Traumatic Brain Injuries (TBIs) are highly prevalent among post-deployment Veterans and are linked with a variety of chronic cognitive and behavioral symptoms, including problems with learning and memory, anxiety and other mood issues, executive function deficits, and personality changes. Such symptoms often have severe impacts on Veterans? functional outcomes and quality of life. These symptoms are induced by neurological damage, as TBI is known to cause diffuse axonal injuries that impair the normal networked communication between brain regions, affecting behavior and cognition. However, current techniques to assess different types of TBI, and thus to predict resulting outcomes, do not use neurobiological information in the assessment. Instead, judgments of TBI severity are made based primarily on the post-traumatic symptoms such as amnesia or loss of consciousness. Unfortunately, these post-traumatic symptom-based assessments are not strongly predictive of functional outcomes. Thus, there is both a clinical and a research need to identify more refined and precise TBI subtypes that are based on neurobiological measures and that predict functional outcomes. The applicant, Dr. Gordon, is an acknowledged expert in advanced neuroimaging techniques, and particularly in examining human brain networks using noninvasive MRI-based approaches. Through his primary VA appointment at the VISN 17 Center of Excellence for Research on Returning War Veterans, Dr. Gordon has access to a large pool of extensively characterized Veterans with and without TBI, as well as to a research-dedicated MRI scanner. Further, Dr. Gordon has established collaborations with external researchers who are experts in advanced classification of neuroimaging data for the purposes of subtype identification. In short, Dr. Gordon is in an ideal position to conduct the necessary research to identify neurobiologically-informed subtypes of TBI. In the proposed study, Dr. Gordon will use neuroimaging techniques to characterize axonal tract damage and disruption of networked communication in 100 Veterans (including 70 with a post- traumatic symptom-defined history of TBI). He will then employ advanced data clustering techniques to identify subtypes of TBI that are both based on neurobiological measures of structural and network damage, and explain a high degree of variance in outcome measures of functioning in everyday life. He will identify which neurobiological measures allow identification of subtypes that best explain variance in outcomes. He will then conduct extensive behavioral characterization of the identified subtypes. In summary, this project will identify neurobiologically-informed subtypes of TBI in the population that predict functional outcomes better than classic severity assessments. If successful, this work will influence both clinical treatment and scientific investigation of TBI. In particular, identification of neurobiologically-based TBI subtypes could enable improved diagnosis and prognosis for Veterans suffering from TBI. Indeed, upon successful completion of this project, Dr. Gordon will apply for a VA Merit Review Award to develop approaches for translation of these improved diagnostic techniques into a clinical setting. Finally, by providing the applicant with mentored research time to conduct this study, this project will help Dr. Gordon achieve his goal of becoming an independent VA investigator with a programmatic line of research focused on applying cutting-edge neuroimaging techniques to understand how TBI adversely affects the function of the brain.
Traumatic Brain Injuries (TBIs) are highly prevalent and represent a high healthcare burden among combat Veterans. TBIs are a form of brain damage in which large axonal tracts are disrupted; this damage impairs networked communication between brain regions, which in turn induces behavioral deficits and reduces quality of life. Notably, current techniques to assess TBI severity rely on self-reports of post-traumatic symptoms rather than on neurobiological information; as a result, these assessments do not predict functional outcomes well. In this study, we will use advanced data classification of neurobiological measures to describe subtypes of TBI that predict functional outcomes better than post-traumatic symptom-based measures. We will identify which neurobiological measures best enable identification of highly predictive TBI subtypes, and we will behaviorally characterize the resulting subtypes. This work will enable improved diagnosis and prognosis for Veterans suffering from TBI.
