Amit Gaikwad,
Abhaykumar Vasanta Adole,
- Associate Professor, Department of Computer Science and Engineering, G.H Raisoni School of Engineering and Technology, University, Mhasala, Maharashtra, India
- Researcher, Department of Computer Science and Engineering, G.H Raisoni School of Engineering and Technology, University, Mhasala, Maharashtra, India
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by difficulties with attention, impulse control, behavioral regulation, and daily functioning that persist across childhood and adulthood. Clinical diagnosis is predominantly based on behavioral assessments and expert interpretation, which may result in subjectivity and delayed clinical decisions. To reduce reliance on subjective evaluation, this study introduces an automated ADHD identification framework that integrates resting-state functional Magnetic Resonance Imaging (rs-fMRI) with advanced machine learning models. Functional connectivity features are derived using a seed-based strategy that quantifies voxel-level correlation patterns between selected brain regions and the rest of the brain. The analysis emphasizes the Default Mode Network (DMN), given its established involvement in attention regulation and executive processing. A Convolutional Neural Network (CNN) is employed to perform classification, leveraging its capacity to learn spatially complex representations from high-dimensional neuroimaging data. The proposed framework is evaluated on the ADHD-200 Global Competition dataset, consisting of rs-fMRI scans from 776 participants acquired across multiple sites. The findings indicate that the proposed model outperforms traditional machine learning methods in terms of classification performance.
Keywords: Attention-deficit/hyperactivity disorder, rs-fMRI, deep learning, CNN, functional connectivity, default mode network
[This article belongs to Research and Reviews: A Journal of Neuroscience ]
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Research and Reviews: A Journal of Neuroscience
| Volume | 16 | |
| Issue | 02 | |
| Received | 04/05/2026 | |
| Accepted | 08/06/2026 | |
| Published | 31/08/2026 | |
| Publication Time | 119 Days |