AUTHOR=Rezaei Masoud , Zare Hoda , Hakimdavoodi Hamidreza , Nasseri Shahrokh , Hebrani Paria 

TITLE=Classification of drug-naive children with attention-deficit/hyperactivity disorder from typical development controls using resting-state fMRI and graph theoretical approach

JOURNAL=Frontiers in Human Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2022.948706

DOI=10.3389/fnhum.2022.948706

ISSN=1662-5161

ABSTRACT=Background and objectives: The study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder (ADHD) has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to determine the brain alterations in patients with ADHD and Typical Development (TD) children and accurately classify ADHD children from TD using the graph-theoretical measures obtained from resting-state fMRI (rs-fMRI).
Methods: We investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFECV feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.
Results: An accuracy of 78% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have the disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.
Conclusion: pattern recognition and graph measures of the brain network, based on the rs-fMRI data, can efficiently assist in the classification of ADHD from TD controls.