Huys Quentin J M, Maia Tiago V, Frank Michael J
Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.
Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich, Zürich, Switzerland.
Nat Neurosci. 2016 Mar;19(3):404-13. doi: 10.1038/nn.4238.
Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.
将神经科学的进展转化为对精神疾病患者的益处面临着巨大挑战，因为这涉及到最复杂的器官——大脑，以及它与同样复杂的环境之间的相互作用。应对如此复杂的情况需要强大的技术。计算精神病学将多种层次和类型的计算与多种类型的数据相结合，以努力增进对精神疾病的理解、预测和治疗。广义而言，计算精神病学包含两种互补的方法：数据驱动和理论驱动。数据驱动的方法将机器学习方法应用于高维数据，以改善疾病分类、预测治疗结果或改进治疗选择。这些方法通常对潜在机制不做假设。相比之下，理论驱动的方法使用模型来实例化关于此类机制的先验知识或明确假设，可能在多个分析和抽象层次上。我们回顾了这两种方法的最新进展，重点是临床应用，并强调了将它们结合起来的实用性。
