Predicting Fear Extinction in Posttraumatic Stress Disorder

Abstract

Fear extinction is the basis of exposure therapies for posttraumatic stress disorder (PTSD), but half of patients do not improve. Predicting fear extinction in individuals with PTSD may inform personalized exposure therapy development. The participants were 125 trauma-exposed adults (96 female) with a range of PTSD symptoms. Electromyography, electrocardiogram, and skin conductance were recorded at baseline, during dark-enhanced startle, and during fear conditioning and extinction. Using a cross-validated, hold-out sample prediction approach, three penalized regressions and conventional ordinary least squares were trained to predict fear-potentiated startle during extinction using 50 predictor variables (5 clinical, 24 self-reported, and 21 physiological). The predictors, selected by penalized regression algorithms, were included in multivariable regression analyses, while univariate regressions assessed individual predictors. All the penalized regressions outperformed OLS in prediction accuracy and generalizability, as indexed by the lower mean squared error in the training and holdout subsamples. During early extinction, the consistent predictors across all the modeling approaches included dark-enhanced startle, the depersonalization and derealization subscale of the dissociative experiences scale, and the PTSD hyperarousal symptom score. These findings offer novel insights into the modeling approaches and patient characteristics that may reliably predict fear extinction in PTSD. Penalized regression shows promise for identifying symptom-related variables to enhance the predictive modeling accuracy in clinical research.

Keywords: electrocardiography; fear extinction; machine learning; penalized regression; posttraumatic stress disorder; psychophysiology; skin conductance; startle.

Figure 1

Schematic of the analysis pipeline (adapted from [11]). The total sample (n = 125) was split into a training sample (80%) and a holdout validation sample (20%). We compared the performance of 3 machine learning algorithms (in addition to conventional linear regression) via 100 iterations of 10-fold cross-validation. The best-performing model (lowest mean squared error; MSE) was tuned and implemented (without modification) in the holdout validation sample.