| Abstract: |
| Cortical neurons form coherent functional networks that are surprisingly stable across subjects and conditions. Together these networks comprise a set of functional units of the brain. Understanding their interactions can lead to better understanding of brain`s function and dysfunction due to disruption of the interaction structure. The most common way of assessing this structure are cross-correlation matrices (usually referred to as functional connectivity) but their drawbacks make us turn to modeling interactions via the directed graph of a Bayesian network (effective connectivity). Various brain imaging modalities contain different and arguably complementary information about interactions of functional network. Our goal is to bring together multimodal information to improve effective connectivity estimates.
In particular, I focus on the problem of finding a common denominator for causal structures learned from time series at different time scales. I will demonstrate 1) a general theory which explains the effects of undersampling on apparent causal structure in terms of the true structure at the causal time scale; 2) a forward algorithm that computes a graph structure at any given undersampling rate; and 3) an inverse algorithm to compute all of the candidate graphs that could have generated the given undersampled structure. |
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