Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.M.; Van Mieghem, P.F.A.; Brookes, M.J.

doi:10.1016/j.neuroimage.2016.01.053

Predicting haemodynamic networks using electrophysiology

Title

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Author

Tewarie, P. (University of Nottingham)
Bright, M.G. (University of Nottingham)
Hillebrand, A. (Amsterdam UMC)
Robson, S.E. (University of Nottingham)
Gascoyne, L.E. (University of Nottingham)
Morris, P.G. (University of Nottingham)
Meier, J.M. (TU Delft Network Architectures and Services)
Van Mieghem, P.F.A. (TU Delft Network Architectures and Services)
Brookes, M.J. (University of Nottingham)

Date

2016

Abstract

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology.

Subject

FMRI
Functional connectivity
Functional magnetic resonance imaging
Magnetoencephalography
Mapping
MEG
Multivariate Taylor series
Relationship between fMRI and MEG
Resting state network
RSN