ArticleBMC medicine2023
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.
Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Divergent Multimodal Age-Association Profiles Across the Human Brain.Nature communications · 2026Article
- Diffusion models for brain imaging computing: a survey of frameworks and applications.Brain informatics · 2026Review
- Characterizing patients who benefit from mature medical AI models in real-world clinical applications.PLOS digital health · 2026Article
- Neurodevelopmental disorders in children: the role of MRI in early detection and intervention planning.Frontiers in neuroscience · 2026Review
- Computational whole-body-exposome models for global precision brain health.Nature communications · 2025Article
- Risk of bias and low reproducibility in meta-analytic evidence from fast-tracked publications during the coronavirus disease 2019 pandemic.PNAS nexus · 2025Article
- Peer relationship, family factors, and mental health in rural children: a network analysis.Frontiers in psychiatry · 2025Article
- Towards collaborative data science in mental health research: The ECNP neuroimaging network accessible data repository.Neuroscience applied · 2025Article
- Article
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Racial and ethnic socioenvironmental inequity and neuroimaging in psychiatry: a brief review of the past and recommendations for the future.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Article
- Why and how to collect representative study samples in educational neuroscience research.Trends in neuroscience and education · 2024Review
- Geographical and economic influences on neuroimaging modality choice.Royal Society open science · 2024Article
- Our Hopes forJAACAP open · 2023Article
- From maps to models: A survey on the reliability of small studies of task-based fMRI.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.
methodsHere, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.
resultsA global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R
conclusionsTogether, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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