  The characterisation of biomarkers and endophenotypic measures has been a
central goal of research in psychiatry over the last years. While most of this
research has focused on the identification of biomarkers and endophenotypes,
using various experimental approaches, it has been recognised that their
instantiations, through computational models, have a great potential to help us
understand and interpret these experimental results. However, the enormous
increase in available neurophysiological and neurocognitive as well as
computational data also poses new challenges. How can a researcher stay on top
of the experimental literature? How can computational modelling data be
efficiently compared to experimental data? How can computational modelling most
effectively inform experimentalists? Recently, a general scientific framework
for the generation of executable tests that automatically compare model results
to experimental observations, SciUnit, has been proposed. Here we exploit this
framework for research in psychiatry to address the challenges mentioned above.
We extend the SciUnit framework by adding an experimental database, which
contains a comprehensive collection of relevant experimental observations, and
a prediction database, which contains a collection of predictions generated by
computational models. Together with appropriately designed SciUnit tests and
methods to mine and visualise the databases, model data and test results, this
extended framework has the potential to greatly facilitate the use of
computational models in psychiatry. As an initial example we present ASSRUnit,
a module for auditory steady-state response deficits in psychiatric disorders.
