Predicting Response to Group Cognitive Behavioral Therapy in Asthma by a Small Number of Abnormal Resting-State Functional Connections

Group cognitive behavior therapy (GCBT) is a successful psychotherapy for asthma. However, response varies considerably among individuals, and identifying biomarkers of GCBT has been challenging. Thus, the aim of this study was to predict an individual’s potential response by using machine learning algorithms and functional connectivity (FC) and to improve the personalized treatment of GCBT. We use the lasso method to make the feature selection in the functional connections between brain regions, and utilize t-test method to test the significant difference of these selected features. The feature selections are performed between controls (size = 20) and patients pre-GCBT (size = 20), patients pre-GCBT (size = 10) and patients post-GCBT (size = 10), as well as patients post-GCBT (size = 10) and controls (size = 10). Depending on these features, support vector classification was used to classify controls, patients pre- and post-GCBT. Pearson correlation analysis was employed to analyze the associations between clinical symptoms and the selected discriminated FCs in patients post-GCBT. At last, linear support vector regression was applied to predict the therapeutic effect of GCBT. After feature selection and significant analysis, five discriminated FC regarding as neuroimaging biomarkers of GCBT were digged out, which are also correlated with clinical symptoms. Using these discriminated functional connections, we could accurately classify the patients before and after GCBT (classification accuracy, 80%), and predict the therapeutic effect of GCBT in asthma (predicted accuracy, 67.8%). The findings in this study would provide a novel sight toward GCBT response prediction and further confirmed neural underpinnings of asthma. Moreover, our findings had clinical implications for personalized treatment by identifying asthmatic patients who will be appropriate for GCBT.