摘要翻译:
提出了一种在低维空间中嵌入功能磁共振成像数据集的新方法。该嵌入最优地保持了fMRI时间序列之间的局部函数耦合,并为检测激活体素提供了一个低维坐标系。为了计算嵌入,我们建立了一个功能连接体素的图。我们使用通勤时间,而不是测地距离,来测量图上的功能距离。由于通勤时间可以直接从图概率转移矩阵的特征向量(对称形式)中计算出来,因此我们使用这些特征向量来嵌入低维数据集。在对数据集进行低维聚类后,会出现易于解释的相干结构。我们对我们的方法进行了广泛的评估,使用合成数据集和体内数据集将其与线性和非线性技术进行了比较。我们分析了在城市虚拟现实环境中与主体交互的EBC竞赛的数据集。我们的探索性方法能够独立地检测视觉区(V1/V2,V5/MT)、听觉区和语言区。我们的方法可用于分析“自然刺激”期间采集的fMRI。
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英文标题:
《Low Dimensional Embedding of fMRI datasets》
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作者:
Xilin Shen and Fran\c{c}ois G. Meyer
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最新提交年份:
2008
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分类信息:
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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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:Applications 应用程序
分类描述:Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学,教育学,流行病学,工程学,环境科学,医学,物理科学,质量控制,社会科学
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英文摘要:
We propose a novel method to embed a functional magnetic resonance imaging (fMRI) dataset in a low-dimensional space. The embedding optimally preserves the local functional coupling between fMRI time series and provides a low-dimensional coordinate system for detecting activated voxels. To compute the embedding, we build a graph of functionally connected voxels. We use the commute time, instead of the geodesic distance, to measure functional distances on the graph. Because the commute time can be computed directly from the eigenvectors of (a symmetric version) the graph probability transition matrix, we use these eigenvectors to embed the dataset in low dimensions. After clustering the datasets in low dimensions, coherent structures emerge that can be easily interpreted. We performed an extensive evaluation of our method comparing it to linear and nonlinear techniques using synthetic datasets and in vivo datasets. We analyzed datasets from the EBC competition obtained with subjects interacting in an urban virtual reality environment. Our exploratory approach is able to detect independently visual areas (V1/V2, V5/MT), auditory areas, and language areas. Our method can be used to analyze fMRI collected during ``natural stimuli''.
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PDF链接:
https://arxiv.org/pdf/709.3121