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 (BDC), 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 modeled using Bayesian
(probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual
Models (DCMs). Next, the parameters from subject-specific models are summarized at
the group level using a Bayesian 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, and we used simulations to establish the face validity of the
comparison measures. To enable people to reproduce these analyses using their own
data and experimental paradigms, we provide general-purpose Matlab code via the SPM
software