{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Xing Y"],"funding":["NIBIB NIH HHS","NIMH NIH HHS","National Natural Science Foundation of China","National Institutes of Health","NIH HHS"],"pagination":["109319"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10933544"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(3)"],"pubmed_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."],"journal":["iScience"],"pubmed_title":["More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method."],"pmcid":["PMC10933544"],"funding_grant_id":["S10 OD023696","RF1 MH123163","R01 MH118695","R01 EB015611","R01 MH123610"],"pubmed_authors":["Pearlson GD","Xing Y","Calhoun VD","Du Y","van Erp TGM","Kochunov P"],"additional_accession":[]},"is_claimable":false,"name":"More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-06-25T03:20:10.786Z","creation":"2026-06-25T03:08:07.654Z"},"accession":"S-EPMC10933544","cross_references":{"pubmed":["38482500"],"doi":["10.1016/j.isci.2024.109319"]}}