fMRI Analysis Using Neighborhood Correlation and Autoregression Analysis

Functional magnetic resonance imaging (fMRI) has become an important tool for brain function studies. Statistical Parametric Mapping (SPM) provided a model-driven method for fMRI studies. Different from SPM, Independent Component Analysis (ICA) provided a data-driven method. Here we are trying to give a data-driven method and solve the high-complexity problem in clustering method and ICA. Correlation coefficient had been used in many ways for fMRI analysis recently and proved efficient. Autoregression analysis is utilized in different time series analysis. Here, we are going to take correlation coefficient and autoregression analysis as two filters, to remove most of the voxels which are considered to be noise. After data reduction, we provide Hierarchical cluster and correlation algorithm to those remaining voxels. Providing the method to the fMRI data from SPM’s auditory experiment, we can correctly get the auditory cortical.