Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
复制标题
神经影像学中脑部疾病的单受试者预测：前景和陷阱
DOI：
10.1016/j.neuroimage.2016.02.079
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发表时间：
2017-01-15
期刊：
影响因子：
5.7
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Arbabshirani MR;Plis S;Sui J;Calhoun VD
近年来，基于神经影像学的单一受试者脑疾病预测得到了越来越多的关注。使用各种神经成像模式，如结构，功能和扩散MRI，沿着机器学习技术，已经进行了数百项研究，以准确分类患有异质性精神和神经退行性疾病的患者，如精神分裂症和阿尔茨海默病。在过去的四分之一世纪中，已经发表了500多项关于单一受试者预测的研究，这些研究集中在多种大脑疾病上。在本研究的第一部分，我们提供了一个调查的200多个报告在这一领域的重点是精神分裂症，轻度认知障碍（MCI），阿尔茨海默氏病（AD），抑郁症，自闭症谱系疾病（ASD）和注意缺陷多动障碍（ADHD）。这些研究的详细信息，如样本量，类型和数量的提取功能和报告的准确性进行了总结和讨论。据我们所知，这是迄今为止最全面的审查基于神经成像的单一主题预测大脑疾病。在第二部分中，我们从机器学习的角度对这些研究的主要缺陷提出了我们的意见。常见的偏见进行了讨论，并提供建议。此外，还讨论了分布式数据共享、多模式脑成像、鉴别诊断、疾病亚型分类和深度学习等新兴趋势。基于这项调查，有大量的证据表明，神经影像学数据的巨大潜力，为单一的主题预测各种疾病。然而，这一令人兴奋的领域的主要瓶颈仍然是有限的样本量，这可能会通过本文讨论的现代数据共享模型来解决。新兴的大数据技术和先进的数据密集型机器学习方法（如深度学习）与在令人兴奋的时期对准确，稳健和可推广的单一受试者预测大脑疾病的需求日益增长相吻合。在本报告中，我们回顾过去，并就今后的道路提出一些意见。
Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there are extensive evidences showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.
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影响因子：
5.7
作者：
Bassett, Danielle S.;Nelson, Brent G.;Mueller, Bryon A.;Camchong, Jazmin;Lim, Kelvin O.
通讯作者：
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影响因子：
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DOI：
10.1097/00004583-200403000-00018
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影响因子：
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