Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions—posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS, etc.). We emphasize that the multimodal, multiscale nature of neuroimaging data is well reflected by a multiway (tensor) structure where the underlying processes can be summarized by a relatively small number of components or “atoms.” We introduce Markov–Penrose diagrams—an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via multiway partial least squares and coupled matrix-tensor factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.
['Esin Karahan', 'Pedro A. Rojas-Lopez', 'Maria L. Bringas-Vega', 'Pedro A. Valdés-Hernández', 'Pedro A. Valdes-Sosa']
Tensor Analysis and Fusion of Multimodal Brain Images