Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach
ObjectiveThis study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson’s disease (PD).MethodsWe leveraged a comp...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1510192/full |
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| author | Yifeng Yang Liangyun Hu Yang Chen Weidong Gu Guangwu Lin YuanZhong Xie Shengdong Nie |
| author_facet | Yifeng Yang Liangyun Hu Yang Chen Weidong Gu Guangwu Lin YuanZhong Xie Shengdong Nie |
| author_sort | Yifeng Yang |
| collection | DOAJ |
| description | ObjectiveThis study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson’s disease (PD).MethodsWe leveraged a comprehensive dataset from the Parkinson’s Progression Markers Initiative (PPMI), which includes high-resolution neuroimaging data, genetic single-nucleotide polymorphism (SNP) profiles, and detailed clinical information from individuals with early-stage PD and healthy controls. Two multi-modal fusion strategies were used: feature-level fusion, where we employed a hybrid feature selection algorithm combining Fisher discriminant analysis, an ensemble Lasso (EnLasso) method, and partial least squares (PLS) regression to identify and integrate the most informative features from neuroimaging and genetic data; and decision-level fusion, where we developed an adaptive ensemble stacking (AE_Stacking) model to synergistically integrate the predictions from multiple base classifiers trained on individual modalities.ResultsThe AE_Stacking model achieving the highest average balanced accuracy of 95.36% and an area under the receiver operating characteristic curve (AUC) of 0.974, significantly outperforming feature-level fusion and single-modal models (p < 0.05). Furthermore, by analyzing the features selected across multiple iterations of our models, we identified stable brain region features [lh 6r (FD) and rh 46 (GI)] and key genetic markers (rs356181 and rs2736990 SNPs within the SNCA gene region; rs213202 SNP within the VPS52 gene region), highlighting their potential as reliable early diagnostic indicators for the disease.ConclusionThe AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder. |
| format | Article |
| id | doaj-art-fc1e28b328a043ff8637de5bd97ca26d |
| institution | Kabale University |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-fc1e28b328a043ff8637de5bd97ca26d2025-02-04T06:31:59ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-02-011710.3389/fnagi.2025.15101921510192Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approachYifeng Yang0Liangyun Hu1Yang Chen2Weidong Gu3Guangwu Lin4YuanZhong Xie5Shengdong Nie6Department of Medical Imaging, Huadong Hospital, Fudan University, Shanghai, ChinaCenter for Functional Neurosurgery, RuiJin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Anesthesiology, Huadong Hospital, Fudan University, Shanghai, ChinaDepartment of Medical Imaging, Huadong Hospital, Fudan University, Shanghai, ChinaMedical Imaging Center, Taian Central Hospital, Shandong, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaObjectiveThis study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson’s disease (PD).MethodsWe leveraged a comprehensive dataset from the Parkinson’s Progression Markers Initiative (PPMI), which includes high-resolution neuroimaging data, genetic single-nucleotide polymorphism (SNP) profiles, and detailed clinical information from individuals with early-stage PD and healthy controls. Two multi-modal fusion strategies were used: feature-level fusion, where we employed a hybrid feature selection algorithm combining Fisher discriminant analysis, an ensemble Lasso (EnLasso) method, and partial least squares (PLS) regression to identify and integrate the most informative features from neuroimaging and genetic data; and decision-level fusion, where we developed an adaptive ensemble stacking (AE_Stacking) model to synergistically integrate the predictions from multiple base classifiers trained on individual modalities.ResultsThe AE_Stacking model achieving the highest average balanced accuracy of 95.36% and an area under the receiver operating characteristic curve (AUC) of 0.974, significantly outperforming feature-level fusion and single-modal models (p < 0.05). Furthermore, by analyzing the features selected across multiple iterations of our models, we identified stable brain region features [lh 6r (FD) and rh 46 (GI)] and key genetic markers (rs356181 and rs2736990 SNPs within the SNCA gene region; rs213202 SNP within the VPS52 gene region), highlighting their potential as reliable early diagnostic indicators for the disease.ConclusionThe AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1510192/fullParkinson’s diseaseimaging genomicsstable feature selectionmulti-modal fusionmachine learning |
| spellingShingle | Yifeng Yang Liangyun Hu Yang Chen Weidong Gu Guangwu Lin YuanZhong Xie Shengdong Nie Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach Frontiers in Aging Neuroscience Parkinson’s disease imaging genomics stable feature selection multi-modal fusion machine learning |
| title | Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach |
| title_full | Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach |
| title_fullStr | Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach |
| title_full_unstemmed | Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach |
| title_short | Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach |
| title_sort | identification of parkinson s disease using mri and genetic data from the ppmi cohort an improved machine learning fusion approach |
| topic | Parkinson’s disease imaging genomics stable feature selection multi-modal fusion machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1510192/full |
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