UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

On nodes and modes in resting state fMRI.

Friston, KJ; Kahan, J; Razi, A; Stephan, KE; Sporns, O; (2014) On nodes and modes in resting state fMRI. Neuroimage , 99 pp. 533-547. 10.1016/j.neuroimage.2014.05.056. Green open access

[img]
Preview
PDF
1-s2.0-S1053811914004212-main.pdf

Download (6MB)

Abstract

This paper examines intrinsic brain networks in light of recent developments in the characterisation of resting state fMRI timeseries - and simulations of neuronal fluctuations based upon the connectome. Its particular focus is on patterns or modes of distributed activity that underlie functional connectivity. We first demonstrate that the eigenmodes of functional connectivity - or covariance among regions or nodes - are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections. This symmetry constraint is motivated by appealing to proximity graphs based upon multidimensional scaling. Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory. Technically, these modes have small negative Lyapunov exponents that approach zero from below. Interestingly, the superposition of modes - whose exponents are sampled from a power law distribution - produces classical 1/f (scale free) spectra. We conjecture that the emergence of dynamical instability - that underlies intrinsic brain networks - is inevitable in any system that is separated from external states by a Markov blanket. This conjecture appeals to a free energy formulation of nonequilibrium steady-state dynamics. The common theme that emerges from these theoretical considerations is that endogenous fluctuations are dominated by a small number of dynamically unstable modes. We use this as the basis of a dynamic causal model (DCM) of resting state fluctuations - as measured in terms of their complex cross spectra. In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents - that can also be interpreted as locations in a multidimensional scaling space. Model inversion provides not only estimates of edges or connectivity but also the topography and dimensionality of the underlying scaling space. Here, we focus on conceptual issues with simulated fMRI data and provide an illustrative application using an empirical multi-region timeseries.

Type: Article
Title: On nodes and modes in resting state fMRI.
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2014.05.056
Publisher version: http://dx.doi.org/10.1016/j.neuroimage.2014.05.056
Additional information: © 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/).
Keywords: Bayesian, Lyapunov exponents, criticality, dynamic causal modelling, effective connectivity, fMRI, free energy, functional connectivity, proximity graph, resting state, self-organisation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1432151
Downloads since deposit
57Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item