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.
Highlights
Functional near-infrared spectroscopy is a noninvasive method for monitoring hemodynamic changes in the brain (Jobsis, 1977; Villringer et al, 1993; Hoshi, 2007; Ferrari and Quaresima, 2012; Scholkmann et al, 2014). fNIRS works by shining near-infrared light in the spectral range between 650 and 950 nm from fiber-optic emitters placed on the scalp
Because the variational Bayesian estimation algorithm is the same as that used for Dynamic Causal Modelling (DCM) for other imaging modalities, this paper focuses on development of a generative model of how observed fNIRS data are caused by the interactions among hidden neuronal states
The generative model of fNIRS data is created by linking the fNIRS optics equation to the hemodynamic and neurodynamic equations
Summary
Functional near-infrared spectroscopy (fNIRS) is a noninvasive method for monitoring hemodynamic changes in the brain (Jobsis, 1977; Villringer et al, 1993; Hoshi, 2007; Ferrari and Quaresima, 2012; Scholkmann et al, 2014). fNIRS works by shining near-infrared light in the spectral range between 650 and 950 nm from fiber-optic emitters placed on the scalp. As compared to fMRI, fNIRS provides a more direct measure of changes in HbO, HbR, and total hemoglobin (HbT), and the time series are sampled at high temporal resolution. It has proved to be an effective tool for studying physiological mechanisms in the healthy brain and in cerebrovascular disease (Highton et al, 2010; Wolf et al, 2012; Obrig, 2014). It is finding unique applications in clinical areas, including bedside monitoring of infants, and studies of auditory and language systems (Lloyd-Fox et al, 2010; Eggebrecht et al, 2014)
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