Machine Learning-driven ADHD Classification: Exploring Medication Effects with VMD Sub-band Analysis

Aker E., GENGEÇ BENLİ Ş., Zeynep A. K.

CURRENT COMPUTER-AIDED DRUG DESIGN, 2025 (SCI-Expanded, Scopus)

Özet

Introduction: There has been increasing interest in neuroimaging studies in recent years, and computer-aided approaches have gained prominence in improving diagnostic accuracy. Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, impulsivity, and hyperactivity. Traditional diagnostic approaches often rely on subjective assessments, highlighting the need for more objective, data-driven methods. This study aims to classify ADHD subtypes and assess medication effects by converting resting-state fMRI images into one-dimensional (1D) signals and extracting statistical features using Variational Mode Decomposition (VMD). Methods: Resting-state fMRI data from the ADHD-200 dataset, including 41 healthy controls (HC), 41 medicated ADHD-Combined (ADHD-C) individuals, and 41 non-medicated ADHD-C individuals, were analyzed. The 1D fMRI signals were decomposed into nine sub-bands using VMD. Statistical features were extracted from each sub-band and classified using Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN). Results: VMD-derived features substantially improved classification performance. The highest binary classification accuracy was achieved by LDA: 96.34% distinguishing non-medicated ADHD from controls and 88.41% for medicated ADHD versus controls. The classification between medicated and non-medicated ADHD yielded 79.63% accuracy. Ternary classification across all groups reached 69.51% accuracy. Discussion: These findings show that the VMD-based approach improves the classification of ADHD subtypes and helps evaluate medication effects. However, the lower performance in multi-class classification reflects the complexity of ADHD neuroimaging data. Conclusion: The VMD-based approach improves classification accuracy, especially in distinguishing ADHD subtypes and medication effects, supporting its potential as an objective tool for diagnosis and treatment planning.