Analyzing the information distribution in the fMRI measurements by estimating the degree of locality

In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each seed voxel and its surroundings by minimizing Akaike's Final Prediction Error (FPE) with respect to the mesh size. The degree of locality is represented by the optimum mesh size. Our results indicate that the local mesh size with the highest discriminative power varies across individual participants. The proposed method was tested on an fMRI study consisting of item recognition (IR) and judgment of recency (JOR) tasks. For each participant, the estimated arc weights of each local mesh with different mesh size are used to classify the type of memory judgment (i.e.IR or JOR). Classification accuracy for each participant was derived using k-Nearest Neighbor (k-NN) method. The results indicate that the proposed local mesh model with optimal mesh size can successfully represent discriminative information for neuroimaging data.