{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105843"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105843","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A Bayesian model for dynamic functional connectivity estimation in the human brain with structural priors","abstract":"Studies of dynamic functional connectivity have demonstrated that anatomical linkage is related to persistent functional connectivity. Bayesian models can leverage this connection by regularizing estimates of functional connectivity according to the strength of the corresponding structural connectivity. We proposed and evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal transformations and an ability to recover simulated data generated according to both discrete and continuous temporal dynamics. Finally, it outperformed sliding window baselines and anatomically un-informed baselines on estimating instantaneous covariances according to out-of-sample log likelihood on two task datasets.","abstract_html":"Studies of dynamic functional connectivity have demonstrated that anatomical linkage is related to persistent functional connectivity. Bayesian models can leverage this connection by regularizing estimates of functional connectivity according to the strength of the corresponding structural connectivity. We proposed and evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal transformations and an ability to recover simulated data generated according to both discrete and continuous temporal dynamics. Finally, it outperformed sliding window baselines and anatomically un-informed baselines on estimating instantaneous covariances according to out-of-sample log likelihood on two task datasets.","abstract_has_math":false,"creators":["Manchanda, Sameer"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:49:36Z","date_published":"2019-11-26T20:49:36Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Bayesian inference","Functional Connectivity"],"languages":["en"],"rights":["Copyright 2019 Sameer Manchanda"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105843","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Manchanda, Sameer"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:49:36Z","2021-11-27T10:15:09Z","2019-07-19","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Bayesian inference","Functional Connectivity"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Sameer Manchanda"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105843"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Studies of dynamic functional connectivity have demonstrated that anatomical linkage is related to persistent functional connectivity. Bayesian models can leverage this connection by regularizing estimates of functional connectivity according to the strength of the corresponding structural connectivity. We proposed and evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal transformations and an ability to recover simulated data generated according to both discrete and continuous temporal dynamics. Finally, it outperformed sliding window baselines and anatomically un-informed baselines on estimating instantaneous covariances according to out-of-sample log likelihood on two task datasets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Sameer Manchanda, accepted the attached license on 2019-07-19 at 10:46.","The student, Sameer Manchanda, submitted this Thesis for approval on 2019-07-19 at 10:50.","This Thesis was approved for publication on 2019-07-19 at 11:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14389 on 2019-11-26 at 13:06:25","Made available in DSpace on 2019-11-26T20:49:36Z (GMT). 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Bayesian models can leverage this connection by regularizing estimates of functional connectivity according to the strength of the corresponding structural connectivity. We proposed and evaluated the ability of such a model to recover covariance matrices. The model performed well in a high dimensional, small sample simulated setting. In addition, it exhibited robustness to temporal transformations and an ability to recover simulated data generated according to both discrete and continuous temporal dynamics. Finally, it outperformed sliding window baselines and anatomically un-informed baselines on estimating instantaneous covariances according to out-of-sample log likelihood on two task datasets.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Sameer Manchanda, accepted the attached license on 2019-07-19 at 10:46.","The student, Sameer Manchanda, submitted this Thesis for approval on 2019-07-19 at 10:50.","This Thesis was approved for publication on 2019-07-19 at 11:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14389 on 2019-11-26 at 13:06:25","Made available in DSpace on 2019-11-26T20:49:36Z (GMT). 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