Trajectories with Functional Data Analysis. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_30
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