Content deleted Content added
m Open access bot: hdl updated in citation with #oabot. |
Polyamorph (talk | contribs) m →Limitations / drawbacks: clean up, typo(s) fixed: time consuming → time-consuming |
||
Line 84:
DCM is a hypothesis-driven approach for investigating the interactions among pre-defined regions of interest. It is not ideally suited for exploratory analyses.<ref name="Stephan 2010" /> Although methods have been implemented for automatically searching over reduced models ([[Bayesian model reduction|Bayesian Model Reduction]]) and for modelling large-scale brain networks,<ref name="Razi 2017" /> these methods require an explicit specification of model space. In neuroimaging, approaches such as [[Psychophysiological Interaction|psychophysiological interaction (PPI)]] analysis may be more appropriate for exploratory use; especially for discovering key nodes for subsequent DCM analysis.
The variational Bayesian methods used for model estimation in DCM are based on the Laplace assumption, which treats the posterior over parameters as Gaussian. This approximation can fail in the context of highly non-linear models, where local minima may preclude the free energy from serving as a tight bound on log model evidence. Sampling approaches provide the gold standard; however, they are time
== Software implementations ==
| |||