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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

46
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....
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Related Experiment Video

Updated: Jun 10, 2025

Event Related Potentials ERPs and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder ADHD
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Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

MohammadHadi Firouzi1, Kamran Kazemi2, Maliheh Ahmadi3

  • 1Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran.

Scientific Reports
|October 18, 2024
PubMed
Summary
This summary is machine-generated.

Skip-Vote-Net effectively classifies Attention Deficit Hyperactivity Disorder (ADHD) using dynamic functional connectivity from brain scans. This deep learning model achieves high accuracy in identifying ADHD, its subtypes, and distinguishing it from typically developing children.

Keywords:
Attention deficit/Hyperactivity disorder (ADHD)Brain dynamic functional connectivityDeep learningMajority votingrs-fMRI

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Attention Deficit Hyperactivity Disorder (ADHD) presents challenges in attention, hyperactivity, and impulsivity.
  • Resting-state functional magnetic resonance imaging (rs-fMRI) and functional connectivity analysis show promise for ADHD classification, but accuracy remains limited.
  • Dynamic changes in functional connectivity patterns are increasingly recognized in children with ADHD.

Purpose of the Study:

  • To introduce Skip-Vote-Net, a novel deep learning network for ADHD classification using dynamic connectivity analysis.
  • To evaluate Skip-Vote-Net's performance in classifying ADHD from typically developing children (TDC) and differentiating between ADHD subtypes (ADHDI and ADHDC).
  • To leverage rs-fMRI data from the ADHD-200 database (NYU dataset) for robust classification.

Main Methods:

  • Functional connectivity matrices were created from overlapping segments of rs-fMRI data using Pearson's correlation.
  • The Automated Anatomical Labeling (AAL) 116 atlas defined 116 regions of interest for analysis.
  • Skip-Vote-Net, a deep learning network with a majority voting mechanism, was developed for classification tasks.

Main Results:

  • Skip-Vote-Net achieved high classification accuracies: 97% (balanced) and 97.7% (unbalanced) for ADHD vs. TDC.
  • Accuracies for differentiating ADHD subtypes were exceptional: 99.4% for ADHDI vs. ADHDC.
  • A three-class classification (ADHDI, ADHDC, TDC) yielded an average accuracy of 98.86%.

Conclusions:

  • Skip-Vote-Net demonstrates superior performance compared to existing methods for ADHD classification.
  • The proposed deep learning approach shows significant potential as an effective diagnostic tool for ADHD and its subtypes.
  • Dynamic connectivity analysis using Skip-Vote-Net offers a promising avenue for identifying ADHD in children.