{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/78024"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/78024","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Vaiana, Michael; 0000-0002-4955-4114"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Muldoon, Sarah","Computational and Data Enabled Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-06-28T20:32:49Z","date_published":"2018-06-28T20:32:49Z","updated_at":"2026-07-27T19:05:07Z","subjects":["mathematics","neurosciences","computer science"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/78024","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Muldoon, Sarah","Computational and Data Enabled Sciences"]},{"key":"dc:creator","label":"Author","values":["Vaiana, Michael; 0000-0002-4955-4114"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-06-28T20:32:49Z","2018","2018-05-15 15:06:29"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mathematics","neurosciences","computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/78024"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Real world systems are complex, dynamic and exist across multiple scales. Recent revolutions in data collection and storage have provided researchers with unprecedented access to information about these systems in greater detail and variety than ever before. It is therefore imperative that we develop models and methods that are able to describe and detect changes in these systems through time and across scales. In this dissertation, I present work integrating multilayer networks and computational topology to perform multi-scale analysis of brain activity in epileptic mice. First, I present work providing theoretical advances to multilayer network theory. I prove that multilayer networks suffer from a resolution limit that prevents a popular method of community detection from detecting changes in community structure across layers of the network. I then propose an improvement to the multilayer network model that is more intuitive and helps mitigate the constraints of the resolution limit. Next, I describe work drawing from the tools of topological data analysis using persistent homology to localize and segment cells in biomedical images. Finally, I employing tools from computational topology and multilayer networks to study the evolution of neuronal dynamics in the brains of epileptic mice in the period leading up to seizure. This intricate, multi-scale analysis of brain dynamics enables us to identify evolving functional groups of neurons that drive the progression to a seizure."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics"]}]}],"canonical_facts":{"dc:contributor":["Muldoon, Sarah","Computational and Data Enabled Sciences"],"dc:creator":["Vaiana, Michael; 0000-0002-4955-4114"],"dc:date":["2018-06-28T20:32:49Z","2018","2018-05-15 15:06:29"],"dc:description":["Ph.D.","Real world systems are complex, dynamic and exist across multiple scales. Recent revolutions in data collection and storage have provided researchers with unprecedented access to information about these systems in greater detail and variety than ever before. It is therefore imperative that we develop models and methods that are able to describe and detect changes in these systems through time and across scales. In this dissertation, I present work integrating multilayer networks and computational topology to perform multi-scale analysis of brain activity in epileptic mice. First, I present work providing theoretical advances to multilayer network theory. I prove that multilayer networks suffer from a resolution limit that prevents a popular method of community detection from detecting changes in community structure across layers of the network. I then propose an improvement to the multilayer network model that is more intuitive and helps mitigate the constraints of the resolution limit. Next, I describe work drawing from the tools of topological data analysis using persistent homology to localize and segment cells in biomedical images. Finally, I employing tools from computational topology and multilayer networks to study the evolution of neuronal dynamics in the brains of epileptic mice in the period leading up to seizure. This intricate, multi-scale analysis of brain dynamics enables us to identify evolving functional groups of neurons that drive the progression to a seizure."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/78024"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mathematics","neurosciences","computer science"],"dc:title":["Integrating Network Science and Computational Topology with Applications in Neuroscience Data Analytics"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:07Z"}