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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: Jun 24, 2025

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

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

Published on: April 1, 2018

Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.

Bo Qiu¹, Qianqian Wang², Xizhi Li¹

¹School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China.

Frontiers in Neuroscience

|June 14, 2024

Summary

This summary is machine-generated.

This study introduces ASTNet, a novel neural network for identifying Attention Deficit Hyperactivity Disorder (ADHD) using resting-state functional magnetic resonance imaging (rs-fMRI). ASTNet effectively captures global brain activity patterns for improved ADHD classification.

Keywords:

adaptive learning dynamic functional connectivity fMRI local and global evolution patterns temporal dependency

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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 24, 2025

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Published on: April 1, 2018

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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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
Medical Imaging
Artificial Intelligence

Background:

Computer-aided diagnosis is crucial for Attention Deficit Hyperactivity Disorder (ADHD) identification.
Dynamic functional connectivity (dFC) analysis of resting-state functional magnetic resonance imaging (rs-fMRI) captures brain activity abnormalities but often overlooks global temporal patterns and adaptive learning.

Purpose of the Study:

To develop an advanced method for ADHD identification using rs-fMRI data.
To address limitations in existing dFC methods by incorporating global dynamic evolution and adaptive learning.

Main Methods:

Proposed an adaptive spatial-temporal neural network (ASTNet) for ADHD classification.
Utilized non-overlapping sliding windows to segment rs-fMRI time series.
Implemented adaptive functional connectivity generation (AFCG) to model spatial relationships and a temporal dependency mining (TDM) module to capture global temporal dynamics.

Main Results:

ASTNet demonstrated superior performance in automated ADHD classification compared to existing methods.
The proposed method effectively captures both spatial relationships and global temporal dependencies in brain activity.

Conclusions:

The developed ASTNet offers a significant advancement in ADHD identification through rs-fMRI analysis.
ASTNet's ability to model adaptive and global dynamic brain activity patterns enhances classification accuracy.