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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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Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning.

Chan-Mo Yang^1,2, Jaeyoung Shin³, Johanna Inhyang Kim⁴

¹Department of Psychiatry, Wonkwang University School of Medicine, Iksan, Korea.

Clinical Psychopharmacology and Neuroscience : the Official Scientific Journal of the Korean College of Neuropsychopharmacology

|October 20, 2023

Summary

This summary is machine-generated.

Functional near-infrared spectroscopy (fNIRS) effectively identified children with attention deficit hyperactivity disorder (ADHD) using machine learning. This neuroimaging technique shows promise as a diagnostic biomarker for ADHD in pediatric populations.

Keywords:

Attention deficit disorder with hyperactivity Child Machine learning Near-infrared spectroscopy, NIRS

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Area of Science:

Neuroscience
Biomarkers
Medical Imaging

Background:

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder impacting self-control.
Previous functional near-infrared spectroscopy (fNIRS) studies indicate brain region differences between ADHD children and controls during cognitive tasks.

Purpose of the Study:

To apply machine learning to task-based fNIRS data for identifying medication-naive ADHD children.
To evaluate the efficacy of fNIRS in discriminating ADHD patients from healthy controls (HC).

Main Methods:

fNIRS signals were collected from 33 ADHD children and 39 HC during the Stroop task.
Regularized linear discriminant analysis (RLDA) was employed for classification.
Leave-one-out cross-validation was used to assess classification performance.

Main Results:

The RLDA model achieved an accuracy of 0.82.
Classification yielded a sensitivity of 0.67 and a specificity of 0.93.
The model successfully discriminated between ADHD children and HC.

Conclusions:

Task-based fNIRS data, analyzed with RLDA, can effectively differentiate children with ADHD from healthy controls.
fNIRS shows potential as a non-invasive diagnostic biomarker for ADHD in children.