Quantifying Resting-State Functional Connectivity in Critically Brain-Injured Patients: A Graph-Theoretical Approach with fNIRS

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.

Name thesis:degree_name

M Sc

Discipline thesis:degree_discipline

Neuroscience

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2024

Author dc:creator

• Gupta, Ira

Advisors dc:contributor.advisor

• Owen, Adrian M.
• Debicki, Derek B.

• Acute brain injury
• resting-state functional connectivity
• functional near-infrared spectroscopy
• graph theory
• machine learning

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/36662

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/36662