Correction to: Multimethod investigation of the neurobiological basis of ADHD symptomatology in children aged 9-10: baseline data from the ABCD study

Allgaier, Nicholas; Hahn, Sage; Yuan, Dekang; Albaugh, Matthew D.; Adise, Shana; Chaarani, Bader; Ortigara, Joseph; Juliano, Anthony; Potter, Alexandra; Garavan, Hugh

doi:10.1038/s41398-021-01320-y

Cited by 7 publications

(1 citation statement)

References 46 publications

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“…the main covariables were balanced between the groups, even though it may increase the classification difficulty. Recently, Owens et al (2021) reported cross-validated elastic net regression to predict a continuous measure of ADHD symptomatology. Elastic net regression can simultaneously implement feature selection and regression by adding weighted L1/L2 regularized terms to the linear model's loss, limiting the scale of variable coefficients for avoiding overfitting.…”

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confidence: 99%

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

et al. 2021

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Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9–10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.

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Section: Figure 5 | (A)mentioning

confidence: 99%

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

et al. 2021

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The Impact of Irritability and Callous Unemotional Traits on Reward Positivity in Youth with ADHD and Conduct Problems

Waxmonsky

Fosco

Waschbusch

et al. 2022

Res Child Adolesc Psychopathol

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

Cao

Martin

2023

Transl Psychiatry

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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.

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Correction to: Multimethod investigation of the neurobiological basis of ADHD symptomatology in children aged 9-10: baseline data from the ABCD study

Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41398-021-01320-y

Cited by 7 publications

References 46 publications

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

The Impact of Irritability and Callous Unemotional Traits on Reward Positivity in Youth with ADHD and Conduct Problems

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

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