title: On nodes and modes in resting state fMRI.
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.
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: k.friston@ucl.ac.uk.
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
authors.affiliation: Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK.
authors.forename: Joshua
authors.initials: J
authors.lastname: Kahan
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, 75270, Pakistan.
authors.forename: Adeel
authors.initials: A
authors.lastname: Razi
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
authors.forename: Klaas Enno
authors.initials: KE
authors.lastname: Stephan
authors.affiliation: Computational Cognitive Neuroscience Laboratory, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
authors.forename: Olaf
authors.initials: O
authors.lastname: Sporns
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D063132:Connectome; D006801:Humans; D008279:Magnetic Resonance Imaging; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D012146:Rest / Q000502:physiology*
keywords: Bayesian; Criticality; Dynamic causal modelling; Effective connectivity; Free energy; Functional connectivity; Lyapunov exponents; Proximity graph; Resting state; Self-organisation; fMRI