Modules for Automated Validation and Comparison of Models of Neurophysiological and Neurocognitive Biomarkers of Psychiatric Disorders: ASSRUnit - A Case Study

TL;DR

This paper introduces ASSRUnit, an extension of the SciUnit framework, to facilitate validation and comparison of computational models of neurophysiological biomarkers in psychiatry, exemplified by auditory steady-state response deficits.

Contribution

It develops an extended SciUnit framework with databases and tools for model validation, specifically applied to neurophysiological biomarkers in psychiatric disorders.

Findings

Extended SciUnit framework with experimental and prediction databases

Implementation of ASSRUnit module for auditory steady-state response deficits

Enhanced tools for model validation and comparison in psychiatric research

Abstract

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.