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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.


DCM fit to optical signal measured during motor execution and motor imagery. Black and blue plots indicate average optical density changes measured at wavelengths 760 nm and 850 nm, respectively. Red plots indicate the corresponding estimates from DCM model 9. To compare model fit to the measurements, we selected channels 2, 5, and channels 3, 4, whose sensitivities to M1 and SMA were highest among eight channels, respectively. DCM produces similar traces to the fNIRS data at both wavelengths. The solid black line indicates the empirical task periods (5 s), including motor execution and motor imagery.
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f0040: DCM fit to optical signal measured during motor execution and motor imagery. Black and blue plots indicate average optical density changes measured at wavelengths 760 nm and 850 nm, respectively. Red plots indicate the corresponding estimates from DCM model 9. To compare model fit to the measurements, we selected channels 2, 5, and channels 3, 4, whose sensitivities to M1 and SMA were highest among eight channels, respectively. DCM produces similar traces to the fNIRS data at both wavelengths. The solid black line indicates the empirical task periods (5 s), including motor execution and motor imagery.

Mentions: As an example of the accuracy of DCM model fits, Fig. 8 shows the predicted and measured optical density signals. Note that our DCM models comprise two neuronal sources, including M1 and SMA, whose activities each generate optical density changes in all eight channels using the forward model in Eq. (13). To compare model fit to the channel measurements, we selected channels 2, 5, and channels 3, 4, whose sensitivities to M1 and SMA are highest among eight channels, respectively. DCM produces similar traces to the actual fNIRS data at both wavelengths 760 nm and 850 nm.


Dynamic causal modelling for functional near-infrared spectroscopy.

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

DCM fit to optical signal measured during motor execution and motor imagery. Black and blue plots indicate average optical density changes measured at wavelengths 760 nm and 850 nm, respectively. Red plots indicate the corresponding estimates from DCM model 9. To compare model fit to the measurements, we selected channels 2, 5, and channels 3, 4, whose sensitivities to M1 and SMA were highest among eight channels, respectively. DCM produces similar traces to the fNIRS data at both wavelengths. The solid black line indicates the empirical task periods (5 s), including motor execution and motor imagery.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0040: DCM fit to optical signal measured during motor execution and motor imagery. Black and blue plots indicate average optical density changes measured at wavelengths 760 nm and 850 nm, respectively. Red plots indicate the corresponding estimates from DCM model 9. To compare model fit to the measurements, we selected channels 2, 5, and channels 3, 4, whose sensitivities to M1 and SMA were highest among eight channels, respectively. DCM produces similar traces to the fNIRS data at both wavelengths. The solid black line indicates the empirical task periods (5 s), including motor execution and motor imagery.
Mentions: As an example of the accuracy of DCM model fits, Fig. 8 shows the predicted and measured optical density signals. Note that our DCM models comprise two neuronal sources, including M1 and SMA, whose activities each generate optical density changes in all eight channels using the forward model in Eq. (13). To compare model fit to the channel measurements, we selected channels 2, 5, and channels 3, 4, whose sensitivities to M1 and SMA are highest among eight channels, respectively. DCM produces similar traces to the actual fNIRS data at both wavelengths 760 nm and 850 nm.

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.