Structural Connectivity Changes and Prognosis in Comatose Patients Post-Cardiac Arrest

Abstract

Accurate neuroprognostication of cardiac arrest survivors who are initially comatose after restoration of spontaneous circulation is crucial for guiding patient management. Because hypoxic-ischemic injury is typically diffuse, damage to a network of brain regions is likely involved in the patient’s disorder of consciousness. To quantify these complex brain network changes, graph theoretical methods were applied. We hypothesize that structural connectivity metrics may provide additional insights into determining which patients will likely recover consciousness and those who will not. Eighteen comatose patients and four community dwelling participants underwent multi-shell high angular diffusion MRI as part of a prospective study. Structural connectivity matrices were constructed using probabilistic tractography and parcellated with the Automated Anatomical Labels atlas. Network topology was analysed using clustering coefficient, global efficiency, and degree. Exploratory analysis extended for this thesis included analysis of the additional graph theory metrics: neighborhood overlap, Rentian scaling, modularity, assortativity, betweenness centrality, and eigenvector centrality. Hub index analysis was performed to explore the relation between structural disconnection of anatomical hubs and disorders of consciousness. Patients demonstrated reduced clustering coefficient, decreased global efficiency, and lower degree than controls, reflecting diminished local and global network efficiencies. Significant trends were associated with all of the exploratory metrics except for assortativity. In general, these changes were most pronounced in patients with worse outcomes, whereas patients who experienced arousal recovery exhibited metrics closer to those of healthy controls. The hub index analysis based on nodal degree and betweenness revealed disproportionate damage to high-degree nodes such as the thalamus, putamen and precuneus, further linking topological disruption to the severity of outcomes. This study highlights the potential of graph theoretical measures of structural connectivity to guide critical decisions in the care patients. By bridging structural connectivity with clinical outcomes, this research provides valuable insights into the neural mechanisms underlying consciousness and recovery after cardiac arrest.