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Related Concept Videos

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....
56

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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Xiaojing Meng1,2, Ying Chen3, Yuan Gao4

  • 1The Affiliated Hospital of Xuzhou Medical University, Xuzhou, People's Republic of China.

Journal of Neural Engineering
|May 8, 2024
PubMed
Summary

This study introduces a new method to consistently identify brain biomarkers for Attention Deficit Hyperactivity Disorder (ADHD) across different datasets, improving diagnostic reliability.

Keywords:
ADHD classificationbiomarker detectioncross-dataset feature selectiongrouped SVM-RFE

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomarker Discovery

Background:

  • Attention Deficit Hyperactivity Disorder (ADHD) is a common childhood neurodevelopmental disorder.
  • Current data-driven ADHD diagnostic methods lack biomarker consistency across datasets, hindering reliability and interpretability.
  • Varying learned features in ADHD classification undermine the trustworthiness of identified biomarkers.

Purpose of the Study:

  • To develop a cross-dataset feature selection module to enhance biomarker consistency for ADHD.
  • To improve the reliability and interpretability of ADHD diagnostic methods.
  • To identify stable ADHD biomarkers using connectome gradient data.

Main Methods:

  • Proposed a cross-dataset feature selection (FS) module using grouped SVM-based recursive feature elimination (G-SVM-RFE).
  • Integrated the G-SVM-RFE module into a binary hypothesis testing (BHT) framework.
  • Utilized connectome gradient data for ADHD classification and biomarker identification.

Main Results:

  • Achieved an average accuracy of 96.7% in ADHD classification across diverse datasets.
  • Identified discriminative gradient components primarily originating from global brain regions.
  • Recognized specific brain regions with high appearance frequencies as consistent ADHD biomarkers.

Conclusions:

  • The proposed method enhances biomarker consistency and diagnostic accuracy for ADHD.
  • Identified biomarkers align with existing research on brain impairments in ADHD.
  • The approach provides enhanced biological explanations for ADHD mechanisms.