The overarching goal of this proposal is to advance the development of a diffusion weighted imaging technique (Hybrid Diffusion Imaging or HYDI) to enhance the diagnosis of mild traumatic brain injury (MTBI) and clarify the role of white matter (WM) pathology in this disorder. The diagnosis of MTBI is made on clinical grounds as there are no definitive biomarkers currently available. Diffusion tensor imaging (DTI) has been proposed as a potential neuroimaging biomarker but to date results in MTBI have been quite mixed. We believe that a more general and complete diffusion weighted imaging technique may provide more accurate and consistent results in MTBI where white matter injury may be quite subtle. One such approach is q-space diffusion imaging which estimates the probability density function (PDF) of water diffusion without any model assumptions. However, the clinical application of q-space imaging is often impeded by long scan-time when sampling the entire q- space (i.e. diffusion space). We have developed a q-space encoding scheme known as Hybrid Diffusion Imaging (HYDI) to facilitate the clinical application of q-space imaging. It obtains measurements on concentric spherical shells in q-space and enables multiple diffusion data analyses from a single imaging sequence including DTI (inner shells), PDF (whole datasets) and q-ball imaging (outer shell) for WM fiber tractography. We propose to advance the development of PDF and orthogonal DTI measures using the HYDI technique and to confirm their sensitivity and specificity to white matter injury within one week of MTBI. Initially, a Monte Carlo simulation study will be performed to optimize the MR image parameters and diffusion parameters of HYDI for MTBI. Subsequently, 40 individuals with MTBI and 40 Other Injury Controls (non-TBI) seen in a Level 1 Trauma Center will be studied with HYDI within one week of injury. Participants will undergo standardized assessment of cognitive function and post concussive symptoms. Analysis of covariance (ANCOVA) will be used to compare whole brain HYDI WM parameters and hand drawn regions of interest (ROIs). In addition we will perform probability tractography to assess these parameters in tracts vulnerable to TBI including corpus callosum, pyramidal (corticospinal) tract, frontal subcortical WM, and the superior longitudinal fasiculus. A general linear model approach including multivariate analysis of variance (MANOVA) and covariance (MANCOVA) will be used to assess the correlation between observed white matter differences and cognitive/neurobehavioral function to determine the functional significance of observed differences. This project proposes a novel approach to diffusion imaging that if successful it would greatly enhance the capacity to diagnose MTBI (i.e. supplement clinical history), monitor recovery from the injury, and serve as a biomarker for response to treatment interventions.
The goal of this project is to improve our diagnosis and understanding of the underlying neural mechanisms of mild traumatic brain injury (MTBI). Specifically this project proposes to refine a new neuroimaging technique that is highly sensitive to the white matter injury thought to be responsible for the symptoms of MTBI, and to test its sensitivity in a group of individuals within one week of injury. If successful this technique would greatly enhance the capacity to diagnose MTBI, monitor recovery from the injury, and serve as imaging biomarkers for response to treatment interventions.
| Wen, Qiuting; Kodiweera, Chandana; Dale, Brian M et al. (2018) Rotating single-shot acquisition (RoSA) with composite reconstruction for fast high-resolution diffusion imaging. Magn Reson Med 79:264-275 |
| Kodiweera, Chandana; Alexander, Andrew L; Harezlak, Jaroslaw et al. (2016) Age effects and sex differences in human brain white matter of young to middle-aged adults: A DTI, NODDI, and q-space study. Neuroimage 128:180-192 |
| Kodiweera, Chandana; Wu, Yu-Chien (2016) Data of NODDI diffusion metrics in the brain and computer simulation of hybrid diffusion imaging (HYDI) acquisition scheme. Data Brief 7:1131-8 |
