ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2019
Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role and application of task-state fNIRS in identifying biomarkers in children and adolescents with ADHD: a systematic review.European child & adolescent psychiatry · 2026Pooled it
- Virtual reality (bio-)markers differentiate ADHD from depression and controls.European archives of psychiatry and clinical neuroscience · 2026Article
- Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.Neuroimage. Reports · 2026Article
- Analysis of electroencephalogram microstates and spectral power in children and adolescents with attention deficit hyperactivity disorder.European child & adolescent psychiatry · 2026Article
- Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG.bioRxiv : the preprint server for biology · 2026Article
- Digital health technologies for adults with ADHD: a scoping review.Frontiers in digital health · 2026Review
- 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
- Shaping the Future of Psychiatric Neurosurgery: From Connectomic Precision to Technological Integration.Brain sciences · 2025Review
- Exploring voice as a digital phenotype in adults with ADHD.Scientific reports · 2025Article
- Analysis of experiments with high frequency time series responses and the implications for power and sample size.Scientific reports · 2025Article
- Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.Genome research · 2025Review
- Impact of Dataset Size on 3D CNN Performance in Intracranial Hemorrhage Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Resting-State Functional Connectivity Predicts Attention Problems in Children: Evidence from the ABCD Study.NeuroSci · 2024Article
- Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods.Neurology · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Stable multivariate lesion symptom mapping.Aperture neuro · 2024Article
- Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study.Frontiers in neuroscience · 2024Article
- Translating Decades of Neuroscience Research into Diagnostic and Treatment Biomarkers for ADHD.Advances in neurobiology · 2024Review
- The present and future of seizure detection, prediction, and forecasting with machine learning, including the future impact on clinical trials.Frontiers in neurology · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundMotivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors.
methodsWe reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias.
resultsCircular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.
conclusionsHigh classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.
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