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Abstract：
Most psychiatric disorders are associated with subtle alterations in brain function and are subject to large interindividual differences. Typically, the diagnosis of these disorders requires time-consuming behavioral assessments administered by a multidisciplinary team with extensive experience. While the application of Machine Learning classification methods (ML classifiers) to neuroimaging data has the potential to speed and simplify diagnosis of psychiatric disorders, the methods, assumptions, and analytical steps are currently opaque and not accessible to researchers and clinicians outside the field. In this paper, we describe potential classification pipelines for autism spectrum disorder, as an example of a psychiatric disorder. The analyses are based on resting-state fMRI data derived from a multisite data repository (ABIDE). We compare several popular ML classifiers such as support vector machines, neural networks, and regression approaches, among others. In a tutorial style, written to be equally accessible for researchers and clinicians, we explain the rationale of each classification approach, clarify the underlying assumptions, and discuss possible pitfalls and challenges. We also provide the data as well as the MATLAB code we used to achieve our results. We show that out-of-the-box ML classifiers can yield classification accuracies of about 60-70%. Finally, we discuss how classification accuracy can be further improved, and we mention methodological developments that are needed to pave the way for the use of ML classifiers in clinical practice.
摘要：
大多数精神疾病与大脑功能的细微改变有关，并且存在很大的个体差异。通常,这些疾病的诊断需要由具有丰富经验的多学科团队进行耗时的行为评估.虽然将机器学习分类方法（ML分类器）应用于神经影像数据有可能加快和简化精神疾病的诊断，方法,假设,和分析步骤目前是不透明的，领域外的研究人员和临床医生无法使用。在本文中,我们描述了自闭症谱系障碍的潜在分类管道，作为精神疾病的一个例子。分析基于从多站点数据存储库（ABIDE）得出的静息状态fMRI数据。我们比较了几种流行的ML分类器，如支持向量机，神经网络，和回归方法，在其他人中。在教程样式中，被写为研究人员和临床医生同样容易获得，我们解释了每种分类方法的基本原理，澄清基本假设，讨论可能的陷阱和挑战。我们还提供数据以及用于实现结果的MATLAB代码。我们证明开箱即用的ML分类器可以产生约60-70%的分类准确率。最后,我们讨论了如何进一步提高分类精度，我们提到了方法学上的发展，为在临床实践中使用ML分类器铺平道路。
