{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/38599"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/38599","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"MRI microstructure and morphology enable machine learning-based prediction of freezing of gait in Parkinson’s disease.","abstract":"Freezing of gait (FOG) in Parkinson’s disease (PD) is a debilitating, often treatment-resistant symptom impairing mobility and quality of life. Early identification of high-risk patients may enable targeted intervention, yet few prognostic biomarkers exist. In this thesis, we developed a machine learning classifier to predict FOG onset from baseline structural neuroimaging of 106 de novo PD patients from the Parkinson’s Progression Marker Initiative. The trained model demonstrated high accuracy (AUC=0.91, Sensitivity=0.94, Specificity=0.80) on unseen data. Model explainability analysis revealed key microstructural and morphometric features within limbic, executive, and visual networks, supporting the idea that multi-network vulnerabilities may contribute to the onset of FOG. Our approach, which relied solely on accessible imaging data while managing confounds, enhances scalability and interpretability compared to previous prognostic frameworks. Future work should validate these biomarkers in larger, more diverse cohorts and explore the role of a prognostic model in enriching clinical trials for preventative interventions.","abstract_html":"Freezing of gait (FOG) in Parkinson’s disease (PD) is a debilitating, often treatment-resistant symptom impairing mobility and quality of life. Early identification of high-risk patients may enable targeted intervention, yet few prognostic biomarkers exist. In this thesis, we developed a machine learning classifier to predict FOG onset from baseline structural neuroimaging of 106 de novo PD patients from the Parkinson’s Progression Marker Initiative. The trained model demonstrated high accuracy (AUC=0.91, Sensitivity=0.94, Specificity=0.80) on unseen data. Model explainability analysis revealed key microstructural and morphometric features within limbic, executive, and visual networks, supporting the idea that multi-network vulnerabilities may contribute to the onset of FOG. Our approach, which relied solely on accessible imaging data while managing confounds, enhances scalability and interpretability compared to previous prognostic frameworks. Future work should validate these biomarkers in larger, more diverse cohorts and explore the role of a prognostic model in enriching clinical trials for preventative interventions.","abstract_has_math":false,"creators":["Rothery, Nathan"],"institution":"The University of Western Ontario","degree_name":"M Sc","degree_level":null,"degree_discipline":"Neuroscience","degree_department":null,"school":null,"contributors":[],"advisors":["MacDonald, Penny","Daley, Mark"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-19","date_published":"2025-08-19","updated_at":"2026-07-27T21:55:54Z","subjects":["Parkinson’s disease (PD)","freezing of gait (FOG)","prognosis","biomarkers","machine learning","magnetic resonance imaging (MRI)","diffusion tensor imaging (DTI)"],"languages":["en"],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/38599","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["MacDonald, Penny","Daley, Mark"]},{"key":"dc:creator","label":"Author","values":["Rothery, Nathan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-08-26T17:55:43Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-26T17:55:43Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-08-19"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Neuroscience","Machine Learning in Health and Biomedical Sciences"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Western Ontario"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Parkinson’s disease (PD)","freezing of gait (FOG)","prognosis","biomarkers","machine learning","magnetic resonance imaging (MRI)","diffusion tensor imaging (DTI)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-NoDerivatives 4.0 International"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/38599"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Freezing of gait (FOG) in Parkinson’s disease (PD) is a debilitating, often treatment-resistant symptom impairing mobility and quality of life. Early identification of high-risk patients may enable targeted intervention, yet few prognostic biomarkers exist. In this thesis, we developed a machine learning classifier to predict FOG onset from baseline structural neuroimaging of 106 de novo PD patients from the Parkinson’s Progression Marker Initiative. The trained model demonstrated high accuracy (AUC=0.91, Sensitivity=0.94, Specificity=0.80) on unseen data. Model explainability analysis revealed key microstructural and morphometric features within limbic, executive, and visual networks, supporting the idea that multi-network vulnerabilities may contribute to the onset of FOG. Our approach, which relied solely on accessible imaging data while managing confounds, enhances scalability and interpretability compared to previous prognostic frameworks. Future work should validate these biomarkers in larger, more diverse cohorts and explore the role of a prognostic model in enriching clinical trials for preventative interventions."]},{"key":"dc:title","label":"Title","values":["MRI microstructure and morphology enable machine learning-based prediction of freezing of gait in Parkinson’s disease."]}]}],"canonical_facts":{"dc:contributor.advisor":["MacDonald, Penny","Daley, Mark"],"dc:creator":["Rothery, Nathan"],"dc:date.accessioned":["2025-08-26T17:55:43Z"],"dc:date.available":["2025-08-26T17:55:43Z"],"dc:date.issued":["2025-08-19"],"dc:description.abstract":["Freezing of gait (FOG) in Parkinson’s disease (PD) is a debilitating, often treatment-resistant symptom impairing mobility and quality of life. Early identification of high-risk patients may enable targeted intervention, yet few prognostic biomarkers exist. 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