  We propose a statistical learning model for classifying cognitive processes
based on distributed patterns of neural activation in the brain, acquired via
functional magnetic resonance imaging (fMRI). In the proposed learning method,
local meshes are formed around each voxel. The distance between voxels in the
mesh is determined by using a functional neighbourhood concept. In order to
define the functional neighbourhood, the similarities between the time series
recorded for voxels are measured and functional connectivity matrices are
constructed. Then, the local mesh for each voxel is formed by including the
functionally closest neighbouring voxels in the mesh. The relationship between
the voxels within a mesh is estimated by using a linear regression model. These
relationship vectors, called Functional Connectivity aware Local Relational
Features (FC-LRF) are then used to train a statistical learning machine. The
proposed method was tested on a recognition memory experiment, including data
pertaining to encoding and retrieval of words belonging to ten different
semantic categories. Two popular classifiers, namely k-nearest neighbour (k-nn)
and Support Vector Machine (SVM), are trained in order to predict the semantic
category of the item being retrieved, based on activation patterns during
encoding. The classification performance of the Functional Mesh Learning model,
which range in 62%-71% is superior to the classical multi-voxel pattern
analysis (MVPA) methods, which range in 40%-48%, for ten semantic categories.
