The role of node dynamics in shaping emergent functional connectivity patterns in the brain

Author:

Forrester Michael1ORCID,Crofts Jonathan J.2ORCID,Sotiropoulos Stamatios N.345ORCID,Coombes Stephen1ORCID,O’Dea Reuben D.1ORCID

Affiliation:

1. Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK

2. Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, UK

3. Sir Peter Mansfield Imaging Centre, Queen’s Medical Centre, University of Nottingham, Nottingham, UK

4. Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), University of Oxford, Oxford, UK

5. National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queen’s Medical Centre, Nottingham, UK

Abstract

The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure–function issue, treating a system of Jansen–Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure–function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called ‘false bifurcations’ (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states.

Funder

Engineering and Physical Sciences Research Council

Publisher

MIT Press - Journals

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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