Institute for Innovations in Developmental Sciences, Northwestern University, Chicago, Illinois; Department of Medical Social Sciences, Northwestern University, Chicago, Illinois.
Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2020 Aug;5(8):791-798. doi: 10.1016/j.bpsc.2019.11.007. Epub 2019 Nov 27.
Psychiatric disorders are complex, involving heterogeneous symptomatology and neurobiology that rarely involves the disruption of single, isolated brain structures. In an attempt to better describe and understand the complexities of psychiatric disorders, investigators have increasingly applied multivariate pattern classification approaches to neuroimaging data and in particular supervised machine learning methods. However, supervised machine learning approaches also come with unique challenges and trade-offs, requiring additional study design and interpretation considerations. The goal of this review is to provide a set of best practices for evaluating machine learning applications to psychiatric disorders. We discuss how to evaluate two common efforts: 1) making predictions that have the potential to aid in diagnosis, prognosis, and treatment and 2) interrogating the complex neurophysiological mechanisms underlying psychopathology. We focus here on machine learning as applied to functional connectivity with magnetic resonance imaging, as an example to ground discussion. We argue that for machine learning classification to have translational utility for individual-level predictions, investigators must ensure that the classification is clinically informative, independent of confounding variables, and appropriately assessed for both performance and generalizability. We contend that shedding light on the complex mechanisms underlying psychiatric disorders will require consideration of the unique utility, interpretability, and reliability of the neuroimaging features (e.g., regions, networks, connections) identified from machine learning approaches. Finally, we discuss how the rise of large, multisite, publicly available datasets may contribute to the utility of machine learning approaches in psychiatry.
精神障碍是复杂的，涉及异质的症状和神经生物学，很少涉及单个孤立的脑结构的破坏。为了更好地描述和理解精神障碍的复杂性，研究人员越来越多地将多元模式分类方法应用于神经影像学数据，特别是监督机器学习方法。然而，监督机器学习方法也带来了独特的挑战和权衡，需要额外的研究设计和解释考虑。本综述的目的是为评估机器学习在精神障碍中的应用提供一套最佳实践。我们讨论了如何评估两种常见的努力：1）进行有潜力辅助诊断、预后和治疗的预测，2）探究精神病理学背后的复杂神经生理机制。我们在这里重点讨论机器学习在磁共振成像功能连接中的应用，作为讨论的基础。我们认为，为了使机器学习分类在个体水平预测方面具有转化实用性，研究人员必须确保分类具有临床意义，不受混杂变量的影响，并对性能和可泛化性进行适当评估。我们认为，要揭示精神障碍背后的复杂机制，就需要考虑从机器学习方法中识别出的神经影像学特征（例如，区域、网络、连接）的独特实用性、可解释性和可靠性。最后，我们讨论了大型、多站点、公开可用数据集的兴起如何有助于机器学习方法在精神病学中的应用。
