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We represent the sequence of fMRI (Functional Magnetic Resonance Imaging)brain volumes recorded during a cognitive stimulus by a graph which consists ofa set of local meshes. The corresponding cognitive process, encoded in thebrain, is then represented by these meshes each of which is estimated assuminga linear relationship among the voxel time series in a predefined locality.First, we define the concept of locality in two neighborhood systems, namely,the spatial and functional neighborhoods. Then, we construct spatially andfunctionally local meshes around each voxel, called seed voxel, by connectingit either to its spatial or functional p-nearest neighbors. The mesh formedaround a voxel is a directed sub-graph with a star topology, where thedirection of the edges is taken towards the seed voxel at the center of themesh. We represent the time series recorded at each seed voxel in terms oflinear combination of the time series of its p-nearest neighbors in the mesh.The relationships between a seed voxel and its neighbors are represented by theedge weights of each mesh, and are estimated by solving a linear regressionequation. The estimated mesh edge weights lead to a better representation ofinformation in the brain for encoding and decoding of the cognitive tasks. Wetest our model on a visual object recognition and emotional memory retrievalexperiments using Support Vector Machines that are trained using the mesh edgeweights as features. In the experimental analysis, we observe that the edgeweights of the spatial and functional meshes perform better than thestate-of-the-art brain decoding models.

Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding