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Functional MRI modeling is challenged by long-range coupling, non-linearity, and lack of detailed physiological information. I will review our progress in modeling the spatio-temporal dynamics of fMRI including hemodynamic deconvolution, blind deconvolution, and brain state decoding based on spatio- ...

Brain imaging by PET, MR, EEG, and MEG has become a cornerstone in systems level neuroscience. Statistical analyses of neuroimage datasets face many interesting challenges including non-linearity and multi-scale spatial and temporal dynamics. The objectives of neuroimaging are dual, we are intereste ...