Evaluation of Traumatic Brain Injury Using Magnetic Resonance Spectroscopy

Abstract

Traumatic brain injury (TBI) is responsible for a third of all injury-related deaths in the United States. With the lack of structural imaging biomarkers available for the detection and evaluation of TBI sequelae, unambiguous diagnosis and prognosis in TBI still remain a huge challenge. Furthermore, complications arising from TBI can lead to cognitive, social, emotional and behavioral defects later in life. Even in confirmed cases of head injury, computed tomography (CT) and conventional MR techniques are limited in their ability to predict the neuropsychological outcome of patients. While the initial trauma can induce structural impairment of brain tissue, the bulk of the cerebral dysfunction ensuing from TBI is due to alterations in cellular biochemical processes that occur in the days and weeks following the traumatic incident. There is therefore a need for advanced imaging modalities that are able to probe the more underlying cellular changes that are induced by TBI. Understanding such cellular changes will be useful in predicting patient outcome and designing interventions to alleviate the injury sequelae. Magnetic Resonance Spectroscopy (MRS) is a non-invasive imaging modality that is capable of detecting cellular metabolic changes in in vivo tissue. In this study we will assess the use of MRS as a clinically relevant tool in the diagnostic and prognostic evaluation of TBI. To this end, we have laid out the following specific aims: (i) To understand the nature and implications of neurometabolic sequelae in mild traumatic brain injury (mTBI) by carrying out cross-sectional comparisons of mTBI patients to neurologically healthy subjects at different stages of injury and to determine associations between early neurometabolic patterns and chronic neuropsychological performance in mTBI patients (ii) To develop novel MRS pulse sequence acquisition and data processing techniques that will enable a more thorough neurometabolic evaluation of TBI and enhance quantification of MRS data (iii) To develop automated classification systems in mTBI using early neurometabolic information that will aid discrimination between subjects with and without injury related sequelae and allow the prediction of symptomatic outcome at the later stages of injury. The research presented herein will help to enhance the utility of MRS in the evaluation of TBI.