{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86684"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86684","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Variability of Brain Networks in Healthy and Pathological Populations","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Nakuci, Johan"],"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","Neuroscience"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:36:27Z","date_published":"2025-02-21T21:36:27Z","updated_at":"2026-07-27T19:05:34Z","subjects":["neurosciences"],"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/10477/86684","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Muldoon, Sarah","Neuroscience"]},{"key":"dc:creator","label":"Author","values":["Nakuci, Johan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:36:27Z","2020"]},{"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":["neurosciences"]}]},{"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/10477/86684"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","The merger of network theory with neuroscience has created a new analytical field within neuroscience that has drastically increased our understanding of the human brain and how it functions. Overall, network neuroscience has expanded to include data from single-cell recordings to macroscopic brain regions. This new analytical methodology has deepened our understanding of the brain at multiple levels of organization from neuronal circuits to the whole brain and includes both functional and structural connections. Despite the wealth of knowledge gained, the statistical methods applied have had a primary focus of identifying the mean group effect, or central tendency, in an attempt to identify normal brain function. In turn, a common practice has been to treat variability as noise or measurement error, with the assumption that the values estimated from the group will also apply to the individual. However, a plethora of factors from genetic to environmental can contribute to structural and functional network variability across human brains to the extent that even after correcting for nuisance factors, there remains substantial 1) within-subject variability; 2) between-subject variability, and 3) inter-trial variability within a task. The work comprising this doctoral thesis aims to give a better understanding of these three different types of variability in the context of brain networks. The results of this work indicate that, within a given individual, structural networks are more consistent over time than functional networks. However, even in structural brain networks, we find variable connectivity patterns amongst subjects within both healthy and disease populations. In heathy populations, differences in structural variability can be related to gender. In a rat model of traumatic brain injury, we also show that the population can be grouped into two subpopulations showing distinct patterns of modified brain connectivity after injury and that one of these subpopulations might potentially have an increased propensity to develop epilepsy. Finally, when examining inter-task variability, we show that during a working memory task, one can identify distinct subtypes describing the spatial-temporal pattern of brain activity in response to the task, suggesting the possibility that difference cognitive streams might be activated to perform the same tasks during different instances. Taken together, these results indicate that statistical analysis of brain networks should move away from approaches that identify the mean group effect and instead develop methodologies that incorporate a more nuanced approach that allows for the presence of subpopulations within the data.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Variability of Brain Networks in Healthy and Pathological Populations"]}]}],"canonical_facts":{"dc:contributor":["Muldoon, Sarah","Neuroscience"],"dc:creator":["Nakuci, Johan"],"dc:date":["2025-02-21T21:36:27Z","2020"],"dc:description":["Ph.D.","The merger of network theory with neuroscience has created a new analytical field within neuroscience that has drastically increased our understanding of the human brain and how it functions. Overall, network neuroscience has expanded to include data from single-cell recordings to macroscopic brain regions. This new analytical methodology has deepened our understanding of the brain at multiple levels of organization from neuronal circuits to the whole brain and includes both functional and structural connections. Despite the wealth of knowledge gained, the statistical methods applied have had a primary focus of identifying the mean group effect, or central tendency, in an attempt to identify normal brain function. In turn, a common practice has been to treat variability as noise or measurement error, with the assumption that the values estimated from the group will also apply to the individual. However, a plethora of factors from genetic to environmental can contribute to structural and functional network variability across human brains to the extent that even after correcting for nuisance factors, there remains substantial 1) within-subject variability; 2) between-subject variability, and 3) inter-trial variability within a task. The work comprising this doctoral thesis aims to give a better understanding of these three different types of variability in the context of brain networks. The results of this work indicate that, within a given individual, structural networks are more consistent over time than functional networks. However, even in structural brain networks, we find variable connectivity patterns amongst subjects within both healthy and disease populations. In heathy populations, differences in structural variability can be related to gender. In a rat model of traumatic brain injury, we also show that the population can be grouped into two subpopulations showing distinct patterns of modified brain connectivity after injury and that one of these subpopulations might potentially have an increased propensity to develop epilepsy. Finally, when examining inter-task variability, we show that during a working memory task, one can identify distinct subtypes describing the spatial-temporal pattern of brain activity in response to the task, suggesting the possibility that difference cognitive streams might be activated to perform the same tasks during different instances. Taken together, these results indicate that statistical analysis of brain networks should move away from approaches that identify the mean group effect and instead develop methodologies that incorporate a more nuanced approach that allows for the presence of subpopulations within the data.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86684"],"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":["neurosciences"],"dc:title":["Variability of Brain Networks in Healthy and Pathological Populations"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:34Z"}