{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/82046"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/82046","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modelling Effective Connectivity in Functional Magnetic Resonance Imaging Data by State Space Models","abstract":"This dissertation develops a new approach using state space models for effective connectivity analysis. The proposed approach integrates both activation and connectivity analysis into one single model. It takes the temporal information in the fMRI data into consideration explicitly and can be used to model the relationship among multiple region-of-interests in the brain. Dynamic characteristics of connectivity can also be investigated. The proposed approach is very flexible and can be used to test for commonalities and differences in the connectivity patterns across subjects. An empirical application for testing an attentional control network from a fMRI experiment is presented. The results provide partial support that higher order control (middle frontal gyrus, MFG) influences the two sites of control (lingual gyrus, LG, and middle occipital gyrus, MOG, in this experiment). There is also strong evidence indicating heterogeneity in the subjects' connectivity between LG and MFG. Moreover, the strength of the connectivity between LG and MFG, and between MOG and MFG show decreasing trend during the experiment.","abstract_html":"This dissertation develops a new approach using state space models for effective connectivity analysis. The proposed approach integrates both activation and connectivity analysis into one single model. It takes the temporal information in the fMRI data into consideration explicitly and can be used to model the relationship among multiple region-of-interests in the brain. Dynamic characteristics of connectivity can also be investigated. The proposed approach is very flexible and can be used to test for commonalities and differences in the connectivity patterns across subjects. An empirical application for testing an attentional control network from a fMRI experiment is presented. The results provide partial support that higher order control (middle frontal gyrus, MFG) influences the two sites of control (lingual gyrus, LG, and middle occipital gyrus, MOG, in this experiment). There is also strong evidence indicating heterogeneity in the subjects' connectivity between LG and MFG. Moreover, the strength of the connectivity between LG and MFG, and between MOG and MFG show decreasing trend during the experiment.","abstract_has_math":false,"creators":["Ho, Moon-Ho Ringo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Hubert, Lawrence J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:38:53Z","date_published":"2015-09-25T20:38:53Z","updated_at":"2026-07-22T22:26:17Z","subjects":["Biology, Neuroscience"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3101864"],"render_values":[{"text":"(MiAaPQ)AAI3101864","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/82046","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hubert, Lawrence J."]},{"key":"dc:creator","label":"Author","values":["Ho, Moon-Ho Ringo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:38:53Z","10000-01-01","2003"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"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":["Biology, Neuroscience"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/82046","(MiAaPQ)AAI3101864"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This dissertation develops a new approach using state space models for effective connectivity analysis. 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