abstract Pedro Valdes Sosa

Pedro Valdes Sosa (Joint CHINA-CUBA Laboratory for Frontier Research in Translational Neurotechnology)

Data and Model Driven EEG/fMRI fusion with an emphasis on Brain Connectivity

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, multi-scale nature of neuroimaging data is well reflected by a multi-way (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 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 Nonlinear Partial Least Squares and Coupled Tensor Fusion. 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 show the potential of the methods and suggest their use in other scientific domains.