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Brain Functional Connectivity and Anatomical Features as Predictors of Cognitive Behavioral Therapy Outcome for Anxiety in Youths. | LitMetric

AI Article Synopsis

  • - The study investigates whether structural and resting-state fMRI can predict anxiety symptoms in children after cognitive-behavioral therapy (CBT), as current predictive models are weak.
  • - Two datasets were analyzed, one with 54 subjects undergoing 12 weeks of CBT and another with 15 subjects receiving 8 weeks of treatment, using Connectome Predictive Modeling (CPM) methods.
  • - Results showed that the models had low predictive power, suggesting that existing methods like CPM are not effective in predicting outcomes for pediatric anxiety treatment despite initial hopes.

Article 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 major impact. However, existing clinical models are weakly predictive. The current 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; additionally we investigated models using anatomical features, instead of functional connectivity. 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 also are presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for , and mean absolute error (MAE).

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

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

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10862993PMC
http://dx.doi.org/10.1101/2024.01.29.24301959DOI Listing

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