Poster
GP CaKe: Effective brain connectivity with causal kernels
Luca Ambrogioni · Max Hinne · Marcel Van Gerven · Eric Maris
Pacific Ballroom #150
Keywords: [ Causal Inference ] [ Connectomics ] [ Dynamical Systems ] [ Network Analysis ] [ Neuroscience ] [ Time Series Analysis ] [ Gaussian Processes ] [ Bayesian Nonparametrics ]
A fundamental goal in network neuroscience is to understand how activity in one brain region drives activity elsewhere, a process referred to as effective connectivity. Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity. The approach combines the tractability and flexibility of autoregressive modeling with the biophysical interpretability of dynamic causal modeling. The causal kernels are learned nonparametrically using Gaussian process regression, yielding an efficient framework for causal inference. We construct a novel class of causal covariance functions that enforce the desired properties of the causal kernels, an approach which we call GP CaKe. By construction, the model and its hyperparameters have biophysical meaning and are therefore easily interpretable. We demonstrate the efficacy of GP CaKe on a number of simulations and give an example of a realistic application on magnetoencephalography (MEG) data.
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