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Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning
Chan-Mo Yang1,2, Jaeyoung Shin3, Johanna Inhyang Kim4
1Department of Psychiatry, Wonkwang University School of Medicine, Iksan, Korea.
Insights
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.
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.
Objective:
: Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults characterized by cognitive and emotional self-control deficiencies. Previous functional near-infrared spectroscopy (fNIRS) studies found significant group differences between ADHD children and healthy controls during cognitive flexibility tasks in several brain regions. This study aims to apply a machine learning approach to identify medication-naive ADHD patients and healthy control (HC) groups using task-based fNIRS data.
Methods:
: fNIRS signals from 33 ADHD children and 39 HC during the Stroop task were analyzed. In addition, regularized linear discriminant analysis (RLDA) was used to identify ADHD individuals from healthy controls, and classification performance was evaluated.
Results:
: We found that participants can be correctly classified in RLDA leave-one-out cross validation, with a sensitivity of 0.67, specificity of 0.93, and accuracy of 0.82.
Conclusion:
: RLDA using only fNIRS data can effectively discriminate children with ADHD from HC. This study suggests the potential utility of the fNIRS signal as a diagnostic biomarker for ADHD children.
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