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Related Concept Videos

Attention-Deficit/Hyperactivity Disorder01:30

Attention-Deficit/Hyperactivity Disorder

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: May 18, 2026

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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Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Evaluation of pattern recognition and feature extraction methods in ADHD prediction.

João Ricardo Sato1, Marcelo Queiroz Hoexter, André Fujita

  • 1Center of Mathematics, Computation and Cognition, Universidade Federal do ABC Santo Andre, Brazil ; Laboratório Interdisciplinar de Neurociências Clínicas, Department of Psychiatry, Universidade Federal de São Paulo São Paulo, Brazil ; Instituto Nacional de Psiquiatria do Desenvolvimento São Paulo, Brazil.

Frontiers in Systems Neuroscience
|September 28, 2012
PubMed
Summary

Neuroimaging analysis using ALFF+ReHo shows potential for identifying Attention Deficit/Hyperactivity Disorder (ADHD). Combining these with resting state networks (RSN) improved classification accuracy for ADHD subtypes, with information distributed across the whole brain.

Keywords:
ADHDSVMclassificationdiagnosisfeaturesmachine learningprediction

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Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
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Published on: April 1, 2018

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Last Updated: May 18, 2026

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
13:09

Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol

Published on: April 1, 2018

Area of Science:

  • Neuroimaging
  • Psychiatry
  • Computational Neuroscience

Background:

  • Attention Deficit/Hyperactivity Disorder (ADHD) is a common childhood neurodevelopmental disorder.
  • The underlying neural mechanisms of ADHD remain incompletely understood.
  • Neuroimaging techniques offer promising avenues for ADHD research and potential clinical applications.

Purpose of the Study:

  • To evaluate the predictive capabilities of various neuroimaging feature extraction and pattern recognition methods for ADHD classification.
  • To investigate the utility of regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and resting state networks (RSN) in discriminating ADHD patients from typically developing controls.
  • To differentiate between combined and inattentive ADHD subtypes using these neuroimaging features.

Main Methods:

  • Utilized structural and functional MRI datasets from the ADHD-200 Consortium.
  • Applied feature extraction methods: regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and independent components analysis maps (resting state networks; RSN).
  • Evaluated 10 different pattern recognition algorithms, including L2-regularized logistic regression, for classification tasks.

Main Results:

  • The combination of ALFF and ReHo features showed some ability to discriminate ADHD from typically developing controls, albeit with limited accuracy.
  • The combined ALFF+ReHo+RSN features achieved 67% accuracy in classifying combined vs. inattentive ADHD subtypes.
  • L2-regularized logistic regression demonstrated superior performance in the ADHD subtype classification.
  • Discriminative information for both ADHD vs. typically developing and subtype classifications was found to be spatially distributed across the entire brain.

Conclusions:

  • Neuroimaging features, particularly the combination of ALFF, ReHo, and RSN, hold potential for ADHD diagnosis and subtype differentiation.
  • The findings suggest that ADHD-related neural information is widespread rather than localized to specific brain regions.
  • Further research utilizing advanced machine learning and comprehensive neuroimaging data is warranted to enhance diagnostic accuracy and clinical utility for ADHD.