Dynamic causal modelling for functional near-infrared spectroscopy

Tak, S, Kempny, A M, Friston, K J, Leff, A P and Penny, W D (2015) Dynamic causal modelling for functional near-infrared spectroscopy. NeuroImage, 111. pp. 338-349. ISSN 1053-8119

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Abstract

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.

Item Type: Article
Additional Information: Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Uncontrolled Keywords: brain mapping,humans,imagination,neurological models,motor activity,motor cortex,nerve net,near-infrared spectroscopy
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 19 Aug 2017 05:06
Last Modified: 23 Mar 2020 15:18
URI: https://ueaeprints.uea.ac.uk/id/eprint/64581
DOI: 10.1016/j.neuroimage.2015.02.035

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