Dynamical Causal Models
Dynamical Causal Modeling (DCM) is a Bayesian estimation framework for fitting differential equation models of neuronal activity to brain imaging data. I have initially focussed on Bayesian model comparison methods required for comparing different models. This allows researchers to formally compare different theories about the architectures of the large-scale neural networks that mediate human perception, cognition and action.More recently, I have worked on descriptions of neurodynamics based on coupled oscillator models. This allows one to study the dynamics of synchronization processes in the brain, which neuroscientists believe may provide a substrate for information transfer among different brain regions.
The picture above shows a flow diagram of phase dynamics as estimated from three brain regions source reconstructed from MEG data, with the x-axis being phase difference between Medial Temporal Lobe (MTL) and Visual Cortex (VIS), and the y-axis being phase difference between Inferior Frontal Gyrus (IFG) and VIS. Red dots show (fitted) phase trajectories for ten working memory trials, which all converge such that the phase difference between IFG and MTL is almost zero. Additionally, Bayesian model comparison revealed that VIS enslaves IFG and MTL.
This project is an ongoing collaboration with Karl Friston, Klaas Stephan, Stefan Kiebel, Andrea Mechelli, Vladimir Litvak, Emrah Duzel and Lluis Fuentemilla.