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Abstract:
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Functional magnetic resonance imaging (fMRI) techniques, a common tool to measure neuronal activity by detecting blood flow changes, have experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. Fully Bayesian approaches allow flexible modeling of spatial and temporal correlations in the data, as well as the integration of multi-modal data. In this talk I will look at models for inference on brain connectivity. I will present a model for the analysis of temporal dynamics of functional networks in task-based fRMi data and a multi-subject vector autoregressive (VAR) modeling approach for inference on effective connectivity.
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