Robust White Matter Morphometry with Small Databases

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 DESCRIPTION (provided by applicant): Statistical comparison of neuroimaging data often requires large databases to produce reliable outcomes. However, in medical imaging studies, databases are usually small due to the difficulty in recruiting patients and volunteers. Samples are even more limited when parameters such as age or gender must be matched between healthy controls and patients. In situations as such, conventional statistical tests may become erroneous and generate either false positive or false negative detections. In addition, automatic image comparison approaches typically require a common reference frame that is often constructed from scans of healthy subjects by means of non-linear registration. However, registration methods are not perfect and may be prone to errors due to noise, artifacts, and complex variations in brain topology. Registration errors introduce structural variability that wil decrease the statistical power in detecting real meaningful differences. The objective of this project is to create a set of novel computational tools for robust statistical analysis of diffusio magnetic resonance imaging (MRI) data, particularly in situations where samples are noisy, limited, and exhibit complex shape variations. We propose three aims to achieve this objective. In Aim 1, we will devise a technique that will drastically increase the number of available samples for the estimation of diffusion statistics and their variability. This is achieved by identifying and agglomerating repetitive local information throughout an image to significantly increase sample size for improving estimation. We will further develop statistical techniques that will utilize these `repeated samples' for resampling- based non-parametric estimation of the variability of statistics of interest. In Aim 2, we will develop methods for effective and robust group and individual comparisons of diffusion statistics using a limited number of samples. This is achieved by explicitly correcting for registration errors via a block matching mechanism to ensure that comparisons are performed only between matching structures. Since variability due to registration errors are minimized, our method will significantly increase statistical power in detecting abnormalities. In addition, similar to Aim 1, our method will allow comparisons to be performed without imposing a priori, but often unrealistic, assumption on the distribution of the statistic of interest. In Aim 3, extensive evaluations of the methods developed in Aim 1 and Aim 2 will be carried out using databases associated with neuropsychiatric disorders, such as Alzheimer's disease. If successful, the statistical computational tools developed in this project will increase the statistical power of studies involving smaller databases and will allow detection of smaller effect sizes in studies with moderately-sized databases.