Krystal John H, Murray John D, Chekroud Adam M, Corlett Philip R, Yang Genevieve, Wang Xiao-Jing, Anticevic Alan
Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA.
Schizophr Bull. 2017 May 1;43(3):473-475. doi: 10.1093/schbul/sbx025.
Schizophrenia research is plagued by enormous challenges in integrating and analyzing large datasets and difficulties developing formal theories related to the etiology, pathophysiology, and treatment of this disorder. Computational psychiatry provides a path to enhance analyses of these large and complex datasets and to promote the development and refinement of formal models for features of this disorder. This presentation introduces the reader to the notion of computational psychiatry and describes discovery-oriented and theory-driven applications to schizophrenia involving machine learning, reinforcement learning theory, and biophysically-informed neural circuit models.
精神分裂症研究在整合和分析大型数据集方面面临巨大挑战，且在发展与该疾病的病因、病理生理学及治疗相关的形式理论方面存在困难。计算精神病学为加强对这些大型复杂数据集的分析以及促进针对该疾病特征的形式模型的开发与完善提供了一条途径。本报告向读者介绍计算精神病学的概念，并描述面向发现和理论驱动的精神分裂症应用，涉及机器学习、强化学习理论以及具有生物物理信息的神经回路模型。
