We propose a method for retrieving similar fMRI statistical images given a query fMRI statistical image. Our
method thresholds the voxels within those images and extracts spatially distinct regions from the voxels that
remain. Each region is defined by a feature vector that contains the region centroid, the region area, the average
activation value for all the voxels within that region, the variance of those activation values, the average distance
of each voxel within that region to the region's centroid, and the variance of the voxel's distance to the region's
centroid. The similarity between two images is obtained by the summed minimum distance of their constituent
feature vectors. Results on a dataset of fMRI statistical images from experiments involving distinct cognitive
tasks are shown.
This work introduces a MATLAB-based tool developed for investigating functional connectivity in the brain.
Independent component analysis (ICA) is used as a measure of voxel similarity which allows the user to find and view
statistically independent maps of correlated voxels. These maps of correlated voxel activity may indicate functionally
connected regions. Specialized clustering and feature extraction techniques have been designed to find and characterize
clusters of activated voxels, which allows comparison of the spatial maps of correlation across subjects. This method is
also used to compare the ICA generated images to fMRI images showing statistically significant activations generated by
Statistical Parametric Mapping (SPM). The capability of querying specific coordinates in the brain supports integration
and comparison with other data modalities such as Cortical Stimulation Mapping and Single Unit Recordings.