One of my main current interests is the model-based analysis of fMRI data to explore the brain coordination when humans perform a task. The model allows for the estimation of directional interactions between brain regions, denoted by whole-brain effective connectivity (EC), moving beyond a simple phenomenological description of their observed correlations (functional connectivity). The modifications of the EC pattern at the network level hint at the selection of specific pathways for, e.g., sensory integration. I also plan to extend the existing framework to interpret EEG and MEG data.Gilson et al. (2016) PLoS Comput Biol (model optimization)
My MSCA project applies the network-specific analysis to multiunit activity recorded in monkeys in collaboration with Prof Alex Thiele (Newcastle University). The goal is to compare the effective connectivity estimated from observed network activity to a-priori knowledge about anatomical connections.
With Adrià Tauste Campo from Gustavo's CNS lab at UPF, we've developed a non-parametric method for connectivity estimation from observed network activity, based on the multivariate autoregressive (MVAR) process, which can be seen as an alternative to Granger causality analysis.Gilson et al. (in press) Network Neurosci; preprint on biorxiv
I am still interested in the dynamics of spiking networks, with and without plasticity (e.g., STDP). Mathematical models formalise how the network structure and neural dynamics determine plasticity, which in turns modifies the network dynamics. I aim to extend this framework to function and coding.Gilson et al. (2010) Front Comput Neurosci 4: 23