Subtype classification of attention deficit hyperactivity disorder with hierarchical binary hypothesis testing framework

Objective. The diagnosis of attention deficit hyperactivity disorder (ADHD) subtypes is important for the refined treatment of ADHD children. Although automated diagnosis methods based on machine learning are performed with structural and functional magnetic resonance imaging (sMRI and fMRI) data which have full observation of brains, they are not satisfactory with the accuracy of less than 80% for the ADHD subtype diagnosis. Approach. To improve the accuracy and obtain the biomarker of ADHD subtypes, we proposed a hierarchical binary hypothesis testing (H-BHT) framework by using brain functional connectivity (FC) as input bio-signals. The framework includes a two-stage procedure with a decision tree strategy and thus becomes suitable for the subtype classification. Also, typical FC is extracted in both two stages of identifying ADHD subtypes. That means the important FC is found out for the subtype recognition. Main results. We apply the proposed H-BHT framework to resting state fMRI datasets from ADHD-200 consortium. The results are achieved with the average accuracy 97.1% and an average kappa score 0.947. Discriminative FC between ADHD subtypes is found by comparing the P-values of typical FC. Significance. The proposed framework not only is an effective structure for ADHD subtype classification, but also provides useful reference for multiclass classification of mental disease subtypes.

Automated summary

In this study, a hierarchical binary hypothesis testing framework using brain functional connectivity as input biomarkers was proposed to improve the accuracy of ADHD subtype diagnosis and obtain biomarkers. Discriminative functional connectivity between ADHD subtypes was found by comparing the P-values of typical functional connectivity.