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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.
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To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings.
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The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
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Related Experiment Video

Updated: Jun 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

Bayesian probability approach to ADHD appraisal.

Raina Robeva1, Jennifer Kim Penberthy2

  • 1Department of Mathematical Sciences, Sweet Briar College, Sweet Briar, Virginia, USA.

Methods in Enzymology
|November 10, 2009
PubMed
Summary
This summary is machine-generated.

Accurate diagnosis of attention-deficit hyperactivity disorder (ADHD) is crucial. A new Bayesian method combines multiple tests for precise ADHD assessment, improving on traditional subjective methods.

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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
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Using Brain Activation (nir-HEG/Q-EEG) and Execution Measures (CPTs) in a ADHD Assessment Protocol
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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)
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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:

  • Neuroscience
  • Psychology
  • Biostatistics

Background:

  • Accurate diagnosis of attention-deficit hyperactivity disorder (ADHD) is critical due to significant psychosocial sequelae associated with undiagnosed cases.
  • Traditional ADHD diagnosis relies on subjective symptom history, lacking a definitive single test.
  • ADHD is a complex disorder with physiological underpinnings and multifactorial etiology.

Purpose of the Study:

  • To introduce a novel stochastic method using a Bayesian interface for quantifying and assessing ADHD.
  • To demonstrate the method's ability to integrate diverse psychometric and physiological data into a unified diagnostic instrument.
  • To establish a framework for meta-analysis of ADHD studies using disparate data and tests.

Main Methods:

  • A Bayesian interface was developed to combine various psychometric tests and physiological markers.
  • The method refines an individual's probability of ADHD at each assessment step.
  • A meta-analysis framework was created to calibrate disparate study data onto a unified probability scale.

Main Results:

  • The Bayesian method achieved 100% correct classification in a small study of college students with ADHD and matched controls.
  • Individual assessments in the pilot study were incapable of perfect classification.
  • Meta-analysis of five studies combining behavioral rating scales and EEG data (56 ADHD, 55 controls) demonstrated the method's utility.

Conclusions:

  • The proposed Bayesian stochastic method offers a more accurate and objective approach to ADHD diagnosis.
  • This integrated approach enhances diagnostic precision compared to individual assessment tools.
  • The framework facilitates robust meta-analysis, unifying findings from diverse ADHD research.