Abstract
Group cognitive behavioral 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 we utilize t-test method to test the significant difference of these selected features. The feature selections are performed between controls (size = 20) and pre-GCBT patients (size = 20), pre-GCBT patients (size = 10) and post-GCBT patients (size = 10), and post-GCBT patients (size = 10) and controls (size = 10). Depending on these features, support vector classification was used to classify controls and pre- and post-GCBT patients. Pearson correlation analysis was employed to analyze the associations between clinical symptoms and the selected discriminated FCs in post-GCBT patients. 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 neuroimaging biomarkers of GCBT were discovered, 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 confirm neural underpinnings of asthma. Moreover, our findings had clinical implications for personalized treatment by identifying asthmatic patients who will be appropriate for GCBT.Clinical Trial RegistrationThe brain mechanisms of group cognitive behavioral therapy to improve the symptoms of asthma (Registration number: Chi-CTR-15007442, http://www.chictr.org.cn/index.aspx).
Highlights
Asthma is a common respiratory disease with the clinical symptom of reversible airflow restriction, which is associated with significantly higher comorbidities including psychiatric diseases (Su et al, 2016; Shen et al, 2017)
Some neurophysiological mechanisms underlying group CBT (GCBT) for asthma have been detected in our previous studies (Zhang et al, 2017a,b), and we found that abnormal spontaneous activity and insula functional connectivity (FC) would be reversed after treatment
We find out five discriminated FCs that were abnormal in asthma, but there were no statistical differences between healthy controls (HCs) and post-GCBT patients (Table 2)
Summary
Asthma is a common respiratory disease with the clinical symptom of reversible airflow restriction, which is associated with significantly higher comorbidities including psychiatric diseases (Su et al, 2016; Shen et al, 2017). Previous studies consistently demonstrated that CBT could be utilized to encourage asthmatic patients to keep control of their symptoms, accept their problems, and alleviate asthmarelated negative emotions (Parry et al, 2012; Feldman et al, 2016; Kew et al, 2016). Regarding CBT in asthma including individual and group models, group CBT (GCBT) may cost less as well as acquire more social support offered by group compared with individual therapy (Yorke et al, 2017).
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