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Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

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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 27, 2025

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Identifying ADHD-Related Abnormal Functional Connectivity with a Graph Convolutional Neural Network.

Yilin Hu1, Junling Ran1, Rui Qiao1

  • 1Department of Biomedical Engineering, School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Neural Plasticity
|May 8, 2024
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Summary
This summary is machine-generated.

Attention deficit hyperactivity disorder (ADHD) involves brain network dysfunction. This study identifies key brain regions, including frontal and temporal areas, crucial for attention, offering potential neuroimaging biomarkers for ADHD.

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Computational Psychiatry

Background:

  • Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder.
  • Existing research inadequately links neural networks and attention networks in ADHD.
  • Understanding the neural underpinnings of ADHD is critical for improved diagnosis and treatment.

Purpose of the Study:

  • To investigate the neural mechanisms underlying ADHD by examining functional connectivity.
  • To identify generalized neuroimaging biological tags within attention networks for ADHD.
  • To explore differential functional connectivity networks between ADHD and typically developing individuals.

Main Methods:

  • Utilized resting-state functional magnetic resonance imaging (fMRI) data.
  • Employed a graph convolutional neural network (GCNN) model for ADHD classification.
  • Visualized brain regions contributing significantly to classification results.

Main Results:

  • Identified significant functional connectivity differences in frontal, temporal, parietal, and cerebellar regions in individuals with ADHD.
  • Pinpointed discriminative brain regions including the orbitofrontal gyrus, rectus gyrus, insula, inferior temporal gyrus, transverse temporal gyrus, lingual gyrus, basal ganglia, and cerebellum.
  • These regions are integral to the attention executive control and attention orientation networks.

Conclusions:

  • Functional connectivity dysfunction in specific brain regions contributes to ADHD.
  • The identified brain regions and their network alterations serve as potential neuroimaging biomarkers for ADHD.
  • This research advances the understanding of ADHD's neural basis and aids in developing objective diagnostic tools.