Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models
IntroductionParkinson’s disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.MethodsHere, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-spec...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1453852/full |
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| author | Benedictor Alexander Nguchu Benedictor Alexander Nguchu Yifei Han Yanming Wang Peter Shaw |
| author_facet | Benedictor Alexander Nguchu Benedictor Alexander Nguchu Yifei Han Yanming Wang Peter Shaw |
| author_sort | Benedictor Alexander Nguchu |
| collection | DOAJ |
| description | IntroductionParkinson’s disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.MethodsHere, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. Finally, we assessed whether PD subtypes described through network-specific patterns are dependent on the APOE genotype.ResultsUsing data from 2396 subjects, we show that PD (n=2037) is highly associated with APOE ϵ2/ϵ4. Our findings reveal a significant DAT deficit in the left and right structures of the caudate, putamen, and anterior putamen in subjects with PD compared to subjects with SWEDD(n=137) or HC(n=222), and that APOE ϵ2/ϵ4 may accelerate DAT deficits and brain alterations in both PD and SWEDD. Furthermore, clinical symptoms of PD in subjects (SWEDD), which hardly validated by DAT scan data, can be explained by variations in APOE genotypes and other brain features beyond DAT. We show the existence of three networks states for the whole data, with the first network state describing the subjects in HC, while the remaining two network states describing the two PD subtypes—one network state typified by a mildly sparsely connected network (patterns) and the other network state characterized by a more intensified sparsity in their network. We also show that the two subtypes of PD are characterized by distinctly different levels of total gray matter volume and DAT deficit. ML models show that features extracted from brain structure and network patterns can serve as reliable biomarkers for PD and its subtypes, with the highest performance (100% AUC, 99.3% accuracy, 0.993 F1) demonstrated by the fine-tuned SVM model.ConclusionOur findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may stem from genetic factors. Such heterogeneity can be characterized by ML models and optimally mapped into network states, providing new insights to consider when developing personalized drugs. |
| format | Article |
| id | doaj-art-ed2a8ff2fc0e4b699dc11e0f746a3131 |
| institution | DOAJ |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-ed2a8ff2fc0e4b699dc11e0f746a31312025-08-20T03:11:33ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-02-011610.3389/fpsyt.2025.14538521453852Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine modelsBenedictor Alexander Nguchu0Benedictor Alexander Nguchu1Yifei Han2Yanming Wang3Peter Shaw4Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, ChinaSchool of Biomedical Engineering, School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, ChinaCenter for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, ChinaIntroductionParkinson’s disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.MethodsHere, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. Finally, we assessed whether PD subtypes described through network-specific patterns are dependent on the APOE genotype.ResultsUsing data from 2396 subjects, we show that PD (n=2037) is highly associated with APOE ϵ2/ϵ4. Our findings reveal a significant DAT deficit in the left and right structures of the caudate, putamen, and anterior putamen in subjects with PD compared to subjects with SWEDD(n=137) or HC(n=222), and that APOE ϵ2/ϵ4 may accelerate DAT deficits and brain alterations in both PD and SWEDD. Furthermore, clinical symptoms of PD in subjects (SWEDD), which hardly validated by DAT scan data, can be explained by variations in APOE genotypes and other brain features beyond DAT. We show the existence of three networks states for the whole data, with the first network state describing the subjects in HC, while the remaining two network states describing the two PD subtypes—one network state typified by a mildly sparsely connected network (patterns) and the other network state characterized by a more intensified sparsity in their network. We also show that the two subtypes of PD are characterized by distinctly different levels of total gray matter volume and DAT deficit. ML models show that features extracted from brain structure and network patterns can serve as reliable biomarkers for PD and its subtypes, with the highest performance (100% AUC, 99.3% accuracy, 0.993 F1) demonstrated by the fine-tuned SVM model.ConclusionOur findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may stem from genetic factors. Such heterogeneity can be characterized by ML models and optimally mapped into network states, providing new insights to consider when developing personalized drugs.https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1453852/fullParkinson’s diseasePD heterogeneityPD subtypesAPOE genotypeclustering algorithmmachine learning models |
| spellingShingle | Benedictor Alexander Nguchu Benedictor Alexander Nguchu Yifei Han Yanming Wang Peter Shaw Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models Frontiers in Psychiatry Parkinson’s disease PD heterogeneity PD subtypes APOE genotype clustering algorithm machine learning models |
| title | Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models |
| title_full | Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models |
| title_fullStr | Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models |
| title_full_unstemmed | Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models |
| title_short | Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models |
| title_sort | identifying network state based parkinson s disease subtypes using clustering and support vector machine models |
| topic | Parkinson’s disease PD heterogeneity PD subtypes APOE genotype clustering algorithm machine learning models |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1453852/full |
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