Single-subject anxiety treatment outcome prediction using functional neuroimaging.

ABSTRACT: The possibility of individualized treatment prediction has profound implications for the development of personalized interventions for patients with anxiety disorders. Here we utilize random forest classification and pre-treatment functional magnetic resonance imaging (fMRI) data from individuals with generalized anxiety disorder (GAD) and panic disorder (PD) to generate individual subject treatment outcome predictions. Before cognitive behavioral therapy (CBT), 48 adults (25 GAD and 23 PD) reduced (via cognitive reappraisal) or maintained their emotional responses to negative images during fMRI scanning. CBT responder status was predicted using activations from 70 anatomically defined regions. The final random forest model included 10 predictors contributing most to classification accurac

SUBMITTER: Ball TM 

PROVIDER: S-EPMC3957121 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature