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Network discovery with DCM.

Friston KJ, Li B, Daunizeau J, Stephan KE - Neuroimage (2010)

Bottom Line: The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks.The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions.We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. k.firston@fil.ion.ucl.ac.uk

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Related in: MedlinePlus

Model spaces and adjacency matrices: This figure illustrates the model spaces induced by considering different adjacency matrices or combinations of edges among the nodes of a graph. The upper panel shows the number of different models that one can entertain as a function of the number of nodes. Here, we placed the additional constraint on the models that each connection has to be bidirectional. The lower panel shows all the alternative models that could be considered, given four nodes. One example is highlighted in the insert, where the solid bidirectional arrows denote edges and the grey arrows denote anti-edges. This particular example was used to generate simulated data for the results described in the next figure.
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f0025: Model spaces and adjacency matrices: This figure illustrates the model spaces induced by considering different adjacency matrices or combinations of edges among the nodes of a graph. The upper panel shows the number of different models that one can entertain as a function of the number of nodes. Here, we placed the additional constraint on the models that each connection has to be bidirectional. The lower panel shows all the alternative models that could be considered, given four nodes. One example is highlighted in the insert, where the solid bidirectional arrows denote edges and the grey arrows denote anti-edges. This particular example was used to generate simulated data for the results described in the next figure.

Mentions: Even with this constraint, the number of models can still be too great to explore exhaustively (see Fig. 5). For example, with three regions there are 8 models, for four regions there are 64, for eight regions there are 268,435,456; and so on. This means that there is a combinatoric explosion as one increases the number of nodes in the network. In what follows, we describe a procedure that deals with this problem by scoring models based on the inversion of just one (full) model.


Network discovery with DCM.

Friston KJ, Li B, Daunizeau J, Stephan KE - Neuroimage (2010)

Model spaces and adjacency matrices: This figure illustrates the model spaces induced by considering different adjacency matrices or combinations of edges among the nodes of a graph. The upper panel shows the number of different models that one can entertain as a function of the number of nodes. Here, we placed the additional constraint on the models that each connection has to be bidirectional. The lower panel shows all the alternative models that could be considered, given four nodes. One example is highlighted in the insert, where the solid bidirectional arrows denote edges and the grey arrows denote anti-edges. This particular example was used to generate simulated data for the results described in the next figure.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3094760&req=5

f0025: Model spaces and adjacency matrices: This figure illustrates the model spaces induced by considering different adjacency matrices or combinations of edges among the nodes of a graph. The upper panel shows the number of different models that one can entertain as a function of the number of nodes. Here, we placed the additional constraint on the models that each connection has to be bidirectional. The lower panel shows all the alternative models that could be considered, given four nodes. One example is highlighted in the insert, where the solid bidirectional arrows denote edges and the grey arrows denote anti-edges. This particular example was used to generate simulated data for the results described in the next figure.
Mentions: Even with this constraint, the number of models can still be too great to explore exhaustively (see Fig. 5). For example, with three regions there are 8 models, for four regions there are 64, for eight regions there are 268,435,456; and so on. This means that there is a combinatoric explosion as one increases the number of nodes in the network. In what follows, we describe a procedure that deals with this problem by scoring models based on the inversion of just one (full) model.

Bottom Line: The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks.The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions.We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies.

View Article: PubMed Central - PubMed

Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. k.firston@fil.ion.ucl.ac.uk

Show MeSH
Related in: MedlinePlus