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  • Decreased Segregation of Whole-Brain Resting-State Network in Spinal Cord Injury: A Graph Theoretical Analysis

    Final Number:
    275

    Authors:
    Mayank Kaushal MD MBA; Akinwunmi Oni-Orisan MD; Gang (Jeff) Chen; Wenjun Li PhD; John Leschke; B. Douglas Ward MS; Benjamin Kalinosky PhD; Matthew Budde PhD; Brian Schmit PhD; Vaishnavi Muqeet MD; Shekar N. Kurpad MD PhD

    Study Design:
    Other

    Subject Category:
    Spine

    Meeting: Section on Disorders of the Spine and Peripheral Nerves Spine Summit 2018

    Introduction: There has been increasing focus on studying large-scale brain networks using graph theory to characterize resting-state functional connectivity. By describing the brain networks in terms of nodes or regions of interest (ROI) and edges or connections between the nodes, various network properties can be evaluated with the help of quantifiable metrics. The present study demonstrates changes to the property of segregation, which evaluates local connectivity of brain networks, in spinal cord injury (SCI).

    Methods: After obtaining the necessary IRB approval, resting-state functional magnetic resonance imaging (rs-fMRI) was performed on 15 subjects with chronic, cervical SCI (ASIA A) and 15 controls that were intact neurologically. The rs-fMRI data was preprocessed to correct for artifacts and then divided into 264 ROIs based on functional atlas from Power et al. The average time series extracted from each ROI was correlated with every other ROI to obtain connectivity matrices. Correlation coefficient (CC), which measures the connectedness of node’s neighbor with each other, and modularity (M), which calculates the extent of division of a network into non-overlapping groups were selected to network segregation. CC and M were computed at multiple incremental cost thresholds (% of total possible connections) and compared between SCI and controls.

    Results: The whole-brain network showed differences between the study groups for both metrics at multiple cost thresholds with CC and M found to be significantly decreased in SCI compared to controls.

    Conclusions: The presence of significant differences in the metrics studying system segregation demonstrates the utility of graph theory in evaluating information processing carried out within specialized regions. The findings illustrate that disruption to information transmission between the brain and the spinal cord leads to decreased functional specialization within the whole-brain network. This has the potential to facilitate noninvasive biomarker development for improving the monitoring of functional improvement conferred by various therapeutic approaches.

    Patient Care: The use of quantitative network metrics to study alterations to network segregation could aid in biomarker development for monitoring functional outcomes associated with different therapeutic interventions in SCI.

    Learning Objectives: By the conclusion of this session, participants should be able to: 1) Appreciate the role of graph theory in studying functional specialization of brain networks. 2) Understand the impact of spinal cord injury information processing within segregated regions of the whole brain network. 3) Observe that persistent connectivity alterations are indicative of inherent neural plasticity in the brain.

    References: Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. Mar 2009;10(3):186-198. Power JD, Cohen AL, Nelson SM, et al. Functional network organization of the human brain. Neuron. Nov 17 2011;72(4):665-678. Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. Sep 2010;52(3):1059-1069. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. Jun 04 1998;393(6684):440-442. Newman ME. Fast algorithm for detecting community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys. Jun 2004;69(6 Pt 2):066133.

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