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On nodes and modes in resting state fMRI.

Friston KJ, Kahan J, Razi A, Stephan KE, Sporns O - Neuroimage (2014)

Bottom Line: We first demonstrate that the eigenmodes of functional connectivity--or covariance among regions or nodes--are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections.Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory.In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents--that can also be interpreted as locations in a multidimensional scaling space.

View Article: PubMed Central - PubMed

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

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This figure reports the results of Bayesian model comparison and inversion of the empirical data. The top row uses the same format as used in Fig. 7. Here, we can see that the optimal exponent for stable modes is around 0.8 Hz, while the number of unstable modes is three. The topography of the connectivity and associated time constants are shown in the lower panels using the format of Fig. 6. In this activation study, there seems to be one dominant (slow) mode with a time constant of about 3.5 seconds. The remaining two modes have a time constant of about 1 second.
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f0045: This figure reports the results of Bayesian model comparison and inversion of the empirical data. The top row uses the same format as used in Fig. 7. Here, we can see that the optimal exponent for stable modes is around 0.8 Hz, while the number of unstable modes is three. The topography of the connectivity and associated time constants are shown in the lower panels using the format of Fig. 6. In this activation study, there seems to be one dominant (slow) mode with a time constant of about 3.5 seconds. The remaining two modes have a time constant of about 1 second.

Mentions: The results of Bayesian model comparison and inversion are shown in Fig. 9. The top row uses the same format as used in Fig. 7. Here, we can see that the optimal exponent for stable modes is around 0.8 Hz, while the number of unstable modes is again three. The topography of the connectivity and associated time constants are shown in the lower panels using the format of Fig. 6. The topography is identical to that in the top row of Fig. 6 — because we based the simulations on the sample covariance of the empirical data. However, we can now ascribe anatomy to the functional topography — such that the cluster of proximate nodes can be seen as belonging to association cortex, namely, prefrontal cortex, frontal eye fields and posterior parietal cortex. The anti-correlated pair of regions comprises the primary visual cortex and superior temporal sulcus. Interestingly, the angular gyrus does not seem to participate in any of these modes and is largely unconnected from all other nodes.


On nodes and modes in resting state fMRI.

Friston KJ, Kahan J, Razi A, Stephan KE, Sporns O - Neuroimage (2014)

This figure reports the results of Bayesian model comparison and inversion of the empirical data. The top row uses the same format as used in Fig. 7. Here, we can see that the optimal exponent for stable modes is around 0.8 Hz, while the number of unstable modes is three. The topography of the connectivity and associated time constants are shown in the lower panels using the format of Fig. 6. In this activation study, there seems to be one dominant (slow) mode with a time constant of about 3.5 seconds. The remaining two modes have a time constant of about 1 second.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4121089&req=5

f0045: This figure reports the results of Bayesian model comparison and inversion of the empirical data. The top row uses the same format as used in Fig. 7. Here, we can see that the optimal exponent for stable modes is around 0.8 Hz, while the number of unstable modes is three. The topography of the connectivity and associated time constants are shown in the lower panels using the format of Fig. 6. In this activation study, there seems to be one dominant (slow) mode with a time constant of about 3.5 seconds. The remaining two modes have a time constant of about 1 second.
Mentions: The results of Bayesian model comparison and inversion are shown in Fig. 9. The top row uses the same format as used in Fig. 7. Here, we can see that the optimal exponent for stable modes is around 0.8 Hz, while the number of unstable modes is again three. The topography of the connectivity and associated time constants are shown in the lower panels using the format of Fig. 6. The topography is identical to that in the top row of Fig. 6 — because we based the simulations on the sample covariance of the empirical data. However, we can now ascribe anatomy to the functional topography — such that the cluster of proximate nodes can be seen as belonging to association cortex, namely, prefrontal cortex, frontal eye fields and posterior parietal cortex. The anti-correlated pair of regions comprises the primary visual cortex and superior temporal sulcus. Interestingly, the angular gyrus does not seem to participate in any of these modes and is largely unconnected from all other nodes.

Bottom Line: We first demonstrate that the eigenmodes of functional connectivity--or covariance among regions or nodes--are the same as the eigenmodes of the underlying effective connectivity, provided we limit ourselves to symmetrical connections.Crucially, the principal modes of functional connectivity correspond to the dynamically unstable modes of effective connectivity that decay slowly and show long term memory.In this model, effective connectivity is parameterised in terms of eigenmodes and their Lyapunov exponents--that can also be interpreted as locations in a multidimensional scaling space.

View Article: PubMed Central - PubMed

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

Show MeSH
Related in: MedlinePlus