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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.


The model structure for the DCM analysis of motor execution and imagery data. (a) The model comprises two regions including supplementary motor area (SMA) and primary motor cortex (M1), and two inputs according to the experimental paradigm. The first input variable (i.e., task) encodes the occurrence of motor execution and motor imagery tasks. Activation of the second input variable (i.e., motor imagery) indicates that the task is motor imagery. (b) 9 models are tested in this study. The models differ in regions receiving task input, and connections modulated by motor imagery.
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f0015: The model structure for the DCM analysis of motor execution and imagery data. (a) The model comprises two regions including supplementary motor area (SMA) and primary motor cortex (M1), and two inputs according to the experimental paradigm. The first input variable (i.e., task) encodes the occurrence of motor execution and motor imagery tasks. Activation of the second input variable (i.e., motor imagery) indicates that the task is motor imagery. (b) 9 models are tested in this study. The models differ in regions receiving task input, and connections modulated by motor imagery.

Mentions: A previous study found consistent activation in the SMA and premotor cortex during motor execution and imagery, and reduced activation in M1 during motor imagery (Hanakawa et al., 2003). Moreover, a recent study has revealed, using DCM for fMRI (Kasess et al., 2008), that coupling between SMA and M1 may serve to attenuate the activation of M1 during motor imagery. In this paper, we test 9 models depicted in Fig. 3, in order to investigate (i) how the motor imagery condition affects the directed connections between SMA and M1, and (ii) how these interactions are associated with the regional activity in M1 and SMA during motor execution and imagery. All models comprise two regions including SMA and M1, and assume reciprocal connections between SMA and M1 for the A matrix; this connectivity is supported by anatomical studies in monkeys (Muakkassa and Strick, 1979; Luppino et al., 1993). The models then differ in regions receiving task input: M1, SMA, and both M1 and SMA. The models also differ in which connections are modulated by motor imagery: modulation of intrinsic (within-region) connectivity, modulation of extrinsic (between-region) connectivity, and modulation of both intrinsic and extrinsic connectivities. Additionally, for each of these 9 configurations, we fit two models; one with hemodynamic response modeled as point source and one with a spatially distributed source. Overall, these 18 models allow us to address 3 experimental questions (i) which regions receive task input?, (ii) which type of connections is modulated by imagery?, and (iii) are distributed sources better than point source models?


Dynamic causal modelling for functional near-infrared spectroscopy.

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

The model structure for the DCM analysis of motor execution and imagery data. (a) The model comprises two regions including supplementary motor area (SMA) and primary motor cortex (M1), and two inputs according to the experimental paradigm. The first input variable (i.e., task) encodes the occurrence of motor execution and motor imagery tasks. Activation of the second input variable (i.e., motor imagery) indicates that the task is motor imagery. (b) 9 models are tested in this study. The models differ in regions receiving task input, and connections modulated by motor imagery.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0015: The model structure for the DCM analysis of motor execution and imagery data. (a) The model comprises two regions including supplementary motor area (SMA) and primary motor cortex (M1), and two inputs according to the experimental paradigm. The first input variable (i.e., task) encodes the occurrence of motor execution and motor imagery tasks. Activation of the second input variable (i.e., motor imagery) indicates that the task is motor imagery. (b) 9 models are tested in this study. The models differ in regions receiving task input, and connections modulated by motor imagery.
Mentions: A previous study found consistent activation in the SMA and premotor cortex during motor execution and imagery, and reduced activation in M1 during motor imagery (Hanakawa et al., 2003). Moreover, a recent study has revealed, using DCM for fMRI (Kasess et al., 2008), that coupling between SMA and M1 may serve to attenuate the activation of M1 during motor imagery. In this paper, we test 9 models depicted in Fig. 3, in order to investigate (i) how the motor imagery condition affects the directed connections between SMA and M1, and (ii) how these interactions are associated with the regional activity in M1 and SMA during motor execution and imagery. All models comprise two regions including SMA and M1, and assume reciprocal connections between SMA and M1 for the A matrix; this connectivity is supported by anatomical studies in monkeys (Muakkassa and Strick, 1979; Luppino et al., 1993). The models then differ in regions receiving task input: M1, SMA, and both M1 and SMA. The models also differ in which connections are modulated by motor imagery: modulation of intrinsic (within-region) connectivity, modulation of extrinsic (between-region) connectivity, and modulation of both intrinsic and extrinsic connectivities. Additionally, for each of these 9 configurations, we fit two models; one with hemodynamic response modeled as point source and one with a spatially distributed source. Overall, these 18 models allow us to address 3 experimental questions (i) which regions receive task input?, (ii) which type of connections is modulated by imagery?, and (iii) are distributed sources better than point source models?

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.