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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 12, 2025

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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Big Data Analytics for Uncovering Voxel Connectivity Patterns in Attention Deficit Hyperactivity Disorder.

Rezzy Eko Caraka^1,2,3,4, Khairunnisa Supardi⁵, Prana Ugiana Gio⁶

¹Engineers Profession Program, Graduate School, Universitas Padjadjaran, Bandung, West Java, 45363, Indonesia.

Journal of Multidisciplinary Healthcare

|August 7, 2025

Summary

This summary is machine-generated.

Machine learning identified key brain regions like the Fusiform Gyrus for Attention Deficit Hyperactivity Disorder (ADHD) diagnosis. This approach enhances classification accuracy for ADHD, improving diagnostic potential.

Keywords:

ADHD activation function brain voxels deep learning feature selection machine learning neuroimaging

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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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Area of Science:

Neuroscience
Computational Psychiatry
Medical Imaging Analysis

Background:

Attention Deficit Hyperactivity Disorder (ADHD) presents complex, heterogeneous brain activity patterns.
Identifying specific brain regions linked to ADHD is challenging due to neuroimaging data complexity.

Purpose of the Study:

To apply advanced machine learning for uncovering critical features in ADHD diagnosis.
To improve classification performance using neuroimaging data.

Main Methods:

Analysis of 5937 brain voxels from ADHD patient neuroimaging records.
Feature selection using Boruta, Random Forest with DALEX, and Neural Networks.
Dimensionality reduction (PCA) and clustering (KMeans, MCLUST) for pattern exploration; evaluation of activation functions (ReLU, Sigmoid, Tanh) in deep neural networks.

Main Results:

Identification of significant ADHD predictors: Fusiform Gyrus, Thalamus, and Superior Temporal Gyrus.
Machine learning integration improved classification accuracy.
ReLU-based neural networks showed superior performance across most metrics.

Conclusions:

A robust machine learning framework can analyze high-dimensional neuroimaging data for ADHD.
Identified brain regions serve as biologically relevant markers for ADHD.
Findings support data-driven approaches for neuropsychiatric diagnosis and personalized interventions.