Brain functional connectivity and anatomical features as predictors of cognitive behavioral therapy outcome for anxiety in youths.

Abstract

Background: Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have a major impact. This study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms.

Methods: Two datasets were studied: (A) one consisted of n = 54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n = 15 subjects treated for 8 weeks. Connectome predictive modeling (CPM) was used to predict treatment response, as assessed with the PARS. The main analysis included network edges positively correlated with treatment outcome and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses are also presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R², r, and mean absolute error (MAE).

Results: The main model showed a MAE of approximately 3.5 (95% CI: [3.1-3.8]) points, an R² of 0.08 [-0.14-0.26], and an r of 0.38 [0.24-0.511]. When testing this model in the left-out sample (B), the results were similar, with an MAE of 3.4 [2.8-4.7], R²-0.65 [-2.29-0.16], and r of 0.4 [0.24-0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R².

Conclusions: The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, this study does not support the extensive use of CPM to predict outcomes in pediatric anxiety.