Large-Scale Biophysical Network Models

Schematic of a biophysical network model

Brain imaging of large-scale brain networks using fMRI and M/EEG 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 our research is to develop large-scale biophysical network models. These are used to gain insights into the underlying generative mechanisms of brain network activity; particularly in the case of spontaneous, resting-state 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.

References

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).