Over the last years, the traditional diagnostic classifications used in psychiatry have been questioned and a breakdown into simpler categories like endophenotypes or cognitive domains has been proposed. This is mainly due to the fact that the gap between symptom-based classifications on the one hand and genes and molecules on the other hand is huge and a clear mapping inbetween not in sight. Underlying these new proposals is the hope that the simpler categories will map nicely to alterations at the genetic/molecular level. However, this hope might be overly optimistic. Not only are disorders such as schizophrenia highly polygenic (more than 100 risk genes have been identified), the proposed network-level endophenotypes can potentially be produced by a myriad of different configurations on the cellular level (multifactoriality).
In order to overcome these limitations, the use of biophysically detailed computational models in psychiatry has been proposed, which enable the implementation of genetic alterations and the exploration of their multifactorial interplay.
In this talk I present some of my efforts to contribute to this new computational psychiatry effort. I will describe a model of auditory click entrainment deficits in schizophrenic patients which incorporates experimentally identified cellular and circuit abnormalities in patients and explores how their interaction might give rise to experimentally observed deficits. Furthermore, I will point out the limitations of the presented approach, especially the crucial influence of the 'illness metric' (i.e. which deficits are incorporated in the analysis and which are not) on the results. I will then generally discuss how to overcome these limitations and briefly present the next steps in the above project (focusing on a new collaboration with the labs of Gaute Einevoll and Ole Andreassen from Oslo and Os, respectively).