摘要翻译：
功能磁共振成像（fMRI）提供了对人脑复杂功能的动态访问，详细描述了数百个连续时间点内数千个体素的血流动力学活动。一种阐明fMRI与认知功能之间联系的方法是通过解码；体素活动的时间序列如何结合以提供关于内部和外部经验的信息？在这里，我们寻求在解释的简单性和预测的有效性之间平衡的fMRI解码模型。我们使用来自一个沉浸在虚拟现实中的主题的信号来比较应用线性和非线性降维技术的全局和局部预测方法。我们发现对复杂刺激的预测是非常低维的，只有不到100个特征饱和。特别是，我们在经典定义的Brodmann区域建立了基于认知活动的非相关成分的有效模型。对于一些刺激，最大的预测区域是令人惊讶的透明，包括用于语言指令的韦尼克区，用于面部和身体特征的视觉皮层，以及用于速度的视觉-时间区域。直接的感觉经验导致最稳健的预测，与最高的相关性($C\sim 0.8$)之间的预测和经验的时间序列之间的口头指令。基于非线性降维（拉普拉斯本征图）的技术执行类似。我们的方法的可解释性和相对简单性提供了一个概念基础，在此基础上建立了更复杂的fMRI解码技术，并提供了一个窗口，以了解动态的自然经验中的认知功能。
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英文标题：
《Locality and low-dimensions in the prediction of natural experience from
  fMRI》
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作者：
Francois G. Meyer and Greg J. Stephens
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Neurons and Cognition        神经元与认知
分类描述：Synapse, cortex, neuronal dynamics, neural network, sensorimotor control, behavior, attention
突触，皮层，神经元动力学，神经网络，感觉运动控制，行为，注意
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels during hundreds of sequential time points. One approach towards illuminating the connection between fMRI and cognitive function is through decoding; how do the time series of voxel activities combine to provide information about internal and external experience? Here we seek models of fMRI decoding which are balanced between the simplicity of their interpretation and the effectiveness of their prediction. We use signals from a subject immersed in virtual reality to compare global and local methods of prediction applying both linear and nonlinear techniques of dimensionality reduction. We find that the prediction of complex stimuli is remarkably low-dimensional, saturating with less than 100 features. In particular, we build effective models based on the decorrelated components of cognitive activity in the classically-defined Brodmann areas. For some of the stimuli, the top predictive areas were surprisingly transparent, including Wernicke's area for verbal instructions, visual cortex for facial and body features, and visual-temporal regions for velocity. Direct sensory experience resulted in the most robust predictions, with the highest correlation ($c \sim 0.8$) between the predicted and experienced time series of verbal instructions. Techniques based on non-linear dimensionality reduction (Laplacian eigenmaps) performed similarly. The interpretability and relative simplicity of our approach provides a conceptual basis upon which to build more sophisticated techniques for fMRI decoding and offers a window into cognitive function during dynamic, natural experience.
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PDF链接：
https://arxiv.org/pdf/712.1219