  In this technical note, we address an unresolved challenge in neuroimaging
statistics: how to determine which of several datasets is the best for
inferring neuronal responses. Comparisons of this kind are important for
experimenters when choosing an imaging protocol - and for developers of new
acquisition methods. However, the hypothesis that one dataset is better than
another cannot be tested using conventional statistics (based on likelihood
ratios), as these require the data to be the same under each hypothesis. Here
we present Bayesian data comparison, a principled framework for evaluating the
quality of functional imaging data, in terms of the precision with which
neuronal connectivity parameters can be estimated and competing models can be
disambiguated. For each of several candidate datasets, neuronal responses are
inferred using Dynamic Casual Modelling (DCM) - a commonly used Bayesian
procedure for modelling neuroimaging data. Next, the parameters from
subject-specific models are summarised at the group level using a Bayesian
General Linear Model (GLM). A series of measures, which we introduce here, are
then used to evaluate each dataset in terms of the precision of (group-level)
parameter estimates and the ability of the data to distinguish similar models.
To exemplify the approach, we compared four datasets that were acquired in a
study evaluating multiband fMRI acquisition schemes. To enable people to
reproduce these analyses using their own data and experimental paradigms, we
provide general-purpose Matlab code via the SPM software.
