alexa Statistical Analysis of Large Cross-Covariance and Cros
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Statistical Analysis of Large Cross-Covariance and Cross-Correlation Matrices Produced by fMRI Images

Sam Efromovich* and Ekaterina Smirnova

Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, 75083, USA

*Corresponding Author:
Sam Efromovich
Department of Mathematical Sciences
The University of Texas at Dallas, Richardson, TX 75080, USA
Tel: 972-883-2161
E-mail: [email protected]

Received date: February 28 2014; Accepted date: March 25, 2014; Published date: March 31, 2014

Citation: Efromovich S, Smirnova E (2014) Statistical Analysis of Large Cross- Covariance and Cross-Correlation Matrices Produced by fMRI Images. J Biomet Biostat 5:193. doi:10.4172/2155-6180.1000193

Copyright: © 2014 Efromovich S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.

 

Abstract

The paper describes the theory, methods and application of statistical analysis of large-p-small-n cross-correlation matrices arising in fMRI studies of neuroplasticity, which is the ability of the brain to recognize neural pathways based on new experience and change in learning. Traditionally these studies are based on averaging images over large areas in right and left hemispheres and then finding a single cross-correlation function. It is proposed to conduct such an analysis based on a voxel-to-voxel level which immediately yields large cross-correlation matrices. Furthermore, the matrices have an interesting property to have both sparse and dense rows and columns. Main steps in solving the problem are: (i) treat observations, available for a single voxel, as a nonparametric regression; (ii) use a wavelet transform and then work with empirical wavelet coefficients; (iii) develop the theory and methods of adaptive simultaneous confidence intervals and adaptive rate-minimax thresholding estimation for the matrices. The developed methods are illustrated via analysis of fMRI experiments and the results allow us not only conclude that during fMRI experiments there is a change in cross-correlation between left and right hemispheres (the fact well known in the literature), but that we can also enrich our understanding how neural pathways are activated and then remain activated in timeon a single voxel-to-voxel level.

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