Rationale and Preliminary Data: We will conduct a study of human spinal cord injury (SCI) to validate MRI biomarkers of injury severity and prognostication of outcome using a novel diffusion MRI technique developed specifically to detect axonal injury in the spinal cord. Predicting outcome from SCI has been a longstanding goal for better clinical management and aiding in the development and testing of therapies. Traditional neurological examination is not an accurate predictor of outcome, and conventional MRI, including T2-weighted imaging, while useful for diagnosis, does not accurately predict the degree of recovery. Diffusion tensor imaging (DTI) has shown promise as a prognostic imaging biomarker in SCI, but its clinical adoption has been hindered by technical challenges and non-specificity to the underlying pathology. Our preclinical studies in a rat SCI model have demonstrated that double diffusion encoding (DDE) MRI is sensitive to acute axonal injury and predicts outcome with accuracy better than either DTI or traditional functional scoring. Likewise, recent developments by our collaborative group have demonstrated the ability to employ diffusion contrast adjacent to metal surgical hardware, which is prone to artifacts. While promising, validation of these technologies to simultaneously improve contrast and quality is critical to advance the technology and ensure its utility in human subjects and clinical settings. This project will translate these techniques to advance the understanding of the DTI changes in the cord as markers of injury. Our hypotheses are 1) in the acute setting, DDE estimates of acute axonal injury will predict long-term functional outcomes, and 2) in the chronic setting, DDE estimates of permanent axonal loss will correlate with existing functional outcomes. It is predicted that DDE will outperform DTI, conventional MRI, or functional neurological exams in SCI. To test this hypothesis, we will perform in vivo MRI and functional assessments in the acute phase after traumatic spinal cord injury.
In Aim 1, we will examine the prognostic ability of DDE to predict later neurological recovery using follow-up functional assessments.
In Aim 2, we will detail the link between axonal loss (sparing) as measured by DDE and permanent neurological function after SCI. These studies seek to establish and validate DDE as a surrogate maker of injury severity and outcome and compare it with existing clinical standards and established MRI indicators of SCI. We hypothesize based on strong preclinical results that detection of microstructural injury using DDE will more accurately reflect the degree of neurological impairment than MRI techniques non-specific to underlying pathology. The potential for clinical translation is highlighted by DDE being a rapid acquisition of only a few minutes and requires minimal post-processing or post-hoc analysis for quantification. Moreover, DDE enables visualization of the degree of injury in individual subjects, making it promising for clinical management of SCI patients. Collectively, these studies will establish and validate DDE as a biomarker of SCI with the potential to improve prognostication in human SCI.
Presently, more than 40,000 Veterans with spinal cord injury (SCI) are eligible for care under the Department of Veteran Affairs. Attempts at restoring neurological function for these Veterans remains a challenge, largely due to our limited understanding of the disease. The current proposal aims to translate a novel and promising spinal cord imaging technique to improve diagnosis and prognostication in SCI and investigate relationships with motor and sensory function along with other functional domains that have a direct impact of patient- reported quality of life. This data will be crucial to understanding the link between spinal cord structural integrity and function. The results of this study will help establish diffusion imaging as a non-invasive biomarker for SCI prognostication and offer newer approaches for monitoring therapeutic interventions in injured Veterans.
