Computational psychiatry as a bridge from neuroscience to clinical applications.
Computational psychiatry as a bridge from neuroscience to clinical applications.
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DOI：
10.1038/nn.4238
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发表时间：
2016-03
影响因子：
25
通讯作者：
Frank MJ
中科院分区：
文献类型：
--
作者：
Huys QJ;Maia TV;Frank MJ
将神经科学的进步转化为精神疾病患者的益处是一个巨大的挑战，因为它涉及到最复杂的器官，大脑及其与类似复杂环境的相互作用。处理这种复杂性需要强大的技术。计算精神病学将多层次和多类型的计算与多种类型的数据相结合，以提高对精神疾病的理解，预测和治疗。广义的计算精神病学包括两种互补的方法：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，预测治疗结果或改善治疗选择。这些方法通常不知道其基本机制。理论驱动的方法，相反，使用的模型，实例化的先验知识，或明确的假设，这种机制，可能在多个层次的分析和抽象。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。
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.