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Regression DCM for fMRI.
Journal article

Regression DCM for fMRI.

  • Frässle S Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, 8032 Zurich, Switzerland. Electronic address: stefanf@biomed.ee.ethz.ch.
  • Lomakina EI 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.
  • Razi A Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Department of Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan.
  • Friston KJ Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, United Kingdom.
  • Buhmann JM Department of Computer Science, ETH Zurich, 8032 Zurich, Switzerland.
  • Stephan KE 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.
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  • 2017-03-06
Published in:
  • NeuroImage. - 2017
Bayesian regression
Connectomics
Dynamic causal modeling
Effective connectivity
Generative model
Variational Bayes
Adult
Bayes Theorem
Brain
Connectome
Humans
Magnetic Resonance Imaging
Models, Neurological
English 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.
Language
  • English
Open access status
hybrid
Identifiers
Persistent URL
https://sonar.ch/global/documents/217706
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