Current neuroimaging protocols used in clinical settings detect only a subset of brain abnormalities in patients with moderate and severe (m/s) TBI, due in part to insensitivity to diffuse axonal injury (DAI) and subtle diffuse brain damage. The goal of this research program is to develop improved quantitative automated lesion detection (QALD) procedures to comprehensively quantify the nature and extent of brain damage following m/s TBI, and to relate the nature and extent of brain damage to cognitive outcome. The principle of QALD is simple: multimodal structural MRI data is coregistered in a standard coordinate system so that the regional brain tissue properties of TBI patients can be statistically compared with those of demographically-matched control populations. QALD will utilize the data from six high-resolution MR imaging sequences that are differentially sensitive to different types of TBI-related damage: (1) T1 images to analyze tissue volumes, cortical thickness, and cortical blurring (indicative of damage at the gray/white junction);(2) T2 weighted images to assure accurate tissue segmentation and to evaluate myelin using T1/T2 ratios;(3) Fluid Attenuated Inversion Recovery (FLAIR) to assist in quantifying lesion extent;(4) Diffusion Tensor Imaging (DTI) to quantify DAI in pericortical fibers and major white matter tracts;(5) Magnetization Transfer Imaging (MTI) to quantify DAI-associated demyelination, and (6) Susceptibility Weighted Imaging (SWI) to detect hemosiderin residues indicative of micro-hemorrhages.
In Aim 1, we will analyze the volume and tissue properties of different cortical regions using a cortical-surface based coordinate system. We will analyze the effects of m/s TBI on regional cortical thickness and volume, and analyze the distribution of contusions, microbleeds, demyelination, and DAI. In addition, we will analyze the laminar distribution of damage to determine if microbleeds and DAI occur disproportionately at the gray/white boundary.
In Aim 2, we will analyze the volume and tissue properties of major fiber tracts. Fiber tract extent will be defined with TRACULA, which uses fiber-tract atlases supplemented with fiber-direction data and subject-specific anatomical priors. We will also apply procedures developed in our laboratory to automatically segment and analyze different regions of the corpus callosum.
In Aim 3, we will analyze subcortical gray matter structures, including the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. Each structure will be automatically segmented to evaluate mean tissue properties and warped onto structure-specific 3D templates to evaluate regional abnormalities. Finally, in Aim 4, we will quantify neuropsychological deficits using computerized neuropsychological tests of memory, attention, executive function, and processing speed. This will enable us to correlate the location and extent of brain damage with the cognitive impairments in m/s TBI patients. We will also describe the sensitivity of different imaging modalities in detecting TBI- related lesion. Thus, QALD will improve the sensitivity and objectivity of neuroimaging studies of m/s TBI, clarify m/s TBI neuropathology, and improve the prognostic value of neuroimaging studies.

Public Health Relevance

to the VA Patient Care Mission Brain damage following moderate and severe traumatic brain injury (m/s TBI) is poorly assessed with current clinical MR studies, primarily because the most common types of TBI-related brain damage -- diffuse axonal injury (DAI) and regional cortical atrophy -- cannot be easily visualized with standard clinical neuroimaging procedures. We propose to further develop Quantitative Automated Lesion Detection (QALD) techniques to objectively assess brain damage, including DAI and regional atrophy, in Veterans who have suffered m/s TBI. Preliminary studies show that QALD procedures are able to detect brain lesions in m/s TBI patients that are not evident with standard clinical MR procedures. Further enhancements are proposed to enhance QALD sensitivity and so that it can both more accurately localize brain lesions in m/s TBI patients and clarify the mechanisms of brain damage. The number and location of brain abnormalities will be correlated with TBI-related impairments in cognitive function measured with computerized neuropsychological tests, in order to identify brain areas whose damage is associated with poor cognitive outcome. QALD promises to enhance the sensitivity and objectivity of magnetic resonance imaging (MRI) assessments of TBI-related brain damage and improve their diagnostic and prognostic value. In addition to the important diagnostic utility that QALD holds for TBI, future applications also include using QALD to influence treatments by guiding the selection of behavioral and/or pharmaceutical treatment for individual patients based on the brain regions that have been damaged. Finally, improved QALD procedures may also find use in the evaluation of brain abnormalities in Veterans with other neurodegenerative and neuropsychiatric disorders.

National Institute of Health (NIH)
Veterans Affairs (VA)
Non-HHS Research Projects (I01)
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Neurobiology C (NURC)
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VA Northern California Health Care System
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