Prediction of Motor Outcome after Acute Stroke using Diffusional Kurtosis Imaging

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DESCRIPTION (provided by applicant): Approximately one-third of all stroke patients endure persistent motor disability after their initial stroke. Rehabilitative interventions are crucial fo promoting motor recovery aimed at improving patients' ability to independently manage daily activities. Decisions regarding the design of specific rehabilitation protocols are governed by the clinical assessment of a patient's potential for motor recovery, among other factors. Diffusion tensor imaging (DTI) is a promising tool for quantifying stroke- related microstructural brain damage thought to be associated with eventual functional recovery. For chronic stroke, several DTI studies have demonstrated that poor motor function recovery is associated with low fractional anisotropy of water diffusion in the ipsilesional corticospinal tract. For acute stroke, however, corresponding DTI results have not been reliably demonstrated. Since delayed efforts for rehabilitation often lead to worse motor outcome, improved neuroimaging techniques for evaluating the potential for recovery during the acute stages of stroke would potentially be valuable for the planning of appropriate and timely rehabilitative intervention. In brain tissue, he presence of diffusion barriers, such as cellular membranes and organelles, causes water diffusion to be markedly non-Gaussian. Because DTI nonetheless assumes water diffusion to be Gaussian, its ability to characterize tissue microstructure is necessarily incomplete. To overcome this limitation, we have developed a clinically feasible diffusion MRI method that accounts for diffusional non-Gaussianity (i.e. kurtosis) called diffusional kurtosis imaging (DKI). Furthermore, diffusion metrics obtained from either DTI or DKI are bulk physical properties with no explicit link to tissue properties. To address this second limitation, we have developed a technique called cerebral microenvironment modeling (CMM) that provides estimates of specific tissue microstructural properties, such as neurite (i.e. axons and dendrites) density, orientation distribution and compartmental diffusion coefficients, by utilizing the diffusion metrics derived from DKI. Our overall hypothesis is that tissue properties obtained from our new CMM method are sensitive biomarkers of ischemic brain injury, which may be reliable prognostic indicators of motor function recovery after acute ischemic stroke. This hypothesis will be tested by the following Specific Aims: (1) to determine the relationship between CMM metrics and histologically defined brain cytoarchitecture after ischemic stroke; and (2) to investigate the association between CMM metrics and spontaneous and rehabilitation-induced motor recovery after stroke. Successful completion of this project will provide sensitive and biophysically interpretable biomarkers of spontaneous and rehabilitation-induced stroke recovery.