Increasing the number of connections does not require more subjects, but there are two other considerations to bear in mind.
More complex models face a higher penalty in the Free energy estimate of log-model-evidence (F), as this depends on the difference between model accuracy and model complexity. Unlike AIC and BIC (SPM2.SPM5), the F penalty for complexity (SPM8, DCM10) is not fixed by the number of parameters/observations. Rather, it also accomodates the dependencies among parameters (via the KL divergence). Bayesian model selection will then be able help you find the right balance of model complexity and accuracy, for a given data set, for one or more subjects.
The dependencies among parameters need careful consideration, especially if you wish to go on to perform classical statistics on the connectivity parameters, as Marta suggests (and as have been successfully undertaken in many published DCM papers). Relevant parameter dependecies are more likely for complex models. If the parameters do covary, then the estimates of indidual parameters become unreliable, increasing type II error in the classical stats. Note that this does not affect F-based model comparisons (e.g. RFX & FFX Bayesian model selection). Klaas offered a clear account of these issues with useful refs in his recent mail #046065 "DCM-conditional dependencies". If you frame your hypothesis in terms of model selection (with one or many subjects) then this problem does not arise.
Maria thought you might be able to help me with a DCM question. I was wondering if increasing the number of connections in a model requires increasing the number of subjects - a bit like increasing the number of cells in a factorial design?