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In this work, we propose a novel framework to encode the local connectivitypatterns of brain, using Fisher Vectors (FV), Vector of Locally AggregatedDescriptors (VLAD) and Bag-of-Words (BoW) methods. We first obtain localdescriptors, called Mesh Arc Descriptors (MADs) from fMRI data, by forminglocal meshes around anatomical regions, and estimating their relationshipwithin a neighborhood. Then, we extract a dictionary of relationships, called\textit{brain connectivity dictionary} by fitting a generative Gaussian mixturemodel (GMM) to a set of MADs, and selecting the codewords at the mean of eachcomponent of the mixture. Codewords represent the connectivity patterns amonganatomical regions. We also encode MADs by VLAD and BoW methods using thek-Means clustering.  We classify the cognitive states of Human Connectome Project (HCP) task fMRIdataset, where we train support vector machines (SVM) by the encoded MADs.Results demonstrate that, FV encoding of MADs can be successfully employed forclassification of cognitive tasks, and outperform the VLAD and BoWrepresentations. Moreover, we identify the significant Gaussians in mixturemodels by computing energy of their corresponding FV parts, and analyze theireffect on classification accuracy. Finally, we suggest a new method tovisualize the codewords of brain connectivity dictionary.

Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification