Dynamic causal modelling for functional near-infrared spectroscopy

S Tak, A M Kempny, K J Friston, A P Leff, W D Penny

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.

Original languageEnglish
Pages (from-to)338-349
Number of pages12
Early online date25 Feb 2015
Publication statusPublished - 1 May 2015


  • Brain Mapping
  • Humans
  • Imagination
  • Neurological Models
  • Motor Activity
  • Motor Cortex
  • Nerve Net
  • Near-Infrared Spectroscopy

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