Mesh learning approach for brain data modeling

The major goal of this study is to model the memory process using neural activation patterns in the brain. To achieve this goal, neural activation was acquired using functional Magnetic Resonance Imaging (fMRI) during memory encoding and retrieval. fMRI are known are trained for each class using a learning system. The most important component of this learning system is feature space. In this project, an original feature space for the fMRI data is proposed. This feature space is defined by a mesh network which models the relationship between voxels. In the suggested mesh network, the distance between voxels is determined by using physical and functional neighborhood concepts. For the functional neighborhood, the similarities between the time series, gained from voxels, are measured. With the proposed method, a data set with 10 classes is used for the encoding and retrieval processes, and the classifier is trained with the learning algorithms in order to predict the class the data belongs.