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

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

doi:10.3389/fnins.2018.00957

Ali Yousefi, Darin D Dougherty + Show 6 more

Open Access

https://doi.org/10.3389/fnins.2018.00957

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Abstract

Mathematical modeling of behavior during a psychophysical task, referred to as “computational psychiatry,” could greatly improve our understanding of mental disorders. One barrier to the 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 very few toolboxes and analytical platforms are capable of processing such signal categories. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. This toolbox is easy to use and capable of integrating signals with a variety of distributions. COMPASS has the 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 those signals captured during a psychophysical task. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows users to efficiently apply 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 also be tested using the goodness-of-fit measurement. Here, we demonstrate that COMPASS can replicate two computational behavioral analyses from different groups. COMPASS replicates and can slightly improve on the original modeling results. We also demonstrate the use of 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.

Highlights

There is a growing need for advanced computational methods within psychiatric neuroscience (Wang and Krystal, 2014; Paulus et al, 2016; Redish and Gordon, 2016)
We fit subject-level models to that behavior and plotted the individual subjects’ Gain vs. Loss Avoidance coefficients for accuracy prediction. This replicated the pattern of healthy controls (HC)/LNS showing gain sensitivity and HNS showing primarily loss sensitivity (Figure 3D; Appendix C in the Supplementary Material provides a detailed explanation of the Gold et al computational model using Computational Psychiatry Adaptive State-Space (COMPASS))
We developed an open-source toolbox for state-space modeling and demonstrated its utility in analyzing dynamical behavioral signals relevant to computational psychiatry

Summary

INTRODUCTION

There is a growing need for advanced computational methods within psychiatric neuroscience (Wang and Krystal, 2014; Paulus et al, 2016; Redish and Gordon, 2016). When Gold et al simulated data based on the model parameters’ fit to each individual subject’s behavior, HC and LNS subjects were learned more by obtaining gains rather than by avoiding losses (Figure 3B) We fit subject-level models to that behavior and plotted the individual subjects’ Gain vs Loss Avoidance coefficients for accuracy prediction This replicated the pattern of HC/LNS showing gain sensitivity and HNS showing primarily loss sensitivity (Figure 3D; Appendix C in the Supplementary Material provides a detailed explanation of the Gold et al computational model using COMPASS). The objective is to estimate in-attention state and model parameters for different threshold levels For this example, we simulated the data to generate inattention state and reaction time for 200 trials. We assume variance of the state and observation processes noise are known

DISCUSSION

Findings

DISCLOSURE