Multi-scale higher order singular value decomposition (MS-HoSVD) for resting-state FMRI compression and analysis

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 6299 - 6303
• Main Authors: Ozdemir, Alp, Villafane-Delgado, Marisel, Zhu, David C., Iwen, Mark A., Aviyente, Selin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2017
• Subjects: Algorithm design and analysis; Higher Order SVD; Indexes; Magnetic resonance imaging; Neuroimaging; resting state fMRI; Tensile stress; tensor; Wavelet transforms
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7953368

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.