A method for functional network connectivity among spatially independent resting-state components in schizophrenia
Advances in information technology are making it possible to collect increasingly massive amounts of multidimensional, multi-modal neuroimaging data such as functional magnetic resonance imaging (fMRI). Current fMRI datasets involve multiple variables including multiple subjects, as well as both temporal and spatial data. These high dimensional datasets pose a challenge to the signal processing community to develop data reduction methods that can exploit their rich structure and extract meaningful summarizations. In this paper, we propose a tensor-based framework for data reduction and low-dimensional structure learning with a particular focus on reducing high dimensional fMRI data sets into physiologically meaningful network components. We develop a multiscale tensor factorization method for higher order data inspired by hybrid linear modeling and subspace clustering techniques. In particular, we develop a multi-scale HoSVD approach where a given tensor is first permuted and then partitioned into several sub-tensors each of which can be represented more efficiently. This multi-scale framework is applied to resting state fMRI data to identify the default mode network from compressed data.