  The study of neurocognitive tasks requiring accurate localisation of activity
often rely on functional Magnetic Resonance Imaging, a widely adopted technique
that makes use of a pipeline of data processing modules, each involving a
variety of parameters. These parameters are frequently set according to the
local goal of each specific module, not accounting for the rest of the
pipeline. Given recent success of neural network research in many different
domains, we propose to convert the whole data pipeline into a deep neural
network, where the parameters involved are jointly optimised by the network to
best serve a common global goal. As a proof of concept, we develop a module
able to adaptively apply the most suitable spatial smoothing to every brain
volume for each specific neuroimaging task, and we validate its results in a
standard brain decoding experiment.
