ArticleScientific reports2024
Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Robust Multi-Site ADHD Classification via GraphSAGE-Based Functional Connectivity Modeling from rs-fMRI.Bioengineering (Basel, Switzerland) · 2026Article
- Hybrid Vi+ECNN framework for advanced ADHD diagnostic accuracy in medical imaging.Scientific reports · 2026Article
- Ecologically-Valid Emotion Signatures Enhance Mood Disorder Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Symptom-based depression subtypes: brain dynamic specificity and its association with gene expression profiles.Translational psychiatry · 2025Article
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHD
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Registered trials
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