Table 1_An explainable machine learning model based on multidimensional task-state electroencephalography for identifying attention-deficit/hyperactivity disorder comorbid developmental dyslexia.docx

Sensitivity and permutation analyses were performed.</p>Results<p>The PCA-LR model achieved a balanced accuracy of 80.5%, a Macro-F1 of 0.799, and a Macro-AUC of 0.912. Class-wise AUCs were 0.905 for ADHD-DD, 0.904 for ADHD, and 0.927 for TD. Left temporal α-band functional connectivity was a shared high-contribution feature in both disorder groups.

ADHD-DD was distinguished by widely distributed cross-regional connectivity spanning θ and α bands and by P200 amplitude. ADHD was characterized primarily by α-band network metrics and α-ERD, whereas TD relied mainly on θ and α bands functional connectivity and graph-theoretic metrics.</p>Conclusion<p>Our findings suggest that multidimensional task-state EEG features may help differentiate children with ADHD-DD comorbidity from those with ADHD or typical development.

They also indicate that machine-learning approaches may be useful for identifying neurodevelopmental comorbid conditions.</p>