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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.


Results of Bayesian model comparison. Family level inference indicated that models with spatially distributed hemodynamic source outperformed models with point sources. Moreover, together with Bayesian inference at the model level, the best model structure was model 9 in which task input could affect regional activity in both supplementary motor area (SMA) and primary motor cortex (M1), and motor imagery could modulate both the extrinsic and intrinsic connections.
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f0025: Results of Bayesian model comparison. Family level inference indicated that models with spatially distributed hemodynamic source outperformed models with point sources. Moreover, together with Bayesian inference at the model level, the best model structure was model 9 in which task input could affect regional activity in both supplementary motor area (SMA) and primary motor cortex (M1), and motor imagery could modulate both the extrinsic and intrinsic connections.

Mentions: DCM was then fitted to the optical density signal averaged across trials. Bayesian model selection compared DCM models which differed in spatial extent of hemodynamic source, regions receiving task input, and connections modulated by motor imagery (Fig. 5). Family level inference indicated that the models with spatially distributed hemodynamic source outperformed the models with point source. Moreover, together with Bayesian inference at the model level, the best model structure was model 9 in which task input could affect regional activity in both SMA and M1, and motor imagery modulated both extrinsic and intrinsic connections.


Dynamic causal modelling for functional near-infrared spectroscopy.

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

Results of Bayesian model comparison. Family level inference indicated that models with spatially distributed hemodynamic source outperformed models with point sources. Moreover, together with Bayesian inference at the model level, the best model structure was model 9 in which task input could affect regional activity in both supplementary motor area (SMA) and primary motor cortex (M1), and motor imagery could modulate both the extrinsic and intrinsic connections.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0025: Results of Bayesian model comparison. Family level inference indicated that models with spatially distributed hemodynamic source outperformed models with point sources. Moreover, together with Bayesian inference at the model level, the best model structure was model 9 in which task input could affect regional activity in both supplementary motor area (SMA) and primary motor cortex (M1), and motor imagery could modulate both the extrinsic and intrinsic connections.
Mentions: DCM was then fitted to the optical density signal averaged across trials. Bayesian model selection compared DCM models which differed in spatial extent of hemodynamic source, regions receiving task input, and connections modulated by motor imagery (Fig. 5). Family level inference indicated that the models with spatially distributed hemodynamic source outperformed the models with point source. Moreover, together with Bayesian inference at the model level, the best model structure was model 9 in which task input could affect regional activity in both SMA and M1, and motor imagery modulated both extrinsic and intrinsic connections.

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.