Cohen Jessica R, Asarnow Robert F, Sabb Fred W, Bilder Robert M, Bookheimer Susan Y, Knowlton Barbara J, Poldrack Russell A
Helen Wills Neuroscience Institute, University of California Berkeley Berkeley, CA, USA.
Front Neurosci. 2011 Jun 15;5:75. doi: 10.3389/fnins.2011.00075. eCollection 2011.
The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.
将统计机器学习技术应用于神经影像数据，使研究人员能够解码参与者的认知和疾病状态。使用这些技术的大多数研究都集中在模式分类上，以解码参与者正在观看的物体类型、正在完成的认知任务类型或参与者大脑的疾病状态。然而，越来越多的文献正在将这些分类研究扩展到使用高维回归方法解码连续变量的值（如年龄、认知特征或神经心理状态）。这篇综述详细介绍了此类分析中使用的方法，并描述了近期的研究结果。我们提供了一些研究的具体例子，这些研究使用这种方法来回答有关年龄以及认知和疾病状态的新问题。我们得出结论，虽然关于这些方法仍有很多需要学习的地方，但它们提供了有关神经活动与年龄、认知状态和疾病状态之间关系的有用信息，而这些信息是使用传统单变量分析方法无法获得的。
