Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study

Abstract

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury

“…They moreover observed enhanced connectivity strengths in a sub-network containing midline sensorimotor cortex and left CB of SCI patients, attributing it to a higher degree of neuronal intercommunication of those areas due to the injury. Kaushal et al ( 2017b ) also applied large-scale GA to their data to further analyze the RS networks of the SCI patients in comparison to those of healthy individuals. They calculated correlation between pairs the already defined ROIs and then calculated modularity, as well as local and global efficiency.…”

Resting-State Functional Connectivity of the Thalamus in Complete Spinal Cord Injury

“…Previous research using resting-state fMRI has reported a decrease in functional connectivity (FC) between cortical sensorimotor regions after SCI. 9-15 Such alterations in resting-state properties have been shown by a previous study to hold significant implications for the prognosis of SCI at 6 months postinjury. 15 Findings from alternative approaches such as graph analysis provide evidence for preserved small-worldness with reduced local efficiency and greater network modularity and characteristic path length in the network architecture of SCI patients.…”

“…15 Findings from alternative approaches such as graph analysis provide evidence for preserved small-worldness with reduced local efficiency and greater network modularity and characteristic path length in the network architecture of SCI patients. 10,16 However, the translation of cortical reorganization into clinical biomarkers is yet to be established. This gap in our knowledge of neuroplasticity and functional recovery may pertain to the subcortical substrates of SCI.…”

Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

“…Functional connectivity (FC) after SCI has been studied by means of electroencephalography (EEG) [ 9 – 15 ] and functional magnetic resonance imaging (fMRI) [ 6 , 16 – 21 ]. Poor recovery after SCI has been associated with decreased FC strengths between midline sensorimotor network nodes during resting state, while the opposite pattern has been associated with good recovery [ 6 ].…”