Exploring potential ADHD biomarkers through advanced machine learning: An examination of audiovisual integration networks

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by inattention and impulsivity, linked to disruptions in functional brain connectivity and structural alterations in large-scale brain networks. Although sensory pathway anomalies have been implicated in ADHD, the exploration of sensory integration regions remains limited. In this study, we adopted an exploratory approach to investigate the connectivity profile of auditory–visual integration networks (AVIN) in children with ADHD and neurotypical controls using the ADHD-200 rs-fMRI dataset. We expanded our exploration beyond network-based statistics (NBS) by extracting a diverse range of graph theoretical features. These features formed the basis for applying machine learning (ML) techniques to discern distinguishing patterns between the control group and children with ADHD. To address class imbalance and sample heterogeneity, we employed ensemble learning models, including balanced random forest (BRF), XGBoost, and EasyEnsemble classifier (EEC). Our findings revealed significant differences in AVIN between ADHD individuals and neurotypical controls, enabling automated diagnosis with moderate accuracy (74.30%). Notably, the EEC model demonstrated balanced sensitivity and specificity metrics, crucial for diagnostic applications, offering valuable insights for potential clinical use. These results contribute to understanding ADHD’s neural underpinnings and highlight the diagnostic potential of AVIN measures. However, the exploratory nature of this study underscores the need for future research to confirm and refine these findings with specific hypotheses and rigorous statistical controls. • This study explores the audiovisual integration network (AVIN) in children with ADHD. • We found differential AVIN associations in ADHD compared to neurotypical controls. • We suggest that incorporating graph features enhances ML diagnosis accuracy. • We showed ML models trained on AVIN outperform classifiers based on the default mode network.