• Suliang Chen, Mathew J Gallagher, Florence Hogg, Marios C Papadopoulos, and Samira Saadoun.
• Academic Neurosurgery Unit, St. George's, University of London, London, United Kingdom.
• J. Neurotrauma. 2018 Dec 15; 35 (24): 2947-2956.

AbstractTo guide management of patients with acute spinal cord injuries, we developed intraspinal pressure monitoring from the injury site. Here, we examine the complex fluctuations in the intraspinal pressure signal using network theory. We analyzed 7097 h of intraspinal pressure data from 58 patients with severe cord injuries. Intraspinal pressure signals were split into hourly windows. Each window was mapped into a visibility graph as follows. Vertical bars were drawn at 0.1 Hz representing signal amplitudes. Each bar produced a node, thus totalling 360 nodes per graph. Two nodes were linked with an edge if the straight line through the nodes did not intersect a bar. We computed several topological metrics for each graph including diameter, modularity, eccentricity, and small-worldness. Patients were followed up for 20 months on average. Our data show that the topological structure of intraspinal pressure visibility graphs is highly sensitive to pathological events at the injury site, including cord compression (high intraspinal pressure), ischemia (low spinal cord perfusion pressure), and deranged autoregulation (high spinal pressure reactivity index). These pathological changes correlate with long graph diameter, high modularity, high eccentricity and reduced small-worldness. In a multivariate logistic regression model, age, neurological status on admission, and average node eccentricity were independent predictors of neurological improvement. We conclude that analysis of intraspinal pressure fluctuations after spinal cord injury as graphs, rather than as time series, captures clinically important information. Our novel technique may be applied to other signals recorded from injured central nervous system (CNS); for example, intracranial pressure, tissue metabolite, and oxygen levels.

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