Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.
Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.
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DOI：
10.1109/tbme.2013.2258344
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发表时间：
2013-09
期刊：
影响因子：
--
通讯作者：
Rogers BP
中科院分区：
文献类型：
--
作者：
Katwal SB;Gore JC;Marois R;Rogers BP
我们提出了一种新的基于图形的自组织映射图可视化方法，用于无监督功能磁共振成像(FMRI)分析。自组织映射是一种人工神经网络模型，它使用无监督学习将高维数据转换为低维(通常是2-D)映射。然而，为了正确解释输出地图上相邻节点原型(特征向量)之间的相似性并在数据中描绘感兴趣的簇和特征，需要一个后处理方案。在本文中，我们使用基于图形的可视化来捕捉fMRI数据特征，其基础是：1)数据在原型的接受野上的分布(基于密度的连通性)；以及2)原型之间的时间相似性(相关性)(基于相关性的连通性)。我们应用这种方法在涉及视觉-手动反应任务的fMRI反应时间实验中识别与任务相关的大脑区域，并将这些区域的fMRI反应的峰值时间与反应时间关联起来。自组织映射图的可视化在识别和分类相关脑区方面优于独立分量分析和体素单变量线性回归分析。我们的结论是，基于图形的自组织映射图可视化有助于高级可视化fMRI数据中的簇边界，从而能够分离大脑响应时间略有差异的区域。
We present novel graph-based visualizations of self-organizing maps for unsupervised functional magnetic resonance imaging (fMRI) analysis. A self-organizing map is an artificial neural network model that transforms high-dimensional data into a low-dimensional (often a 2-D) map using unsupervised learning. However, a postprocessing scheme is necessary to correctly interpret similarity between neighboring node prototypes (feature vectors) on the output map and delineate clusters and features of interest in the data. In this paper, we used graph-based visualizations to capture fMRI data features based upon 1) the distribution of data across the receptive fields of the prototypes (density-based connectivity); and 2) temporal similarities (correlations) between the prototypes (correlation-based connectivity). We applied this approach to identify task-related brain areas in an fMRI reaction time experiment involving a visuo-manual response task, and we correlated the time-to-peak of the fMRI responses in these areas with reaction time. Visualization of self-organizing maps outperformed independent component analysis and voxelwise univariate linear regression analysis in identifying and classifying relevant brain regions. We conclude that the graph-based visualizations of self-organizing maps help in advanced visualization of cluster boundaries in fMRI data enabling the separation of regions with small differences in the timings of their brain responses.