  With the wide adoption of functional magnetic resonance imaging (fMRI) by
cognitive neuroscience researchers, large volumes of brain imaging data have
been accumulated in recent years. Aggregating these data to derive scientific
insights often faces the challenge that fMRI data are high-dimensional,
heterogeneous across people, and noisy. These challenges demand the development
of computational tools that are tailored both for the neuroscience questions
and for the properties of the data. We review a few recently developed
algorithms in various domains of fMRI research: fMRI in naturalistic tasks,
analyzing full-brain functional connectivity, pattern classification, inferring
representational similarity and modeling structured residuals. These algorithms
all tackle the challenges in fMRI similarly: they start by making clear
statements of assumptions about neural data and existing domain knowledge,
incorporating those assumptions and domain knowledge into probabilistic
graphical models, and using those models to estimate properties of interest or
latent structures in the data. Such approaches can avoid erroneous findings,
reduce the impact of noise, better utilize known properties of the data, and
better aggregate data across groups of subjects. With these successful cases,
we advocate wider adoption of explicit model construction in cognitive
neuroscience. Although we focus on fMRI, the principle illustrated here is
generally applicable to brain data of other modalities.
