A short history of causal modeling of fMRI data

K.E. Stephan, A. Roebroeck

Research output: Contribution to journalArticleAcademicpeer-review

46 Citations (Scopus)

Abstract

Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced analyses of regional brain activity, but also laid the foundation for novel approaches to studying effective connectivity, which is essential for mechanistically interpretable accounts of neuronal systems. Dynamic causal modeling (DCM) and Granger causality (G-causality) modeling have since become the most frequently used techniques for inferring effective connectivity from fMRI data. In this paper, we provide a short historical overview of these approaches, describing milestones of their development from our subjective perspectives. (C) 2012 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)856-863
Number of pages8
JournalNeuroimage
Volume62
Issue number2
DOIs
Publication statusPublished - 15 Aug 2012

Keywords

  • Effective connectivity
  • Dynamic causal modeling
  • DCM
  • Granger causality
  • Granger causality mapping
  • GCM
  • Bayesian model selection
  • BMS
  • Model evidence
  • Brain Connectivity Workshop
  • TIME-RESOLVED FMRI
  • GRANGER CAUSALITY
  • EFFECTIVE CONNECTIVITY
  • CORTICAL INTERACTIONS
  • FUNCTIONAL CONNECTIVITY
  • AUTOREGRESSIVE MODELS
  • BAYESIAN-ESTIMATION
  • DYNAMICAL-SYSTEMS
  • INFORMATION-FLOW
  • PREDICTION ERROR

Cite this

Stephan, K.E. ; Roebroeck, A. / A short history of causal modeling of fMRI data. In: Neuroimage. 2012 ; Vol. 62, No. 2. pp. 856-863.
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A short history of causal modeling of fMRI data. / Stephan, K.E.; Roebroeck, A.

In: Neuroimage, Vol. 62, No. 2, 15.08.2012, p. 856-863.

Research output: Contribution to journalArticleAcademicpeer-review

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KW - BAYESIAN-ESTIMATION

KW - DYNAMICAL-SYSTEMS

KW - INFORMATION-FLOW

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