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Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy
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Dynamic causal modelling for functional near-infrared spectroscopy.

S Tak1, A M Kempny2, K J Friston1

  • 1Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.

Neuroimage
|March 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces Dynamic Causal Modelling for functional near-infrared spectroscopy (fNIRS) data, enabling directional brain connectivity analysis. The method reveals how motor imagery suppresses primary motor cortex activity via supplementary motor area influence.

Keywords:
Dynamic causal modellingEffective connectivityFunctional near-infrared spectroscopy

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Cognitive Science

Background:

  • Functional near-infrared spectroscopy (fNIRS) measures brain activity via hemoglobin changes.
  • Current fNIRS methods lack the ability to determine the directionality of neuronal connections.
  • Understanding directed brain connectivity is crucial for neuroscience research.

Purpose of the Study:

  • To develop and validate a method for inferring directed neuronal connectivity from fNIRS data.
  • To apply Dynamic Causal Modelling (DCM) to fNIRS signals for analyzing effective connectivity.
  • To investigate directed interactions between motor areas during motor imagery and execution.

Main Methods:

  • Application of Dynamic Causal Modelling (DCM) to fNIRS data.
  • Development of a generative model linking fNIRS signals to hidden neuronal states.
  • Inversion of the generative model using a Bayesian framework (variational Laplace) for parameter inference.
  • Analysis of experimental data from motor imagery and motor execution tasks.

Main Results:

  • The study successfully inferred directed connectivity from fNIRS data.
  • Effective connectivity from the supplementary motor area to the primary motor cortex was identified.
  • Motor imagery was shown to negatively modulate this directed connectivity, suppressing primary motor cortex activity.
  • Results align with previous fMRI findings, validating the DCM-fNIRS approach.

Conclusions:

  • The proposed DCM-fNIRS method allows for the inference of directed brain interactions.
  • This technique advances the study of neuronal dynamics and effective connectivity using optical measurements.
  • The findings contribute to a deeper understanding of motor control and cognitive processes.