Revealing multiple biological subtypes of schizophrenia through a data-driven approach
Rattachement africain : cn, us, jp, ca. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
INTRODUCTION: The brain imaging subtypes of schizophrenia have been widely investigated using data-driven approaches. However, the heterogeneity of SZ in multiple biological data is largely unknown. METHODS: A data-driven model was used to classify brain imaging, gut microbiota, and brain-gut fusion data obtained through a dot product fusion method, identifying significant subtypes and calculating their correlations with clinical symptoms and cognitive performance. RESULTS: These subtypes remain relatively independent and demonstrate typical features and biomarkers, which are significantly associated with clinical symptoms and cognitive performance. Two brain subtypes with opposite structural and functional changes are identified: (1) a structural variant-dominant brain subtype with negative symptoms and cognitive deficits and (2) a functional alteration-dominant brain subtype with positive symptoms. The three gut subtypes include the following: (1) Collinsella-dominant; (2) Prevotella-dominant with positive symptoms; and (3) Streptococcus-dominant. Two brain-gut subtypes show different abnormalities in brain‒genus linkages: (1) strong connectivity of "brain function in the temporal and parietal lobes-Prevotella" with reduced attention scores and (2) strong connectivity of "brain structure and function in the frontal and parietal lobes-multiple genera" with positive symptoms. Notably, brain subtypes and brain-gut subtypes are most relevant to clinical symptoms, whereas gut subtypes reveal more cognitive biomarkers. CONCLUSION: These findings show the potential to identify multiple biological subtypes with distinct biomarkers, thereby suggesting the possibility of personalized and precise treatment for SZ patients.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Revealing multiple biological subtypes of schizophrenia through a data-driven approach
- Date Crossref
- 02/05/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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South China University of Technology National Engineering Research Center for Tissue Restoration and Reconstruction pays non établi dans la noticeUniversité ou école supérieure
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Guangzhou Medical University Department of Psychiatry pays non établi dans la noticeUniversité ou école supérieure
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New Jersey Institute of Technology Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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First Affiliated Hospital of Guangzhou Medical University pays non établi dans la noticeÉtablissement de santé
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Guangdong 999 Brain Hospital pays non établi dans la noticeÉtablissement de santé
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Tohoku University Department of Nuclear Medicine and Radiology pays non établi dans la noticeUniversité ou école supérieure
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Institute of Aging pays non établi dans la noticeStructure de recherche
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School of Biomedical Sciences and Engineering pays non établi dans la noticeUniversité ou école supérieure
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Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders pays non établi dans la noticeStructure de recherche
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Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia pays non établi dans la noticeStructure de recherche
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School of Material Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
National Engineering Research Center for Tissue Restoration and Reconstruction — South China University of Technology, Department of Psychiatry — Guangzhou Medical University et Department of Biomedical Engineering — New Jersey Institute of Technology, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.