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Post-traumatic stress disorder (PTSD) is a psychiatric condition that arises following exposure to traumatic events such as natural disasters, forced displacement, or severe accidents. It significantly impairs individuals' ability to cope with daily activities and disrupts their emotional and psychological equilibrium.
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Neural network differences shortly after trauma predict posttraumatic stress disorder (PTSD) symptom trajectories. Connectome-based predictive modeling (CPM) may guide personalized PTSD interventions.

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Machine Learning

Background:

  • The link between subjective posttraumatic stress disorder (PTSD) symptoms and objective neurobiological markers is weak, hindering personalized treatment development.
  • Identifying early neural predictors of PTSD is crucial for timely intervention in trauma survivors.

Purpose of the Study:

  • To identify early neural networks associated with the development of posttraumatic stress disorder (PTSD) in recent trauma survivors.
  • To explore the predictive power of neural network connectivity for PTSD symptom trajectories.

Main Methods:

  • A prognostic study utilizing the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) longitudinal neuroimaging dataset.
  • Connectome-based predictive modeling (CPM) applied to functional magnetic resonance imaging (fMRI) data from 162 recent trauma survivors at 1 month post-trauma.
  • Assessed PTSD symptom severity (CAPS-5) and symptom clusters at 1, 6, and 14 months post-trauma.

Main Results:

  • CPM significantly predicted PTSD severity at 1 month (ρ=0.18) and 14 months (ρ=0.24) post-trauma, but not at 6 months.
  • Early predictive neural networks involved connections within and between the anterior default mode, motor sensory, and salience networks.
  • CPM predicted specific symptom clusters at different time points, with avoidance and negative alterations predicted at 1 month, and intrusion and hyperarousal at 14 months.

Conclusions:

  • Individual differences in large-scale neural networks shortly after trauma are associated with PTSD symptom variability over the first year.
  • These findings suggest that CPM can identify potential neural targets for early intervention in trauma survivors.
  • This research highlights the potential of neuroimaging and machine learning for predicting and potentially mitigating PTSD development.