Large-Scale Biophysical Network Models
Resting state brain imaging has seen rapid growth in recent years. However, much of this work has been phenomenological, and hard to relate to neuronal processes. As a result an active area of research is the development of large-scale biophysical network models to gain insight into the underlying generative mechanisms of spontaneous brain activity. These biophysical network models combine knowledge of the anatomical (white matter) connectivity, with models that capture the dynamic behaviour of populations of neurons within the grey matter. These are used to predict the spatio-temporal dynamics of real functional neuroimaging data.
Woolrich and Stephan. Biophysical network models and the human connectome. Neuroimage (2013) vol. 80 (C) pp. 330-338
Cabral et al. Exploring mechanisms of spontaneous MEG functional connectivity: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations. Neuroimage (2013) pp. 1-13
Hadida et al. Bayesian Optimisation of Large-Scale Biophysical Networks. Neuroimage (2018).
Abeysuriya et al. A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks. PLoS Computational Biology (2018). 14(2).