In this thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific cerebrovascular conditions, allowing us to focus on WMH patterns in a healthy aging population. We have interrogated the association of location-specific WMHs with a variety of demographic, clinical, and genetic factors. Our multivariable regression analysis evaluates the strength and nature of the associations between these factors and WMH distribution. The analysis integrates variables such as age, sex, smoking habits, medication usage for hypertension and cholesterol, intima media thickness (IMT), Alzheimer's disease polygenic risk score (AD PRS), circulatory diseases and other clinical measures that may illuminate the origins and progression of WMH. We have uncovered significant associations of location-specific WMH with risk factors such as IMT, circulatory diseases like hypertension, hypotension and atrial fibrillation. Moreover, we discovered that WMH patterns bear significant links to AD PRS, suggesting shared pathogenic pathways between Alzheimer’s disease and WMH. The aim of the thesis is to refine the diagnostic accuracy for WMH-related conditions and to expand our comprehension of how WMH location intersects with cognitive deterioration and overall brain health.
","abstract_html":"<p>In this thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific cerebrovascular conditions, allowing us to focus on WMH patterns in a healthy aging population. We have interrogated the association of location-specific WMHs with a variety of demographic, clinical, and genetic factors. Our multivariable regression analysis evaluates the strength and nature of the associations between these factors and WMH distribution. The analysis integrates variables such as age, sex, smoking habits, medication usage for hypertension and cholesterol, intima media thickness (IMT), Alzheimer's disease polygenic risk score (AD PRS), circulatory diseases and other clinical measures that may illuminate the origins and progression of WMH. We have uncovered significant associations of location-specific WMH with risk factors such as IMT, circulatory diseases like hypertension, hypotension and atrial fibrillation. Moreover, we discovered that WMH patterns bear significant links to AD PRS, suggesting shared pathogenic pathways between Alzheimer’s disease and WMH. The aim of the thesis is to refine the diagnostic accuracy for WMH-related conditions and to expand our comprehension of how WMH location intersects with cognitive deterioration and overall brain health.</p>","abstract_has_math":false,"creators":["Roy, Sugandha"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Biomedical Engineering","degree_department":null,"school":null,"contributors":["Professor Aristeidis Sotiras, Assistant Professor of Radiology, Assistant Professor of Institute for Informatics","Professor Aimilia Gastounioti, Professor Arash Nazeri"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-13T07:00:00Z","date_published":"2024-05-13T07:00:00Z","updated_at":"2026-07-24T06:12:58Z","subjects":["Neuroimaging","Machine Learning","Matrix decomposition","Data analysis","White Matter Hyperintensities","Brain aging","Alzheimer's Disease","Cerebral Small Vessel Disease","Bioelectrical and Neuroengineering","Bioimaging and Biomedical Optics","Biomedical Engineering and Bioengineering","Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/1019"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/1019","href":"https://openscholarship.wustl.edu/eng_etds/1019","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/98sw-qz50","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Professor Aristeidis Sotiras, Assistant Professor of Radiology, Assistant Professor of Institute for Informatics","Professor Aimilia Gastounioti, Professor Arash Nazeri"]},{"key":"dc:creator","label":"Author","values":["Roy, Sugandha"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-08T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biomedical Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neuroimaging","Machine Learning","Matrix decomposition","Data analysis","White Matter Hyperintensities","Brain aging","Alzheimer's Disease","Cerebral Small Vessel Disease","Bioelectrical and Neuroengineering","Bioimaging and Biomedical Optics","Biomedical Engineering and Bioengineering","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/98sw-qz50","https://openscholarship.wustl.edu/eng_etds/1019"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific cerebrovascular conditions, allowing us to focus on WMH patterns in a healthy aging population. We have interrogated the association of location-specific WMHs with a variety of demographic, clinical, and genetic factors. Our multivariable regression analysis evaluates the strength and nature of the associations between these factors and WMH distribution. The analysis integrates variables such as age, sex, smoking habits, medication usage for hypertension and cholesterol, intima media thickness (IMT), Alzheimer's disease polygenic risk score (AD PRS), circulatory diseases and other clinical measures that may illuminate the origins and progression of WMH. We have uncovered significant associations of location-specific WMH with risk factors such as IMT, circulatory diseases like hypertension, hypotension and atrial fibrillation. Moreover, we discovered that WMH patterns bear significant links to AD PRS, suggesting shared pathogenic pathways between Alzheimer’s disease and WMH. The aim of the thesis is to refine the diagnostic accuracy for WMH-related conditions and to expand our comprehension of how WMH location intersects with cognitive deterioration and overall brain health.
"]},{"key":"dc:title","label":"Title","values":["Data-Driven Insights into Spatial Patterns and Disease Etiologies of White Matter Hyperintensities"]}]}],"canonical_facts":{"dc:contributor":["Professor Aristeidis Sotiras, Assistant Professor of Radiology, Assistant Professor of Institute for Informatics","Professor Aimilia Gastounioti, Professor Arash Nazeri"],"dc:creator":["Roy, Sugandha"],"dc:date.available":["2026-05-08T07:00:00Z"],"dc:description.abstract":["In this thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific cerebrovascular conditions, allowing us to focus on WMH patterns in a healthy aging population. We have interrogated the association of location-specific WMHs with a variety of demographic, clinical, and genetic factors. Our multivariable regression analysis evaluates the strength and nature of the associations between these factors and WMH distribution. The analysis integrates variables such as age, sex, smoking habits, medication usage for hypertension and cholesterol, intima media thickness (IMT), Alzheimer's disease polygenic risk score (AD PRS), circulatory diseases and other clinical measures that may illuminate the origins and progression of WMH. We have uncovered significant associations of location-specific WMH with risk factors such as IMT, circulatory diseases like hypertension, hypotension and atrial fibrillation. Moreover, we discovered that WMH patterns bear significant links to AD PRS, suggesting shared pathogenic pathways between Alzheimer’s disease and WMH. The aim of the thesis is to refine the diagnostic accuracy for WMH-related conditions and to expand our comprehension of how WMH location intersects with cognitive deterioration and overall brain health.
"],"dc:identifier":["https://doi.org/10.7936/98sw-qz50","https://openscholarship.wustl.edu/eng_etds/1019"],"dc:language":["English (en)"],"dc:rights":["I have not registered my thesis with the U.S. Copyright Office, but intend to later."],"dc:subject":["Neuroimaging","Machine Learning","Matrix decomposition","Data analysis","White Matter Hyperintensities","Brain aging","Alzheimer's Disease","Cerebral Small Vessel Disease","Bioelectrical and Neuroengineering","Bioimaging and Biomedical Optics","Biomedical Engineering and Bioengineering","Engineering"],"dc:title":["Data-Driven Insights into Spatial Patterns and Disease Etiologies of White Matter Hyperintensities"],"thesis:degree_discipline":["Biomedical Engineering","McKelvey School of Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T06:12:58Z"}