ArticleiScience2024
More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis.bioRxiv : the preprint server for biology · 2026Article
- Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia.IEEE transactions on medical imaging · 2025Article
- Unraveling the Neural Landscape of Mental Disorders Using Double Functional Independent Primitives.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.Schizophrenia bulletin · 2025Review
- A prior-knowledge-guided feature selection method and its application to biomarker identification of schizophrenia.Network neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs).bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.
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