Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume
Abstract Background The thalamus plays a crucial role in sensory processing, emotional regulation, and cognitive functions, and its dysregulation may be implicated in psychosis. The aim of the present study was to examine the differences in thalamic subregional volumes between pediatric bipolar diso...
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2025-06-01
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| Online Access: | https://doi.org/10.1186/s12888-025-07018-5 |
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| author | Weijia Gao Kejing Zhang Qing Jiao Linyan Su Dong Cui Shaojia Lu Rongwang Yang |
| author_facet | Weijia Gao Kejing Zhang Qing Jiao Linyan Su Dong Cui Shaojia Lu Rongwang Yang |
| author_sort | Weijia Gao |
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| description | Abstract Background The thalamus plays a crucial role in sensory processing, emotional regulation, and cognitive functions, and its dysregulation may be implicated in psychosis. The aim of the present study was to examine the differences in thalamic subregional volumes between pediatric bipolar disorder patients with (P-PBD) and without psychotic symptoms (NP-PBD). Methods Participants including 28 P-PBD, 26 NP-PBD, and 18 healthy controls (HCs) underwent structural magnetic resonance imaging (sMRI) scanning using a 3.0T MRI scanner. All T1-weighted imaging data were processed by FreeSurfer 7.4.0 software. The volumetric differences of thalamic subregions among three groups were compared by using analyses of covariance (ANCOVA) and post-hoc analyses. Additionally, we applied a standard support vector classification (SVC) model for pairwise comparison among the three groups to identify brain regions with significant volumetric differences. Results The ANCOVA revealed that significant volumetric differences were observed in the left pulvinar anterior (L_PuA) and left reuniens medial ventral (L_MV-re) thalamus among three groups. Post-hoc analysis revealed that patients with P-PBD exhibited decreased volumes in the L_PuA and L_MV-re when compared to the NP-PBD group and HCs, respectively. Furthermore, the SVC model revealed that the L_MV-re volume exhibited the best capacity to discriminate P-PBD from NP-PBD and HCs. Conclusion The present findings demonstrated that reduced thalamic subregional volumes in the L_PuA and L_MV-re might be associated with psychotic symptoms in PBD. |
| format | Article |
| id | doaj-art-89634e850c6d4be2981473dc682c61d3 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
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| series | BMC Psychiatry |
| spelling | doaj-art-89634e850c6d4be2981473dc682c61d32025-08-20T02:05:42ZengBMCBMC Psychiatry1471-244X2025-06-0125111110.1186/s12888-025-07018-5Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volumeWeijia Gao0Kejing Zhang1Qing Jiao2Linyan Su3Dong Cui4Shaojia Lu5Rongwang Yang6Department of Child Psychology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children’s Regional Medical CenterDepartment of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang Key Laboratory of Precision Psychiatry, Zhejiang Engineering Center for Mathematical Mental HealthSchool of Radiology, Shandong First Medical University & Shandong Academy of Medical SciencesMental Health Institute, The Second Xiangya Hospital of Central South University, Key Laboratory of Psychiatry and Mental Health of Hunan Province, National Technology Institute of PsychiatrySchool of Radiology, Shandong First Medical University & Shandong Academy of Medical SciencesDepartment of Psychiatry, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang Key Laboratory of Precision Psychiatry, Zhejiang Engineering Center for Mathematical Mental HealthDepartment of Child Psychology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children’s Regional Medical CenterAbstract Background The thalamus plays a crucial role in sensory processing, emotional regulation, and cognitive functions, and its dysregulation may be implicated in psychosis. The aim of the present study was to examine the differences in thalamic subregional volumes between pediatric bipolar disorder patients with (P-PBD) and without psychotic symptoms (NP-PBD). Methods Participants including 28 P-PBD, 26 NP-PBD, and 18 healthy controls (HCs) underwent structural magnetic resonance imaging (sMRI) scanning using a 3.0T MRI scanner. All T1-weighted imaging data were processed by FreeSurfer 7.4.0 software. The volumetric differences of thalamic subregions among three groups were compared by using analyses of covariance (ANCOVA) and post-hoc analyses. Additionally, we applied a standard support vector classification (SVC) model for pairwise comparison among the three groups to identify brain regions with significant volumetric differences. Results The ANCOVA revealed that significant volumetric differences were observed in the left pulvinar anterior (L_PuA) and left reuniens medial ventral (L_MV-re) thalamus among three groups. Post-hoc analysis revealed that patients with P-PBD exhibited decreased volumes in the L_PuA and L_MV-re when compared to the NP-PBD group and HCs, respectively. Furthermore, the SVC model revealed that the L_MV-re volume exhibited the best capacity to discriminate P-PBD from NP-PBD and HCs. Conclusion The present findings demonstrated that reduced thalamic subregional volumes in the L_PuA and L_MV-re might be associated with psychotic symptoms in PBD.https://doi.org/10.1186/s12888-025-07018-5Pediatric bipolar disorderPsychotic symptomThalamusSubregional volumeMachine learning |
| spellingShingle | Weijia Gao Kejing Zhang Qing Jiao Linyan Su Dong Cui Shaojia Lu Rongwang Yang Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume BMC Psychiatry Pediatric bipolar disorder Psychotic symptom Thalamus Subregional volume Machine learning |
| title | Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume |
| title_full | Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume |
| title_fullStr | Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume |
| title_full_unstemmed | Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume |
| title_short | Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume |
| title_sort | machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume |
| topic | Pediatric bipolar disorder Psychotic symptom Thalamus Subregional volume Machine learning |
| url | https://doi.org/10.1186/s12888-025-07018-5 |
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