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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Main Authors: Yifeng Yang, Liangyun Hu, Yang Chen, Weidong Gu, Guangwu Lin, YuanZhong Xie, Shengdong Nie
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
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.
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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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