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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.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....

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Updated: Jun 25, 2025

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

Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white

Huey-Ling Chiang¹, Chi-Shin Wu², Chang-Le Chen³

¹Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei City, Taiwan; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.

Asian Journal of Psychiatry

|May 31, 2024

Summary

This summary is machine-generated.

Machine learning identified key white matter microstructure features distinguishing attention-deficit/hyperactivity disorder (ADHD). Developmental changes in specific brain tracts are crucial for ADHD identification and may correlate with cognitive improvements.

Keywords:

ADHD Diffusion spectrum imaging Longitudinal study Machine-learning White matter microstructure

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Last Updated: Jun 25, 2025

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

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Published on: November 8, 2012

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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Published on: June 9, 2018

Area of Science:

Neuroimaging
Neuroscience
Computational Psychiatry

Background:

Attention-deficit/hyperactivity disorder (ADHD) diagnosis relies on behavioral symptoms, lacking objective biomarkers.
White matter microstructure alterations are implicated in ADHD pathophysiology.

Purpose of the Study:

To identify distinct white matter microstructure features differentiating ADHD from typically developing controls (TDC) using machine learning.
To assess the diagnostic value of baseline and longitudinal white matter changes in ADHD.

Main Methods:

Diffusion spectrum imaging (DSI) was performed on 51 ADHD patients and 60 TDC at two time points.
Three machine learning models were evaluated: baseline features, combined time points, and features including the rate of change.
Random forest algorithm was employed for classification.

Main Results:

The model incorporating longitudinal changes and baseline features achieved the highest classification performance (AUC = 0.73).
Key distinguishing features included the relative rate of change per year in tracts such as the superior longitudinal fasciculus and frontal aslant tract.
Faster change rates in certain tracts correlated with improved visual attention and memory functions.

Conclusions:

White matter microstructure and its developmental trajectory provide significant diagnostic value for ADHD.
These findings highlight the potential of neuroimaging biomarkers for objective ADHD identification and understanding developmental deviations.