title: Regression DCM for fMRI.
abstract: The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland. Electronic address: stefanf@biomed.ee.ethz.ch.
authors.forename: Stefan
authors.initials: S
authors.lastname: Frässle
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland.
authors.forename: Ekaterina I
authors.initials: EI
authors.lastname: Lomakina
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Department of Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan.
authors.forename: Adeel
authors.initials: A
authors.lastname: Razi
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom.
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
authors.affiliation: Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland.
authors.forename: Joachim M
authors.initials: JM
authors.lastname: Buhmann
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom.
authors.forename: Klaas E
authors.initials: KE
authors.lastname: Stephan
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological
keywords: Bayesian regression; Connectomics; Dynamic causal modeling; Effective connectivity; Generative model; Variational Bayes