The application of a mathematical model linking structural and functional connectomes in severe brain injury

Abstract

Following severe injuries that result in disorders of consciousness, recovery can occur over many months or years post-injury. While post-injury synaptogenesis, axonal sprouting and functional reorganization are known to occur, the network-level processes underlying recovery are poorly understood. Here, we test a network-level functional rerouting hypothesis in recovery of patients with disorders of consciousness following severe brain injury. This hypothesis states that the brain recovers from injury by restoring normal functional connections via alternate structural pathways that circumvent impaired white matter connections. The so-called network diffusion model, which relates an individual's structural and functional connectomes by assuming that functional activation diffuses along structural pathways, is used here to capture this functional rerouting. We jointly examined functional and structural connectomes extracted from MRIs of 12 healthy and 16 brain-injured subjects. Connectome properties were quantified via graph theoretic measures and network diffusion model parameters. While a few graph metrics showed groupwise differences, they did not correlate with patients' level of consciousness as measured by the Coma Recovery Scale — Revised. There was, however, a strong and significant partial Pearson's correlation (accounting for age and years post-injury) between level of consciousness and network diffusion model propagation time (r = 0.76, p < 0.05, corrected), i.e. the time functional activation spends traversing the structural network. We concluded that functional rerouting via alternate (and less efficient) pathways leads to increases in network diffusion model propagation time. Simulations of injury and recovery in healthy connectomes confirmed these results. This work establishes the feasibility for using the network diffusion model to capture network-level mechanisms in recovery of consciousness after severe brain injury.

Mapping the functional connectome in traumatic brain injury: What can graph metrics tell us?

“…While Nakamura and colleagues (2009) showed a reduced small world index in severe TBI patients (at timepoint 1), Kuceyeski et al (2016) revealed a higher small world index in severe TBI patients. Moreover, Hillary et al (2014) showed a marginally significant group difference in small worldness, and the studies by Caeyenberghs et al (2012) and Messé et al (2013) failed to observe any significant group differences on small-world metrics.…”

“…Given the different etiologies (other than TBI only) discussed in several surveyed studies (Arnemann et al, 2015;Gratton et al, 2012;Kuceyeski et al, 2016;Nomura et al, 2010) and the significant pathophysiological differences between mild and moderate-to-severe TBI, we should not generalize functional connectivity findings across etiologies of ABI and injury severities. Subgroup analyses could be performed, whereby each type of injury (focal contusion, diffuse axonal injuries, axonal degeneration) could be examined in detail, investigating the extent to which graph metrics are preserved or affected by each type of injury.…”

“…Among the studies that used graph theory to assess functional connectivity after TBI, only a few have quantified small-worldness (Caeyenberghs et al, 2012;Hillary et al, 2014;Kuceyeski et al, 2016;Messé et al, 2013;Nakamura et al, 2009) but with mixed findings.…”

“…As shown in Table 2, 13 studies investigated the functional connectome, as evaluated by either task-related fMRI (n = 4) (Arnemann et al, 2015;Caeyenberghs et al, 2012;Caeyenberghs et al, 2013;Hillary et al, 2014) or resting-state (RS)-fMRI (n = 9) (Gratton et al, 2012;Han et al, 2016;Han et al, 2014;Kuceyeski et al, 2016;Messé et al, 2013;Nakamura et al, 2009;Nomura et al, 2010;Pandit et al, 2013;Spielberg et al, 2015) (see Table 2); three studies combined fMRI with structural MRI for mapping changes in white matter (Caeyenberghs et al, 2013;Hillary et al, 2014;Pandit et al, 2013) or grey matter (Hillary et al, 2014) Table 2). …”

“…Of note, the patient group in the study of Caeyenberghs et al (2013) was a subsample of the larger group of participants in the study of Caeyenberghs et al (2012). Also, many studies included other etiologies than TBI only (Arnemann et al, 2015;Gratton et al, 2012;Kuceyeski et al, 2016;Nomura et al, 2010). In these studies, the source of brain injury varied among the patients, resulting from trauma (N=27), stroke (N=44), tumor resection (N=7), chemotherapy (N=1), cerebral hemorrhage (N =2), vascular (N=1), or multiple sceloris (N=1).…”