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Attention-Deficit/Hyperactivity Disorder

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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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...

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Related Experiment Video

Updated: Jun 12, 2025

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

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

Unsupervised machine learning for identifying attention-deficit/hyperactivity disorder subtypes based on cognitive

Masatoshi Yamashita^1,2, Qiulu Shou^1,2, Yoshifumi Mizuno^1,2,3

¹Research Center for Child Mental Development, University of Fukui, Fukui, Japan.

Psychological Medicine

|September 26, 2024

Summary

Attention-deficit/hyperactivity disorder (ADHD) has diverse subtypes. One subtype, ADHD-C, shows smaller brain volumes in specific regions, linked to language deficits, explaining inconsistent findings in ADHD research.

Keywords:

attention-deficit/hyperactivity disorder brain structure cognitive function heterogeneity unsupervised machine learning

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

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Last Updated: Jun 12, 2025

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

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

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Published on: April 1, 2018

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

Neuroscience
Developmental Psychology
Cognitive Science

Background:

Attention-deficit/hyperactivity disorder (ADHD) is associated with frontal lobe and basal ganglia anomalies.
Inconsistent findings in ADHD neuroimaging studies may stem from ADHD's inherent diversity.
Identifying distinct ADHD subtypes is crucial for understanding its neurobiological underpinnings.

Purpose of the Study:

To identify ADHD subtypes based on cognitive function.
To investigate distinct brain structural characteristics associated with each ADHD subtype.

Main Methods:

Utilized unsupervised machine learning on data from 656 children with ADHD (ABCD Study).
Employed National Institutes of Health Toolbox Tasks for cognitive function assessment.
Compared regional brain volumes between identified ADHD subtypes and 6601 non-ADHD children.

Main Results:

Hierarchical cluster analysis revealed three ADHD subtypes: ADHD-A (high cognitive ability), ADHD-B (low cognitive control, processing speed, memory), and ADHD-C (severely impaired cognitive control, memory, language).
ADHD-C subtype exhibited significantly smaller left inferior temporal gyrus and right lateral orbitofrontal cortex volumes compared to controls.
Right lateral orbitofrontal cortex volume positively correlated with language performance in the ADHD-C subtype; ADHD-A and ADHD-B showed no significant volume differences.

Conclusions:

The ADHD-C subtype presents with lateral orbitofrontal cortex anomalies linked to language deficits.
Subtype-specific brain structural differences may resolve inconsistencies in previous ADHD neuroimaging research.