Traumatic brain injury (TBI) is the leading cause of death and disability in Americans under age 45, and is increasing in prevalence worldwide. Neuroimaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI) are important diagnostic tools for the clinical management of acute TBI. However, the focal lesions detected by CT or MRI in acute TBI, such as contusions and axonal shearing injuries, are often not predictive of long-term functional disability after TBI, especially in mild cases. The objective of this research proposal is to establish quantitative macrostructural and microstructural imaging biomarkers for predicting patient outcome after mild TBI. The macrostructural biomarker measures post- traumatic focal atrophy using deformation-based morphometry (DBM) of serial high-resolution 3D MR scans of the brain. The microstructural biomarker measures post-traumatic decreases in white matter integrity using quantitative fiber tracking based on serial diffusion tensor imaging (DTI). One hundred mild TBI patients will undergo high-resolution 3D structural MRI and DTI on 3 Tesla MR scanners at 1 month after injury, at 6 months after injury, and then again at 1 year after injury. Comparison will be made to the same imaging protocol in 40 age-, gender-, and education-matched healthy control subjects. All subjects will undergo neurocognitive and functional outcome tests at the same time points as the MRI/DTI scans. The hypothesis will be tested that increasing spatial extent of progressive focal atrophy detected by DBM of serial MRI and/or progressive white matter microstructural injury on serial DTI is correlated with worse neurocognitive and functional outcomes at one year after injury, after controlling for clinical measures of injury severity including Glasgow Coma Scale, duration of unconsciousness, and duration of post-traumatic amnesia. These macrostructural and microstructural imaging biomarkers will also be correlated with functional and metabolic imaging data using fMRI and 3D MR spectroscopic imaging, respectively. If the proposed investigation is successful in establishing these quantitative macrostructural and microstructural imaging biomarkers of long-term outcome in TBI, then they could potentially serve as surrogate endpoints for clinical intervention trials. They might also yield endophenotypes for studies of genetic susceptibility factors that worsen outcome after TBI. Towards this purpose, DNA will be banked from patients in this study for genotype analysis. Specifically, we will examine whether ApoE genotype influences the degree of regional brain atrophy and microstructural white matter injury. The allelic variants of ApoE are already known to modulate clinical outcome after TBI, and this study will determine if DBM and DTI can provide """"""""intermediate phenotypes"""""""" for the effect of ApoE genotype on TBI outcome.
The objective of this research proposal is to apply two new advanced magnetic resonance imaging (MRI) technologies to the study of patients with mild traumatic brain injury: (1) deformation-based morphometry, and (2) diffusion tensor imaging. This research may advance the scientific understanding of brain injury as well as improve the diagnosis of patients suffering from the long-term effects of concussion.
|Palacios, Eva M; Yuh, Esther L; Chang, Yi-Shin et al. (2017) Resting-State Functional Connectivity Alterations Associated with Six-Month Outcomes in Mild Traumatic Brain Injury. J Neurotrauma 34:1546-1557|
|Palacios, E M; Martin, A J; Boss, M A et al. (2017) Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study. AJNR Am J Neuroradiol 38:537-545|
|Owen, Julia P; Chang, Yi Shin; Mukherjee, Pratik (2015) Edge density imaging: mapping the anatomic embedding of the structural connectome within the white matter of the human brain. Neuroimage 109:402-17|
|Friedman, Eric J; Landsberg, Adam S; Owen, Julia P et al. (2014) Stochastic geometric network models for groups of functional and structural connectomes. Neuroimage 101:473-84|
|Owen, Julia P; Ziv, Etay; Bukshpun, Polina et al. (2013) Test-retest reliability of computational network measurements derived from the structural connectome of the human brain. Brain Connect 3:160-76|
|Owen, Julia P; Li, Yi-Ou; Yang, Fanpei G et al. (2013) Resting-state networks and the functional connectome of the human brain in agenesis of the corpus callosum. Brain Connect 3:547-62|
|Tarapore, Phiroz E; Findlay, Anne M; Lahue, Sara C et al. (2013) Resting state magnetoencephalography functional connectivity in traumatic brain injury. J Neurosurg 118:1306-16|
|Owen, Julia P; Li, Yi-Ou; Ziv, Etay et al. (2013) The structural connectome of the human brain in agenesis of the corpus callosum. Neuroimage 70:340-55|
|Li, Yi-Ou; Yang, Fanpei G; Nguyen, Christopher T et al. (2012) Independent component analysis of DTI reveals multivariate microstructural correlations of white matter in the human brain. Hum Brain Mapp 33:1431-51|
|Wahl, Michael; Li, Yi-Ou; Ng, Joshua et al. (2010) Microstructural correlations of white matter tracts in the human brain. Neuroimage 51:531-41|