The Dynamics of Concussion: Mapping Pathophysiology, Persistence, and Recovery With Causal-Loop Diagramming

• Published in: Frontiers in neurology Vol. 9; p. 203
• Main Authors: Kenzie, Erin S., Parks, Elle L., Bigler, Erin D., Wright, David W., Lim, Miranda M., Chesnutt, James C., Hawryluk, Gregory W. J., Gordon, Wayne, Wakeland, Wayne
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 04.04.2018
• Subjects: causal-loop diagram; complexity; concussion; Neuroscience; recovery; systems science; traumatic brain injury; causal-loop diagram; complexity; systems medicine; traumatic brain injury; concussion; recovery; models of injury; systems science
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2018.00203

Despite increasing public awareness and a growing body of literature on the subject of concussion, or mild traumatic brain injury, an urgent need still exists for reliable diagnostic measures, clinical care guidelines, and effective treatments for the condition. Complexity and heterogeneity complicate research efforts and indicate the need for innovative approaches to synthesize current knowledge in order to improve clinical outcomes. Methods from the interdisciplinary field of systems science, including models of complex systems, have been increasingly applied to biomedical applications and show promise for generating insight for traumatic brain injury. The current study uses causal-loop diagramming to visualize relationships between factors influencing the pathophysiology and recovery trajectories of concussive injury, including persistence of symptoms and deficits. The primary output is a series of preliminary systems maps detailing feedback loops, intrinsic dynamics, exogenous drivers, and hubs across several scales, from micro-level cellular processes to social influences. Key system features, such as the role of specific restorative feedback processes and cross-scale connections, are examined and discussed in the context of recovery trajectories. This systems approach integrates research findings across disciplines and allows components to be considered in relation to larger system influences, which enables the identification of research gaps, supports classification efforts, and provides a framework for interdisciplinary collaboration and communication-all strides that would benefit diagnosis, prognosis, and treatment in the clinic.