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Dynamic causal modelling for functional near-infrared spectroscopy.

Tak S, Kempny AM, Friston KJ, Leff AP, Penny WD - Neuroimage (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: s.tak@ucl.ac.uk.

No MeSH data available.


Schematic of the generative model of fNIRS data. The neurodynamic equation uses linear differential equations and a single state variable per region describing neural activity. Coupling parameter matrices A, Bi, and C represent the average connectivity among regions, the modulation of effective connectivity by experimental manipulation, and the influence of inputs on regions, respectively. The hemodynamic equation uses the Balloon model and its extensions to describe how neural activity causes a change in a flow inducing signal which in turn causes an increase in blood flow with concomitant changes in relative blood volume and deoxy-hemoglobin. The optics equation uses a sensitivity matrix, S, describing how changes in hemodynamic sources cause changes in optical measurements. Potential pial vein contamination of the fNIRS measurements is corrected using matrices WH and WQ. Spatially distributed hemodynamic source is generated using Gaussian spatial smoothing kernel K.
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f0005: Schematic of the generative model of fNIRS data. The neurodynamic equation uses linear differential equations and a single state variable per region describing neural activity. Coupling parameter matrices A, Bi, and C represent the average connectivity among regions, the modulation of effective connectivity by experimental manipulation, and the influence of inputs on regions, respectively. The hemodynamic equation uses the Balloon model and its extensions to describe how neural activity causes a change in a flow inducing signal which in turn causes an increase in blood flow with concomitant changes in relative blood volume and deoxy-hemoglobin. The optics equation uses a sensitivity matrix, S, describing how changes in hemodynamic sources cause changes in optical measurements. Potential pial vein contamination of the fNIRS measurements is corrected using matrices WH and WQ. Spatially distributed hemodynamic source is generated using Gaussian spatial smoothing kernel K.

Mentions: The generative model for fNIRS data comprises three components: (i) neurodynamics describing neural activity in terms of inter-regional interactions and its experimentally induced modulation (Friston et al., 2003), (ii) hemodynamics linking neural activity with the changes in total hemoglobin, and deoxy-hemoglobin based on the Balloon model (Friston et al., 2000; Buxton et al., 2004; Cui et al., 2010), and (iii) optics relating the hemodynamic sources to optical density changes (Delpy et al., 1988; Arridge, 1999). A schematic of the generative model is summarized in Fig. 1. The following subsections describe each of these components. These are followed by sections on computing the optical sensitivity matrix and confounding effects that underlie the optical model, and a section on model estimation.


Dynamic causal modelling for functional near-infrared spectroscopy.

Tak S, Kempny AM, Friston KJ, Leff AP, Penny WD - Neuroimage (2015)

Schematic of the generative model of fNIRS data. The neurodynamic equation uses linear differential equations and a single state variable per region describing neural activity. Coupling parameter matrices A, Bi, and C represent the average connectivity among regions, the modulation of effective connectivity by experimental manipulation, and the influence of inputs on regions, respectively. The hemodynamic equation uses the Balloon model and its extensions to describe how neural activity causes a change in a flow inducing signal which in turn causes an increase in blood flow with concomitant changes in relative blood volume and deoxy-hemoglobin. The optics equation uses a sensitivity matrix, S, describing how changes in hemodynamic sources cause changes in optical measurements. Potential pial vein contamination of the fNIRS measurements is corrected using matrices WH and WQ. Spatially distributed hemodynamic source is generated using Gaussian spatial smoothing kernel K.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401444&req=5

f0005: Schematic of the generative model of fNIRS data. The neurodynamic equation uses linear differential equations and a single state variable per region describing neural activity. Coupling parameter matrices A, Bi, and C represent the average connectivity among regions, the modulation of effective connectivity by experimental manipulation, and the influence of inputs on regions, respectively. The hemodynamic equation uses the Balloon model and its extensions to describe how neural activity causes a change in a flow inducing signal which in turn causes an increase in blood flow with concomitant changes in relative blood volume and deoxy-hemoglobin. The optics equation uses a sensitivity matrix, S, describing how changes in hemodynamic sources cause changes in optical measurements. Potential pial vein contamination of the fNIRS measurements is corrected using matrices WH and WQ. Spatially distributed hemodynamic source is generated using Gaussian spatial smoothing kernel K.
Mentions: The generative model for fNIRS data comprises three components: (i) neurodynamics describing neural activity in terms of inter-regional interactions and its experimentally induced modulation (Friston et al., 2003), (ii) hemodynamics linking neural activity with the changes in total hemoglobin, and deoxy-hemoglobin based on the Balloon model (Friston et al., 2000; Buxton et al., 2004; Cui et al., 2010), and (iii) optics relating the hemodynamic sources to optical density changes (Delpy et al., 1988; Arridge, 1999). A schematic of the generative model is summarized in Fig. 1. The following subsections describe each of these components. These are followed by sections on computing the optical sensitivity matrix and confounding effects that underlie the optical model, and a section on model estimation.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: s.tak@ucl.ac.uk.

No MeSH data available.