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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 10, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

fMRI-Based Multi-class DMDC Model Efficiently Decodes the Overlaps between ASD and ADHD.

Zahra Zolghadr¹, Seyed Amir Hossein Batouli², Hamid Alavi Majd¹

¹Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Basic and Clinical Neuroscience

|October 15, 2024

Summary

This summary is machine-generated.

A new machine learning model accurately distinguishes between Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), and healthy individuals using neuroimaging data. This computational approach offers improved diagnostic capabilities for neurodevelopmental disorders.

Keywords:

ADHD-200 Attention deficit hyperactivity disorder (ADHD)Autism Autism brain imaging data exchange (ABIDE)Classification Functional connectivity High-dimensional low sample size data maximum dispersion classifier (DMDC)fMRI

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Last Updated: Jun 10, 2025

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Comparing Eye-tracking Data of Children with High-functioning ASD, Comorbid ADHD, and of a Control Watching Social Videos

Published on: December 7, 2018

Area of Science:

Neuroscience
Computational Psychiatry
Machine Learning

Background:

Neurodevelopmental disorders like ADHD and ASD share overlapping symptoms, complicating diagnosis and treatment.
Current diagnostic methods rely on behavior, which can be imprecise due to symptom overlap.
Neuroimaging offers potential for objective diagnostic tools.

Purpose of the Study:

To develop and evaluate a computational framework for discriminating between ADHD, ASD, and healthy controls using functional neuroimaging data.
To compare the performance of a novel Data Maximum Dispersion Classifier (DMDC) with Support Vector Machine (SVM).

Main Methods:

Applied a two-level multi-class Data Maximum Dispersion Classifier (DMDC) algorithm.
Utilized functional neuroimaging datasets from ADHD-200 and ABIDE.
Classified participants into ADHD, ASD, or healthy control groups based on functional connectivity values.

Main Results:

The DMDC model achieved an overall accuracy of 62%, with specific accuracies of 51% for healthy controls, 61% for ASD, and 84% for ADHD.
The SVM model showed lower accuracy for healthy controls (46%) and ASD (46%) but matched ADHD accuracy (84%).
The DMDC model demonstrated superior discrimination power, particularly for the ASD group.

Conclusions:

The novel DMDC method shows acceptable performance in classifying neurodevelopmental disorders and healthy individuals, outperforming the SVM in certain aspects.
Functional connectivity patterns, especially those involving the cerebellum, are key discriminators for these conditions.