While concussion is highly prevalent, there is remarkably little known about its underlying pathology. Concussion implicates various cell types, including neurons and glia, as well as the metabolic, immune, and neurovascular systems.

Further complicating the study of concussion is the incredible individuality of each injury in humans.

Each injury is a unique combination of forces and cellular responses. This means that each person experiences a distinct injury and recovery.

While some recover completely, up to 20% of patients suffer long-term effects from concussion.
To understand the heterogeneity of concussion, we are investigating the following three questions:

  • Why are some people more resilient to injury than others?
  • Why do some people recover completely while others do not?
  • How can we intervene to prevent injury and promote recovery?

Asked another way, these questions examine how the state of the brain before injury, or preconditioning, predict concussion risk and the likelihood of full recovery. Preconditioning can take two forms – some factors may increase susceptibility to injury while others may decrease vulnerability.
Beneficial preconditioning factors are of special interest because we can ask how we can take advantage of biology to better protect the brain before impact.

Our work spans various spatial scales and research modalities, but network theory offers a unifying framework by which to consider our results.

Through network analysis, we can both characterize empirical changes in neuronal networks and explore their underlying mechanisms via computational models.

Furthermore, network theory tools provide metrics to depict and explain the heterogeneity of concussion.