Post processing Technique to Detect Active Voxels in fMRI Signals

Abstract

Universally acceptable data analysis techniques which use all the available, potential information from functional Magnetic Resonance Imaging (fMRI) of the brain are yet to be developed. This is despite the fact that fMRI yields rich temporal and spatial data for each subject scanned. Statistical analysis of fMRI signals corresponding to an auditory data set was performed to determine the contribution of a robust, unknown parameter to the fMRI response. Preprocessing steps were taken in order to remove confounding errors. The Maximum Likelihood Estimation Method was used to determine the unknown parameter contributing to the resulting data. The obtained parameter was used to test the significance of each voxel of the brain by the Wald Test. This process of evaluating the response of each voxel is called the Univariate Analysis, as the analysis is done voxel by voxel.