基于模式的 fMRI 方法概论：原理、陷阱与展望。
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives.
机构信息
Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin, 10117 Berlin, Germany; Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin, 10117 Berlin, Germany; Berlin School of Mind and Brain, Humboldt Universität zu Berlin, 10117 Berlin, Germany; Department of Neurology, Charité - Universitätsmedizin, 10117 Berlin, Germany; Department of Psychology, Humboldt Universität zu Berlin, 10117 Berlin, Germany; Cluster of Excellence NeuroCure, Charité - Universitätsmedizin, 10117 Berlin, Germany; SFB 940, Volition and Cognitive Control, Technische Universität Dresden, 01069 Dresden, Germany.
出版信息
Neuron. 2015 Jul 15;87(2):257-70. doi: 10.1016/j.neuron.2015.05.025.
Human fMRI signals exhibit a spatial patterning that contains detailed information about a person's mental states. Using classifiers it is possible to access this information and study brain processes at the level of individual mental representations. The precise link between fMRI signals and neural population signals still needs to be unraveled. Also, the interpretation of classification studies needs to be handled with care. Nonetheless, pattern-based analyses make it possible to investigate human representational spaces in unprecedented ways, especially when combined with computational modeling.
人类 fMRI 信号呈现出一种空间模式，其中包含有关个体心理状态的详细信息。使用分类器可以访问此信息，并在个体心理表征水平上研究大脑过程。fMRI 信号与神经群体信号之间的精确联系仍有待阐明。此外，分类研究的解释需要谨慎处理。尽管如此，基于模式的分析使得以前所未有的方式研究人类的代表性空间成为可能，尤其是与计算模型相结合时。
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