Network discovery with DCM.
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.
Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. firstname.lastname@example.orgShow MeSH
Related in: MedlinePlus
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.
Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK. email@example.com