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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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Related Experiment Video

Updated: Sep 15, 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

Transformer-based structural connectivity networks for ADHD-related connectivity alterations.

Liting Shi¹, Lei Shi¹, Zhijun Cui²

¹Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.

Communications Medicine

|July 17, 2025

Summary

Structural connectivity networks derived from MRI show promise for diagnosing Attention-deficit/hyperactivity disorder (ADHD). Transformer-based deep learning models identified significant differences in brain connectivity patterns, aiding in objective ADHD assessment.

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Published on: May 7, 2014

Area of Science:

Neuroimaging
Computational Neuroscience
Developmental Neuroscience

Background:

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder impacting behavior and learning.
Current ADHD diagnosis relies on subjective assessments, highlighting the need for objective, imaging-based diagnostic tools.
This research explores the potential of magnetic resonance imaging (MRI)-derived structural connectivity networks for identifying ADHD-related alterations.

Purpose of the Study:

To investigate whether structural connectivity networks from MRI data can reveal alterations associated with ADHD.
To leverage Transformer-based deep learning models for constructing and analyzing brain structural connectivity.
To support a data-driven understanding of the neurobiological underpinnings of ADHD.

Main Methods:

Utilized brain MRI data from 947 individuals (aged 7-26 years) from the ADHD-200 dataset.
Employed Transformer-based deep learning models to learn brain region relationships and build structural connectivity networks.
Applied five-fold cross-validation and statistical analyses to assess model performance and group differences.

Main Results:

The developed method accurately distinguished individuals with ADHD from healthy controls with 71.9% accuracy and an AUC of 0.74.
Significant differences in structural connectivity patterns were observed (P < 10^-6), particularly in regions related to motor and executive functions.
Brain regions such as the thalamus and caudate showed markedly different importance rankings between ADHD and control groups.

Conclusions:

ADHD is associated with alterations in structural connectivity across multiple brain regions.
Transformer-based methods for building brain structural connectivity networks show potential for ADHD diagnosis and research.
Objective, network-based approaches can enhance our understanding and diagnosis of neurodevelopmental disorders like ADHD.