(supplied by applicant) ? ? Brain stroke is one of the most common medical and social problems in the world and a leading cause of chronic disabilities. Patients who survive the acute phase may partially recover some of the lost brain functions. Part of this process of functional recovery is attributed to brain plasticity. We believe that tissue organization in cortex and white matter plays an important role in determining plasticity. Identifying and understanding the factors involved in this process will likely contribute not only to the understanding of brain function but also to the design of novel therapeutic options for acute stroke. In agreement with the NIH mission of investigating ways to prevent and reduce the burden of neurological diseases, we propose to analyze the evolution of cortical and white matter architectural changes after stroke. Though cortical plasticity is relatively well explored by functional imaging, white matter reorganization was up to now an intractable problem. The development of new analytical tools, such as the diffusion tensor- magnetic resonance imaging (DT-MRI), allows this important issue to be properly assessed. Therefore, the general goal of this research program is to establish the changes of connectivity tracts in patients after stroke affecting motor function. We will: 1) Establish the DT-MRI maps of white matter in subacute and chronic phases post-stroke. This method uses fractional anisotropy (FA) and apparent coefficient diffusion (ADC) measurements to indirectly determine the architecture of neuronal fibers; 2) Establish the active areas during a motor-task with functional MRI (fMRI) in subacute and chronic phases post-stroke to study cortical rewriting in addition to white matter remodeling; 3) Compare DT-MRI in the affected hemispheres in the subacute and chronic phases post-stroke with non-affected hemispheres and with normal control' brains to investigate a possible remodeling of white matter and in which phase post stroke it occurs. We will use a pixel-based analysis with manual-regions of interest (R0l)-based and a more global normalization-based approach to detect differences in areas not suspected a priori; 4) Detect differences on DT-MRI and fMRI between phases post stroke in the same individual and compare them with a score model of patient outcome indices, trying to establish a pattern of connectivity related to recovering of function; 5) Analyze the relationship between DT-MRI and fMRI of patients to correlate alterations in cortical activation with development of new white matter connections or recruitment of pathways that already exist. 6) Establish the connectivity among the areas of interest using tractography. If the analyses performed in aims #1 through #5 will likely identify areas of white matter remodeling with signs of functional evolvement. We will use the end of these areas to use as seed points to design the points of interest. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
International Research Fellowships (FIC) (F05)
Project #
1F05NS059230-01
Application #
7277482
Study Section
Special Emphasis Panel (ZNS1-SRB-M (39))
Program Officer
Golanov, Eugene V
Project Start
2007-06-21
Project End
2009-06-20
Budget Start
2007-06-21
Budget End
2008-06-20
Support Year
1
Fiscal Year
2007
Total Cost
$51,278
Indirect Cost
Name
Johns Hopkins University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Faria, Andreia Vasconcellos; Landau, Barbara; O'Hearn, Kirsten M et al. (2012) Quantitative analysis of gray and white matter in Williams syndrome. Neuroreport 23:283-9
Faria, Andreia V; Hoon, Alexander; Stashinko, Elaine et al. (2011) Quantitative analysis of brain pathology based on MRI and brain atlases--applications for cerebral palsy. Neuroimage 54:1854-61
Faria, Andreia V; Zhang, Jiangyang; Oishi, Kenichi et al. (2010) Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage 52:415-28