Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity
Abstract To explore the neurobiological heterogeneity within the Clinical High-Risk (CHR) for psychosis population, this study aimed to identify and characterize distinct neurobiological biotypes within CHR using features from resting-state functional networks. A total of 239 participants from the S...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Schizophrenia |
| Online Access: | https://doi.org/10.1038/s41537-025-00565-6 |
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| author | Xiaochen Tang Yanyan Wei Jiaoyan Pang Lihua Xu Huiru Cui Xu Liu Yegang Hu Mingliang Ju Yingying Tang Bin Long Wei Liu Min Su Tianhong Zhang Jijun Wang |
| author_facet | Xiaochen Tang Yanyan Wei Jiaoyan Pang Lihua Xu Huiru Cui Xu Liu Yegang Hu Mingliang Ju Yingying Tang Bin Long Wei Liu Min Su Tianhong Zhang Jijun Wang |
| author_sort | Xiaochen Tang |
| collection | DOAJ |
| description | Abstract To explore the neurobiological heterogeneity within the Clinical High-Risk (CHR) for psychosis population, this study aimed to identify and characterize distinct neurobiological biotypes within CHR using features from resting-state functional networks. A total of 239 participants from the Shanghai At Risk for Psychosis (SHARP) program were enrolled, consisting of 151 CHR individuals and 88 matched healthy controls (HCs). Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. The cognitive function, clinical symptoms, FC patterns, and correlation with neurotransmitter systems of biotype profiles were compared. Three distinct CHR biotypes were identified based on 646 significant ROI-ROI connectivity features, comprising 29.8%, 19.2%, and 51.0% of the CHR sample, respectively. Despite the absence of overall FC differences between CHR and HC groups, each CHR biotype demonstrated unique FC abnormalities. Biotype 1 displayed augmented somatomotor connection, Biotype 2 shown compromised working memory with heightened subcortical and network-specific connectivity, and Biotype 3, characterized by significant negative symptoms, revealed extensive connectivity reductions along with increased limbic-subcortical connectivity. The neurotransmitter correlates differed across biotypes. Biotype 2 revealed an inverse trend to Biotype 3, as increased neurotransmitter concentrations improved functional connectivity in Biotype 2 but reduced it in Biotype 3. The identification of CHR biotypes provides compelling evidence for the early manifestation of heterogeneity within the psychosis spectrum, suggesting that distinct pathophysiological mechanisms may underlie these subgroups. |
| format | Article |
| id | doaj-art-d072b6e1f6d647bbbf995d877c9dcb1d |
| institution | Kabale University |
| issn | 2754-6993 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Schizophrenia |
| spelling | doaj-art-d072b6e1f6d647bbbf995d877c9dcb1d2025-02-09T12:42:05ZengNature PortfolioSchizophrenia2754-69932025-02-0111111010.1038/s41537-025-00565-6Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivityXiaochen Tang0Yanyan Wei1Jiaoyan Pang2Lihua Xu3Huiru Cui4Xu Liu5Yegang Hu6Mingliang Ju7Yingying Tang8Bin Long9Wei Liu10Min Su11Tianhong Zhang12Jijun Wang13Neuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineSchool of Government, Shanghai University of Political Science and LawNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineSchool of Psychology, Shanghai Normal UniversityNingde Rehabilitation HospitalNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineNeuromodulation Center, Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineAbstract To explore the neurobiological heterogeneity within the Clinical High-Risk (CHR) for psychosis population, this study aimed to identify and characterize distinct neurobiological biotypes within CHR using features from resting-state functional networks. A total of 239 participants from the Shanghai At Risk for Psychosis (SHARP) program were enrolled, consisting of 151 CHR individuals and 88 matched healthy controls (HCs). Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. The cognitive function, clinical symptoms, FC patterns, and correlation with neurotransmitter systems of biotype profiles were compared. Three distinct CHR biotypes were identified based on 646 significant ROI-ROI connectivity features, comprising 29.8%, 19.2%, and 51.0% of the CHR sample, respectively. Despite the absence of overall FC differences between CHR and HC groups, each CHR biotype demonstrated unique FC abnormalities. Biotype 1 displayed augmented somatomotor connection, Biotype 2 shown compromised working memory with heightened subcortical and network-specific connectivity, and Biotype 3, characterized by significant negative symptoms, revealed extensive connectivity reductions along with increased limbic-subcortical connectivity. The neurotransmitter correlates differed across biotypes. Biotype 2 revealed an inverse trend to Biotype 3, as increased neurotransmitter concentrations improved functional connectivity in Biotype 2 but reduced it in Biotype 3. The identification of CHR biotypes provides compelling evidence for the early manifestation of heterogeneity within the psychosis spectrum, suggesting that distinct pathophysiological mechanisms may underlie these subgroups.https://doi.org/10.1038/s41537-025-00565-6 |
| spellingShingle | Xiaochen Tang Yanyan Wei Jiaoyan Pang Lihua Xu Huiru Cui Xu Liu Yegang Hu Mingliang Ju Yingying Tang Bin Long Wei Liu Min Su Tianhong Zhang Jijun Wang Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity Schizophrenia |
| title | Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity |
| title_full | Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity |
| title_fullStr | Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity |
| title_full_unstemmed | Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity |
| title_short | Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity |
| title_sort | identifying neurobiological heterogeneity in clinical high risk psychosis a data driven biotyping approach using resting state functional connectivity |
| url | https://doi.org/10.1038/s41537-025-00565-6 |
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