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首页> 外文会议>Conference on physiology and function from multidimensional images >Unsupervised time course analysis of functional magnetic resonance imaging (fMRI) using self-organizing maps (SOMs)
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Unsupervised time course analysis of functional magnetic resonance imaging (fMRI) using self-organizing maps (SOMs)

机译：使用自组织地图功能磁共振成像（FMRI）的无监督时间课程分析（SOMS）

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Functional Magnetic Resonance Imaging (fMRI) data of the brain includes activated parenchymal voxels, corresponding to the paradigm performed, non-activated parenchymal voxels and background voxels. Statistical tests, e.g. using the general linear model approach of SPM or the Kolmogorov-Smirnov (KS) non-parametric statistic, are common 'supervised' techniques to look for activation in functional brain MRI. Selection of voxel type by comparing the voxel time course with a model of the expected hemodynamic response function (HRF) from the task paradigm has proven to be difficult due to individual and spatial variance of the measured HRF. For the functional differentiation of brain voxels we introduce a method separating brain voxels based on their features in the time domain using a self-organizing map (SOM) neural network technique without modeling the HRF. Since activation measured by fMRI is related to magnetic susceptibility changes in venous blood which represents only 2 - 5% of brain matter, preprocessing is required to remove the majority of non- activated voxels which dominate learning instead of real activation patterns. Using the auto-correlation function one can select voxels which are candidates of being activated. Features of the time course of the selected voxels can be learned with the SOM. In the first step the SOM is trained by the voxels time course, fitting its neurons to the input. After learning, the neurons have adapted to the intrinsic features space of the voxel time courses. Using the trained SOM, voxel time courses are presented again, now labeled by the neuron having the smallest Euclidean distance to the presented voxel time course. The result of the labeling and the learned feature time course vectors are compared visually with the p-value map of the KS statistic. With the SOM map one can visually separate the voxels based on their features in the time domain into different functional task related classes.
机译：大脑的功能性磁共振成像（fMRI）数据包括激活实质体素中，对应于执行的范例，非活化的实质体素和背景体元。统计检验，例如使用SPM或洛夫 - 斯米尔诺夫的一般线性模型方法（KS）非参数统计量，是常见的“监督”技术来寻找在功能性脑MRI活化。由体素时程与从任务范例预期血液动力学响应函数（HRF）的模型进行比较的体素类型的选择已被证明是困难的，因为所测量的HRF的个人和空间变化。对于脑的功能分化的体素我们引入分离基于使用自组织映射（SOM）神经网络技术，而不建模HRF其在时域特征的体素的脑的方法。由于由功能磁共振成像测量的激活涉及在静脉血其中仅表示2磁化率变化 - 5％的脑物质，预处理需要去除大部分未活化的体素，其主导学习，而不是真正的激活模式。使用自相关函数一个可以选择的体素，其是被激活的候选人。所选择的体素的时间过程的特征可以与SOM学习。在第一步骤中，SOM由体素时间过程的训练，其嵌合神经元到输入端。学习后，神经元已经适应了的体素时间课程的内在功能空间。用训练SOM，素时程再次出现，现在有对所呈现的体素时间过程的最小欧几里得距离的神经元标记。标记和所学习的特征时间过程向量的结果与KS统计的p值映射目测比较。随着SOM地图可以直观地分开根据自己的特点在时域成不同的功能任务相关的类体素。

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