title: Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions.
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.
authors.affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK. Electronic address: Prejaas.tewarie@nottingham.ac.uk.
authors.forename: P
authors.initials: P
authors.lastname: Tewarie
authors.affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
authors.forename: M G
authors.initials: MG
authors.lastname: Bright
authors.affiliation: Department of Clinical Neurophysiology and MEG Center, VU University Medical Centre, Amsterdam, The Netherlands.
authors.forename: A
authors.initials: A
authors.lastname: Hillebrand
authors.affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
authors.forename: S E
authors.initials: SE
authors.lastname: Robson
authors.affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
authors.forename: L E
authors.initials: LE
authors.lastname: Gascoyne
authors.affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
authors.forename: P G
authors.initials: PG
authors.lastname: Morris
authors.affiliation: Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands.
authors.forename: J
authors.initials: J
authors.lastname: Meier
authors.affiliation: Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, The Netherlands.
authors.forename: P
authors.initials: P
authors.lastname: Van Mieghem
authors.affiliation: Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
authors.forename: M J
authors.initials: MJ
authors.lastname: Brookes
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006439:Hemodynamics; D006801:Humans; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net / Q000502:physiology*
keywords: Functional connectivity; Functional magnetic resonance imaging; MEG; Magnetoencephalography; Mapping; Multivariate Taylor series; RSN; Relationship between fMRI and MEG; Resting state network; fMRI
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