{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24259"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24259","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Statistical methods for fMRI data analysis","abstract":"Since the early 1990s, functional magnetic resonance imaging (fMRI) has dominated the brain mapping field and it has been proved a powerful tool for mapping human brain functions. The fMRI is a high spatial-temporal resolution medical-imaging modality, which means the data structure is complicated and the data size is huge. These features of fMRI data pose some challenges to traditional statistical methods which focus on data with smal sample size and simple data structure. The functional activation detection and functional connectivity network analysis by using fMRI are two important research topics in the neuroscience. In this work, we present three different statistical methods, corresponding to three chapters, for the activation detection and network discovery. In the first part, we present a spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation; in the second part, we propose a model based clustering method to detect the functional connectivity network; in the third part, we present a general and novel statistical framework for robust and more complete estimation of brain functional connectivity based on correlation analysis and hypothesis testing. The complicated data features are taken into account in the three algorithms.","abstract_html":"Since the early 1990s, functional magnetic resonance imaging (fMRI) has dominated the brain mapping field and it has been proved a powerful tool for mapping human brain functions. The fMRI is a high spatial-temporal resolution medical-imaging modality, which means the data structure is complicated and the data size is huge. These features of fMRI data pose some challenges to traditional statistical methods which focus on data with smal sample size and simple data structure. The functional activation detection and functional connectivity network analysis by using fMRI are two important research topics in the neuroscience. In this work, we present three different statistical methods, corresponding to three chapters, for the activation detection and network discovery. In the first part, we present a spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation; in the second part, we propose a model based clustering method to detect the functional connectivity network; in the third part, we present a general and novel statistical framework for robust and more complete estimation of brain functional connectivity based on correlation analysis and hypothesis testing. The complicated data features are taken into account in the three algorithms.","abstract_has_math":false,"creators":["Xia, Jing"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Wang, Michelle Y.","Liang, Feng","Simpson, Douglas G.","Marden, John I."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T14:56:20Z","date_published":"2011-05-25T14:56:20Z","updated_at":"2026-07-22T22:25:23Z","subjects":["Functional magnetic resonance imaging (fMRI)","Functional Activation","Functional Connectivity","Bayesian"],"languages":["en"],"rights":["Copyright 2011 Jing Xia"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24259","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Michelle Y.","Liang, Feng","Simpson, Douglas G.","Marden, John I."]},{"key":"dc:creator","label":"Author","values":["Xia, Jing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T14:56:20Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Functional magnetic resonance imaging (fMRI)","Functional Activation","Functional Connectivity","Bayesian"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2011 Jing Xia"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24259"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Since the early 1990s, functional magnetic resonance imaging (fMRI) has dominated the brain mapping field and it has been proved a powerful tool for mapping human brain functions. 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In the first part, we present a spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation; in the second part, we propose a model based clustering method to detect the functional connectivity network; in the third part, we present a general and novel statistical framework for robust and more complete estimation of brain functional connectivity based on correlation analysis and hypothesis testing. The complicated data features are taken into account in the three algorithms.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-01-20T14:35:11Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Xia_Jing.pdf: 3148477 bytes, checksum: 87903571faa50705d0fd6aae0ce61eac (MD5)","Made available in DSpace on 2011-05-25T14:56:20Z (GMT). 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The functional activation detection and functional connectivity network analysis by using fMRI are two important research topics in the neuroscience. In this work, we present three different statistical methods, corresponding to three chapters, for the activation detection and network discovery. In the first part, we present a spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation; in the second part, we propose a model based clustering method to detect the functional connectivity network; in the third part, we present a general and novel statistical framework for robust and more complete estimation of brain functional connectivity based on correlation analysis and hypothesis testing. 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