ArticleTranslational psychiatry2021
Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Technical system of electroencephalography-based brain-computer interface: Advances, applications, and challenges.Neural regeneration research · 2026Article
- Positive Selection in Aggression-Linked Genes and Their Protein Interaction Networks.Life (Basel, Switzerland) · 2025Article
- Toward a fair, gender-debiased classifier for the diagnosis of attention deficit/hyperactivity disorder- a Machine-Learning based classification study.BMC medical informatics and decision making · 2025Article
- White Matter Microstructural Abnormalities in Children with Familial vs. Non-Familial Attention-Deficit/Hyperactivity Disorder (ADHD).Biomedicines · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Machine learning on a smartphone-based CPT for ADHD prediction.Frontiers in psychiatry · 2025Article
- Predicting the onset of internalizing disorders in early adolescence using deep learning optimized with AI.Frontiers in psychiatry · 2025Article
- Virtual reality-assisted prediction of adult ADHD based on eye tracking, EEG, actigraphy and behavioral indices: a machine learning analysis of independent training and test samples.Translational psychiatry · 2024Article
- Resting-State Functional Connectivity Predicts Attention Problems in Children: Evidence from the ABCD Study.NeuroSci · 2024Article
- The Utility of Neuromuscular Assessment to Identify ADHD Among Patients with a Complex Symptom Picture.Journal of attention disorders · 2024Article
- Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback.Journal of electrical bioimpedance · 2024Article
- Predicting long-term outcome in anorexia nervosa: a machine learning analysis of brain structure at different stages of weight recovery.Psychological medicine · 2023Article
- Article
- Predicting individual cases of major adolescent psychiatric conditions with artificial intelligence.Translational psychiatry · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach.Molecular psychiatry · 2023Article
- Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial.Child and adolescent psychiatry and mental health · 2023Article
- Gray matter volumetric correlates of attention deficit and hyperactivity traits in emerging adolescents.Scientific reports · 2022Article
- Functional abnormality in the sensorimotor system attributed to NRXN1 variants in boys with attention deficit hyperactivity disorder.Brain imaging and behavior · 2022Article
- Structural brain morphometry as classifier and predictor of ADHD and reward-related comorbidities.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD's brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.
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