On nodes and modes in resting state fMRI.
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.
Affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: email@example.com.Show MeSH
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Mentions: Fig. 2 shows the form of the generative model in terms of a Bayesian graph. A generative model is simply a model of how data are generated. In this case the data are complex cross spectra of sampled timeseries. The model starts with the spatial eigenmodes μ = eig(Σy) of the sample covariance matrix. Although the number of hidden states exceeds the number of regional timeseries, we can still use the eigenmodes of the sample covariance of regional responses as proxies for the eigenmodes of hidden (neuronal) states — because there is only one neuronal state per region. The remaining hidden states model local haemodynamics, which effectively smooth or convolve the neural activity to produce a BOLD response.
Affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. Electronic address: firstname.lastname@example.org.