The purpose of this study was to examine neural network properties at separate time-points during recovery from traumatic brain injury (TBI) using graph theory. Whole-brain analyses of the topological properties of the fMRI signal were conducted in 6 participants at 3 months and 6 months following severe TBI. Results revealed alterations of network properties including a change in the degree distribution, reduced overall strength in connectivity, and increased “small-worldness” from 3 months to 6 months post injury. The findings here indicate that, during recovery from injury, the strength but not the number of network connections diminishes, so that over the course of recovery, the network begins to approximate what is observed in healthy adults. These are the first data examining functional connectivity in a disrupted neural system during recovery.

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer’s disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.

There remains much unknown about how large-scale neural networks accommodate neurological disruption, such as moderate and severe traumatic brain injury (TBI). A primary goal in this study was to examine the alterations in network topology occurring during the first year of recovery following TBI. To do so we examined 21 individuals with moderate and severe TBI at 3 and 6 months after resolution of posttraumatic amnesia and 15 age- and education-matched healthy adults using functional MRI and graph theoretical analyses. There were two central hypotheses in this study: 1) physical disruption results in increased functional connectivity, or hyperconnectivity, and 2) hyperconnectivity occurs in regions typically observed to be the most highly connected cortical hubs, or the “rich club”. The current findings generally support the hyperconnectivity hypothesis showing that during the first year of recovery after TBI, neural networks show increased connectivity, and this change is disproportionately represented in brain regions belonging to the brain's core subnetworks. The selective increases in connectivity observed here are consistent with the preferential attachment model underlying scale-free network development. This study is the largest of its kind and provides the unique opportunity to examine how neural systems adapt to significant neurological disruption during the first year after injury.

Older adults tend to have poorer outcomes compared to younger adults following moderate-to-severe traumatic brain injury (TBI). Currently, there is a need for research focusing on how elderly TBI has changed as the U.S. population shifts. This study provides a statewide account of moderate-to-severe TBI in regard to injury-related variables and incidence rates in the elderly. Data from Pennsylvania accredited trauma centers collected in the Pennsylvania Trauma Outcome Study between 1992 and 2009 were used in the current study. Incidence rates for TBI were calculated using U.S. Census Bureau estimates for individuals aged 65–90 years (separated into three subgroups: ages 65–73, 74–82, and 83–90 years). In addition, we focused on describing the following injury-related variables: mechanism of injury, injury severity, hospital length of stay, and functional status at discharge. The results indicate that the incidence of elderly TBI has approximately doubled in the past 18 years, and that the increase in elderly TBI is greatest for individuals between the ages of 83 and 90. Furthermore, this age group had the poorest outcomes following TBI. Prevention and awareness of TBI in the elderly is imperative in reducing the likelihood of injury and disability. Continued statewide work is needed to demonstrate trends in elderly TBI nationwide to further add to the knowledge base used for prevention and rehabilitation work.