ADHD subgroup discrimination with global connectivity features using hierarchical extreme learning machine: Resting-state FMRI study

Abstract

The differential diagnosis among ADHD subtypes is an important research area for the neuroimaging community. We pursue this goal by using machine learning techniques in this study. Selective subjects matched by age and handedness information from publicly available ADHD-200 dataset were used in this study. In addition, this work is based only on the resting-state fMRI images. We calculated the global connectivity maps from the fMRI images and used the average of the connectivity measure of each atlas-based cortical parcellation as a feature for the classifier input. For the classification, we used hierarchical extreme learning machine (H-ELM) classifier. By using the proposed feature extraction method, we achieved a 71.11% (p < 0.0090) nested cross-validated accuracy and a kappa score of 0.57 in multiclass classification settings.