AUTHOR=Yousefi Ali , Paulk Angelique C. , Basu Ishita , Mirsky Jonathan L. , Dougherty Darin D. , Eskandar Emad N. , Eden Uri T. , Widge Alik S. 

TITLE=COMPASS: An Open-Source, General-Purpose Software Toolkit for Computational Psychiatry

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 12 - 2018

YEAR=2019

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00957

DOI=10.3389/fnins.2018.00957

ISSN=1662-453X

ABSTRACT=Mathematical modeling of behavior during psychophysical tasks, referred to as "computational psychiatry", could greatly improve our understanding of mental disorders. One barrier to broader adoption of computational methods is that they often require advanced statistical modeling and mathematical skills. Biological and behavioral signals often show skewed or non-Gaussian distributions, and few of toolboxes and analytical platforms are capable of processing these categories of signals. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package, to be easy to use and capable of integrating signals with a variety of distributions. COMPASS has tools to process signals with continuous-valued and binary measurements or signals with incomplete - missing or censored - measurements, which makes it well suited for processing signals captured during psychophysical tasks After specifying a few parameters in a small set of user-friendly functions, COMPASS allows the user to efficiently fit a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data and can be tested using goodness-of-fit methods. Here, we demonstrate that COMPASS can replicate two computational behavior analyses from different groups. COMPASS replicates and, in one case, slightly improves on the original modeling results. We also demonstrate COMPASS application in a censored-data problem and compare its performance result with naïve estimation methods. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.