Yousefi Ali, Paulk Angelique C, Basu Ishita, Mirsky Jonathan L, Dougherty Darin D, Eskandar Emad N, Eden Uri T, Widge Alik S
Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Department of Mathematics and Statistics, Boston University, Boston, MA, United States.
Front Neurosci. 2019 Jan 11;12:957. doi: 10.3389/fnins.2018.00957. eCollection 2018.
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.
在心理物理学任务中对行为进行数学建模，即所谓的“计算精神病学”，可以极大地增进我们对精神障碍的理解。计算方法更广泛应用的一个障碍是，它们通常需要先进的统计建模和数学技能。生物和行为信号往往呈现出偏态或非高斯分布，而且能够处理此类信号类别的工具箱和分析平台非常少。我们开发了计算精神病学自适应状态空间（COMPASS）工具箱，这是一个基于MATLAB的开源软件包。这个工具箱易于使用，能够整合具有各种分布的信号。COMPASS拥有处理连续值和二元测量信号，或具有不完全缺失或删失测量信号的工具，这使得它非常适合处理在心理物理学任务中捕获的那些信号。在一小组用户友好的函数中指定几个参数后，COMPASS允许用户有效地应用广泛的计算行为模型。模型输出可以作为实验结果进行分析，或用作神经数据的回归变量，并且还可以使用拟合优度测量进行测试。在这里，我们证明COMPASS可以复制来自不同组的两种计算行为分析。COMPASS复制并能在一定程度上改进原始建模结果。我们还展示了COMPASS在删失数据问题中的应用，并将其性能结果与简单估计方法进行比较。这个灵活的通用工具包应该会加速计算建模在精神神经科学中的应用。
