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oai:figshare.com:article/34054554 2026-10-02T05:37:06Z category_452 portal_316 item_type_3 month_year_10_2026

Table 1_An explainable machine learning model based on multidimensional task-state electroencephalography for identifying attention-deficit/hyperactivity disorder comorbid developmental dyslexia.docx Keduo Huang (25162629) Pengxiang Zuo (3152715) Xuemei Luo (65327) Chun Ji (6743789) Fang Yang (123514) Yuhao Huang (6255938) Xiaole Wang (2585827) Neurocognitive Patterns and Neural Networks attention deficit hyperactivity disorder comorbidity developmental dyslexia electroencephalography machine learning clinical identification Background<p>Attention-deficit/hyperactivity disorder (ADHD) and developmental dyslexia (DD) are highly comorbid, yet objective differentiation from individual disorders remains challenging due to the lack of neurophysiological biomarkers. This study aimed to develop an interpretable machine learning model leveraging multidimensional task-state EEG features to distinguish ADHD–DD comorbidity from ADHD and typical development (TD).</p>Methods<p>Ninety children (27 ADHD-DD, 40 ADHD, 23 TD) underwent EEG during a VSWM task. Five feature categories were extracted: event-related potentials, time-frequency characteristics, functional connectivity, graph-theoretic metrics, and nonlinear dynamics. Three feature-processing strategies and nine classifiers were compared within a nested cross-validation framework. Feature contribution was quantified by coefficient back-projection of the optimal PCA-LR mode. 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> 2026-10-02T05:37:06Z Dataset Dataset 10.3389/fnhum.2026.1976311.s001 https://figshare.com/articles/dataset/Table_1_An_explainable_machine_learning_model_based_on_multidimensional_task-state_electroencephalography_for_identifying_attention-deficit_hyperactivity_disorder_comorbid_developmental_dyslexia_docx/34054554 CC BY 4.0