Dissociating mTBI and PTSD brain activity at rest

University Of Florida, Gainesville FL

Investigators

Abstract

DESCRIPTION (provided by applicant): The goal of the proposed project is to determine whether large-scale neural networks are differentially affected in mild traumatic brain injury (mTBI) and posttraumatic stress disorders (PTSD), two commonly co-occurring disorders in returning Veterans, and how this relates to cognitive and affective dysfunction. TBI confers additional risk of PTSD, with mTBI showing the highest comorbidity rates10. Unfortunately, many cognitive, affective and somatic symptoms of mTBI and PTSD significantly overlap11 posing challenges to providing adequate care for returning Veterans14,10. Parsing out the unique and overlapping neural bases of these often comorbid conditions can inform appropriate treatments5,15. While both mTBI and PTSD show disturbances in the connectivity of the default mode network (DMN; a set of structurally and functionally connected brain regions active at rest), evidence from the literature suggests that there may be theoretically and clinically significant unique and overlapping neural correlates of these disorders; however, to date, these groups have yet to be compared directly. To test the hypothesis that mTBI and PTSD differentially affect large scale neural networks, and thus may show dissociable connectivity patterns within the DMN and between limbic and paralimbic structures, returning Veterans with mTBI, PTSD, mTBI and PTSD, and combat controls will undergo resting-state functional MRI (fMRI) as part of a larger DOD-funded study by the fellowship Mentor. The second hypothesis, specifically that alteration of the DMN following mTBI and psychological trauma may promote a better understanding of cognitive, affective, and somatic dysfunction, will be tested by correlating fMRI functional connectivity data with symptom reports and measures of neuropsychological functioning. If supported, findings from this study may eventually lead to the identification of neural risk/resilience factors, identify potential targets of treatment, track changes associated with treatment, and potentially aid in differential diagnosis.