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 Most of the software tools for neuroimaging analysis are released and distributed as standalone desktop applications. Latest trends in neuroimaging studies are focusing more on scalability issues both related to larger data samples and cpu intensive computational methods. The open challenge is how to combine data management and data analysis. We may consider LONI, NeuGrid and INCF-AWS as the leading solutions to combine large data repositories and scalable computational architecture.

Given the critical importance of selective attention in everyday life for guiding our perceptions, actions, thoughts and memories, characterizing the brain mechanisms of attention is a major goal in mind/brain sciences. Understanding brain mechanisms for attention also has numerous applications, especially with regard to disorders of attention.

The general objective is the development of new brain decoding methods that use the spatial relation information in fRMI signals: (1) the development of a new feature selection method for brain decoding based on clustering ensemble algorithms that preserve the spatial relation among voxels and yield a consensus parcelation of the brain image; (2) the design of a graph representation of the brain image that captures all the information in a given parcelation; (3) the design of graph kernels able to measure the semantics inherit in the graph structure and with a low computational cost in its