Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors

Abstract

Importance: The weak link between subjective symptom-based diagnostics for posttraumatic psychopathology and objective neurobiological indices hinders the development of effective personalized treatments.

Objective: To identify early neural networks associated with posttraumatic stress disorder (PTSD) development among recent trauma survivors.

Design, setting, and participants: This prognostic study used data from the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) large-scale longitudinal neuroimaging dataset of recent trauma survivors. The NMPTDT study was conducted from January 20, 2015, to March 11, 2020, and included adult civilians who were admitted to a general hospital emergency department in Israel and screened for early PTSD symptoms indicative of chronic PTSD risk. Enrolled participants completed comprehensive clinical assessments and functional magnetic resonance imaging (fMRI) scans at 1, 6, and 14 months post trauma. Data were analyzed from September 2023 to March 2024.

Exposure: Traumatic events included motor vehicle incidents, physical assaults, robberies, hostilities, electric shocks, fires, drownings, work accidents, terror attacks, or large-scale disasters.

Main outcomes and measures: Connectome-based predictive modeling (CPM), a whole-brain machine learning approach, was applied to resting-state and task-based fMRI data collected at 1 month post trauma. The primary outcome measure was PTSD symptom severity across the 3 time points, assessed with the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Secondary outcomes included Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) PTSD symptom clusters (intrusion, avoidance, negative alterations in mood and cognition, hyperarousal).

Results: A total of 162 recent trauma survivors (mean [SD] age, 33.9 [11.5] years; 80 women [49.4%] and 82 men [50.6%]) were included at 1 month post trauma. Follow-up assessments were completed by 136 survivors (84.0%) at 6 months and by 133 survivors (82.1%) at 14 months post trauma. Among the 162 recent trauma survivors, CPM significantly predicted PTSD severity at 1 month (ρ = 0.18, P < .001) and 14 months (ρ = 0.24, P < .001) post trauma, but not at 6 months post trauma (ρ = 0.03, P = .39). The most predictive edges at 1 month included connections within and between the anterior default mode, motor sensory, and salience networks. These networks, with the additional contribution of the central executive and visual networks, were predictive of symptoms at 14 months. CPM predicted avoidance and negative alterations in mood and cognition at 1 month, but it predicted intrusion and hyperarousal symptoms at 14 months.

Conclusions and relevance: In this prognostic study of recent trauma survivors, individual differences in large-scale neural networks shortly after trauma were associated with variability in PTSD symptom trajectories over the first year following trauma exposure. These findings suggest that CPM may identify potential targets for interventions.

Figure 1.. Study Flow Diagram

ED indicates emergency department; fMRI, functional magnetic resonance imaging; MRI, magnetic resonance imaging. ^aOther exclusion criteria were serious medical condition requiring clinical attention (n = 5), chronic posttraumatic stress disorder before the current event (n = 2), current substance use disorder (n = 1), head injury (n = 1), and no traumatic event (n = 1).

Figure 2.. Prediction of Development of Posttraumatic Stress Disorder (PTSD) Symptoms

A to C, Correlations between connectome-based predictive modeling (CPM)–predicted Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) total scores and actual total scores at 1 month (A), 6 months (B), and 14 months (C) post trauma. Left panels: For each time point, dashed lines mark the median of these correlations, and bars indicate CPM predictions and null distributions. Right panels: For each time point, scatter plots present the correlations between CPM-predicted and actual CAPS-5 total scores.

Figure 3.. Brain Networks Contribute to Symptom Predictions

Node-level and network-level contributions to predicting posttraumatic stress disorder (PTSD) symptom severity across time. Because only a few edges were positively associated with Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) total scores (as described in the Results), we present here only edges negatively associated with CAPS-5 total scores (ie, decreased connectivity associated with increased PTSD symptom severity). A and B, Degree centrality of nodes that negatively predicted CAPS-5 scores at 1 month (A) and 14 months (B) post trauma. Darker colors represent increased network centrality. C and D, Connections within and between canonical functional networks at 1 month (C) and 14 months (D) post trauma. The diagonal represents the average contribution of edges within a single network, and off-diagonal elements represent the average contribution of edges between 2 network pairs. Darker colors represent networks that contributed more toward the final prediction. aDMN indicates anterior default mode network; CBL, cerebellar network; CEN, central executive network; MSN, motor sensory network; pDMN, posterior default mode network; SAL, salience network; SC, subcortical network; VAs, visual association network; VI, visual network 1; VII, visual network 2.

Figure 4.. Lesion Analysis Results

A and B, Spearman rank correlations between the predicted symptom severity and actual symptom severity (Clinician-Administered PTSD Scale for DSM-5 total scores) from virtual lesion analyses at 1 month (A) and 14 months (B) post trauma. The whole-brain predictions at each time point are indicated by the horizontal dotted lines. Boxes indicate the IQR, and the solid lines within the boxes indicate the median; whiskers indicate the minimum and maximum values within 1.5 times the IQR. Single canonical networks that predicted better than the whole-brain connectomes were identified as driving predictions. aDMN indicates anterior default mode network; CBL, cerebellar network; CEN, central executive network; MSN, motor sensory network; pDMN, posterior default mode network; SAL, salience network; SC, subcortical network; VAs, visual association network; VI, visual network 1; VII, visual network 2.