Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis
Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder with complex progression. This study aims to analyze electroencephalography (EEG) connectivity patterns to better understand PD progression and stage of the disease using machine learning. Resting-state, eyes-closed EEG recordin...
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| Format: | Article |
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IOP Publishing
2025-01-01
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| Series: | Journal of Physics: Complexity |
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| Online Access: | https://doi.org/10.1088/2632-072X/adf58a |
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| author | Caroline L Alves Loriz Francisco Sallum Francisco Aparecido Rodrigues Thaise G L de O Toutain Patrícia Maria de Carvalho Aguiar Michael Moeckel |
| author_facet | Caroline L Alves Loriz Francisco Sallum Francisco Aparecido Rodrigues Thaise G L de O Toutain Patrícia Maria de Carvalho Aguiar Michael Moeckel |
| author_sort | Caroline L Alves |
| collection | DOAJ |
| description | Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder with complex progression. This study aims to analyze electroencephalography (EEG) connectivity patterns to better understand PD progression and stage of the disease using machine learning. Resting-state, eyes-closed EEG recordings were acquired from 31 individuals: 16 healthy controls (HCs) and 15 PD patients. The PD group was stratified by disease duration into early-stage (1–3 years, n = 9) and advanced-stage (6–12 years, n = 6). EEG was recorded using a 32-channel Biosemi Active-Two system (512 Hz), with signals segmented into non-overlapping 10 s windows. Functional connectivity matrices were constructed using multiple metrics, including coherence, Pearson, Spearman, canonical correlation, and Ledoit–Wolf shrinkage. Machine learning models were applied for both binary (PD vs HC) and multiclass (HC vs early vs advanced PD) classification. Interpretability was achieved using Shapley Additive Explanations (PD) methodology, and the most discriminative neural connections were statistically validated using the Wilcoxon test with Bonferroni correction. Our approach achieved high accuracy in classifying PD stages, with coherence emerging as the optimal metric for capturing synchronized neural activity. SHAP values revealed critical brain regions and connectivity patterns associated with disease progression. Statistical validation confirmed the significance of these connections across disease stages. Early-stage PD exhibited neural connectivity patterns similar to HCs, while advanced stages showed distinct connectivity changes. The findings highlight the utility of EEG connectivity and machine learning in staging PD, offering insights into PD pathogenesis and progression. SHAP-enhanced model interpretability ensures reliable identification of key neural connections, supporting personalized diagnostics and therapeutic strategies. |
| format | Article |
| id | doaj-art-231e771defcd413fb4cf11b2d96129ab |
| institution | Kabale University |
| issn | 2632-072X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Journal of Physics: Complexity |
| spelling | doaj-art-231e771defcd413fb4cf11b2d96129ab2025-08-20T03:40:13ZengIOP PublishingJournal of Physics: Complexity2632-072X2025-01-016303500610.1088/2632-072X/adf58aDynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysisCaroline L Alves0https://orcid.org/0000-0003-4708-1330Loriz Francisco Sallum1Francisco Aparecido Rodrigues2https://orcid.org/0000-0002-0145-5571Thaise G L de O Toutain3Patrícia Maria de Carvalho Aguiar4Michael Moeckel5Laboratory for Hybrid Modeling, Aschaffenburg University of Applied Sciences , Aschaffenburg, GermanyInstitute of Mathematical and Computer Sciences, University of São Paulo , São Paulo, BrazilInstitute of Mathematical and Computer Sciences, University of São Paulo , São Paulo, BrazilHealth Sciences Institute , Federal University of Bahia, Bahia, BrazilHospital Israelita Albert Einstein , São Paulo, Brazil; Department of Neurology and Neurosurgery, Federal University of São Paulo , SãoPaulo, BrazilLaboratory for Hybrid Modeling, Aschaffenburg University of Applied Sciences , Aschaffenburg, GermanyParkinson’s disease (PD) is a multifactorial neurodegenerative disorder with complex progression. This study aims to analyze electroencephalography (EEG) connectivity patterns to better understand PD progression and stage of the disease using machine learning. Resting-state, eyes-closed EEG recordings were acquired from 31 individuals: 16 healthy controls (HCs) and 15 PD patients. The PD group was stratified by disease duration into early-stage (1–3 years, n = 9) and advanced-stage (6–12 years, n = 6). EEG was recorded using a 32-channel Biosemi Active-Two system (512 Hz), with signals segmented into non-overlapping 10 s windows. Functional connectivity matrices were constructed using multiple metrics, including coherence, Pearson, Spearman, canonical correlation, and Ledoit–Wolf shrinkage. Machine learning models were applied for both binary (PD vs HC) and multiclass (HC vs early vs advanced PD) classification. Interpretability was achieved using Shapley Additive Explanations (PD) methodology, and the most discriminative neural connections were statistically validated using the Wilcoxon test with Bonferroni correction. Our approach achieved high accuracy in classifying PD stages, with coherence emerging as the optimal metric for capturing synchronized neural activity. SHAP values revealed critical brain regions and connectivity patterns associated with disease progression. Statistical validation confirmed the significance of these connections across disease stages. Early-stage PD exhibited neural connectivity patterns similar to HCs, while advanced stages showed distinct connectivity changes. The findings highlight the utility of EEG connectivity and machine learning in staging PD, offering insights into PD pathogenesis and progression. SHAP-enhanced model interpretability ensures reliable identification of key neural connections, supporting personalized diagnostics and therapeutic strategies.https://doi.org/10.1088/2632-072X/adf58abrain networkParkinson diseaseage stagemachine learning |
| spellingShingle | Caroline L Alves Loriz Francisco Sallum Francisco Aparecido Rodrigues Thaise G L de O Toutain Patrícia Maria de Carvalho Aguiar Michael Moeckel Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis Journal of Physics: Complexity brain network Parkinson disease age stage machine learning |
| title | Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis |
| title_full | Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis |
| title_fullStr | Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis |
| title_full_unstemmed | Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis |
| title_short | Dynamical patterns of EEG connectivity unveil Parkinson’s disease progression: insights from machine learning analysis |
| title_sort | dynamical patterns of eeg connectivity unveil parkinson s disease progression insights from machine learning analysis |
| topic | brain network Parkinson disease age stage machine learning |
| url | https://doi.org/10.1088/2632-072X/adf58a |
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