Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

Document Type

Article

Publication Date

12-1-2023

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization.

Identifier

85164129213 (Scopus)

Publication Title

Translational Psychiatry

External Full Text Location

https://doi.org/10.1038/s41398-023-02536-w

e-ISSN

21583188

PubMed ID

37391419

Issue

1

Volume

13

Grant

CBIR17PIL012

Fund Ref

National Institute of Mental Health

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