{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/36662"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/36662","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Quantifying Resting-State Functional Connectivity in Critically Brain-Injured Patients: A Graph-Theoretical Approach with fNIRS","abstract":"Assessment of consciousness in behaviourally unresponsive patients with critical brain injuries continues to be a challenge. There remains a need for robust tools that can accurately characterize preserved cortical function and predict patient outcomes. In the present study, functional near-infrared spectroscopy is employed in conjunction with graph theory and machine learning to quantify resting-state functional connectivity in 16 acutely brain-injured patients and 23 healthy controls. Results revealed significant channel-level differences between the groups for three graph metrics, including degree, clustering coefficient, and local efficiency. Further investigation using machine learning algorithms revealed that these metrics can be used to distinguish between patients and healthy controls with 76% accuracy, and between good and poor patient outcomes with 83% accuracy. Overall, findings from this study provide valuable insights into alterations in brain connectivity following acute brain injury, along with a robust statistical approach for determining patient diagnosis and prognosis.","abstract_html":"Assessment of consciousness in behaviourally unresponsive patients with critical brain injuries continues to be a challenge. There remains a need for robust tools that can accurately characterize preserved cortical function and predict patient outcomes. In the present study, functional near-infrared spectroscopy is employed in conjunction with graph theory and machine learning to quantify resting-state functional connectivity in 16 acutely brain-injured patients and 23 healthy controls. Results revealed significant channel-level differences between the groups for three graph metrics, including degree, clustering coefficient, and local efficiency. Further investigation using machine learning algorithms revealed that these metrics can be used to distinguish between patients and healthy controls with 76% accuracy, and between good and poor patient outcomes with 83% accuracy. Overall, findings from this study provide valuable insights into alterations in brain connectivity following acute brain injury, along with a robust statistical approach for determining patient diagnosis and prognosis.","abstract_has_math":false,"creators":["Gupta, Ira"],"institution":"The University of Western Ontario","degree_name":"M Sc","degree_level":null,"degree_discipline":"Neuroscience","degree_department":null,"school":null,"contributors":[],"advisors":["Owen, Adrian M.","Debicki, Derek B."],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-06-14","date_published":"2024-06-14","updated_at":"2026-07-27T21:56:01Z","subjects":["Acute brain injury","resting-state functional connectivity","functional near-infrared spectroscopy","graph theory","machine learning"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/36662","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Owen, Adrian M.","Debicki, Derek B."]},{"key":"dc:creator","label":"Author","values":["Gupta, Ira"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T21:24:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T21:24:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-06-14"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Neuroscience"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M Sc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Acute brain injury","resting-state functional connectivity","functional near-infrared spectroscopy","graph theory","machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/36662"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["Assessment of consciousness in behaviourally unresponsive patients with critical brain injuries continues to be a challenge. There remains a need for robust tools that can accurately characterize preserved cortical function and predict patient outcomes. In the present study, functional near-infrared spectroscopy is employed in conjunction with graph theory and machine learning to quantify resting-state functional connectivity in 16 acutely brain-injured patients and 23 healthy controls. Results revealed significant channel-level differences between the groups for three graph metrics, including degree, clustering coefficient, and local efficiency. Further investigation using machine learning algorithms revealed that these metrics can be used to distinguish between patients and healthy controls with 76% accuracy, and between good and poor patient outcomes with 83% accuracy. Overall, findings from this study provide valuable insights into alterations in brain connectivity following acute brain injury, along with a robust statistical approach for determining patient diagnosis and prognosis."]},{"key":"dc:title","label":"Title","values":["Quantifying Resting-State Functional Connectivity in Critically Brain-Injured Patients: A Graph-Theoretical Approach with fNIRS"]}]}],"canonical_facts":{"dc:contributor.advisor":["Owen, Adrian M.","Debicki, Derek B."],"dc:creator":["Gupta, Ira"],"dc:date.accessioned":["2025-07-10T21:24:14Z"],"dc:date.available":["2025-07-10T21:24:14Z"],"dc:date.issued":["2024-06-14"],"dc:description":["The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["Assessment of consciousness in behaviourally unresponsive patients with critical brain injuries continues to be a challenge. There remains a need for robust tools that can accurately characterize preserved cortical function and predict patient outcomes. In the present study, functional near-infrared spectroscopy is employed in conjunction with graph theory and machine learning to quantify resting-state functional connectivity in 16 acutely brain-injured patients and 23 healthy controls. Results revealed significant channel-level differences between the groups for three graph metrics, including degree, clustering coefficient, and local efficiency. Further investigation using machine learning algorithms revealed that these metrics can be used to distinguish between patients and healthy controls with 76% accuracy, and between good and poor patient outcomes with 83% accuracy. Overall, findings from this study provide valuable insights into alterations in brain connectivity following acute brain injury, along with a robust statistical approach for determining patient diagnosis and prognosis."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/36662"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Acute brain injury","resting-state functional connectivity","functional near-infrared spectroscopy","graph theory","machine learning"],"dc:title":["Quantifying Resting-State Functional Connectivity in Critically Brain-Injured Patients: A Graph-Theoretical Approach with fNIRS"],"dc:type":["thesis"],"thesis:degree_discipline":["Neuroscience"],"thesis:degree_name":["M Sc"]},"updated_at":"2026-07-27T21:56:01Z"}