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.