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首页> 外文会议>Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV >Detecting Low-Frequency Functional Connectivity in fMRI Using Unsupervised Clustering Algorithms
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Detecting Low-Frequency Functional Connectivity in fMRI Using Unsupervised Clustering Algorithms

机译：使用无监督聚类算法在fMRI中检测低频功能连接

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摘要

Recent research in functional magnetic resonance imaging (fMRI) revealed slowly varying temporally correlated fluctuations between functionally related areas. These low-frequency oscillations of less than 0.08 Hz appear to be a property of symmetric cortices, and they are known to be present in the motor cortex among others. These low-frequency data are difficult to detect and quantify in fMRI. Traditionally, user-based regions of interests (ROI) or "seed clusters" have been the primary analysis method. We propose in this paper to employ unsupervised clustering algorithms employing arbitrary distance measures to detect the resting state of functional connectivity. There are two main benefits using unsupervised algorithms instead of traditional techniques: (1) the scan time is reduced by finding directly the activation data set, and (2) the whole data set is considered and not a relative correlation map. The achieved results are evaluated for different distance metrics. The Euclidian metric implemented by the standard unsupervised clustering approaches is compared with a more general topographic mapping of proximities based on the correlation and the prediction error between time courses. Thus, we are able to detect functional connectivity based on model-free analysis methods implementing arbitrary distance metrics.
机译：功能磁共振成像（fMRI）的最新研究表明功能相关区域之间的时间相关的波动缓慢变化。小于0.08 Hz的这些低频振荡似乎是对称皮层的一种特性，已知它们存在于运动皮层中。这些低频数据很难在fMRI中检测和量化。传统上，基于用户的兴趣区域（ROI）或“种子集群”已成为主要的分析方法。我们在本文中提出采用无监督的聚类算法，该算法采用任意距离度量来检测功能连接的静止状态。使用无监督算法而不是传统技术有两个主要好处：（1）通过直接查找激活数据集来减少扫描时间，并且（2）考虑整个数据集而不是相对相关图。针对不同的距离量度评估获得的结果。通过基于时间过程之间的相关性和预测误差，将标准无监督聚类方法实现的欧几里得度量与更一般的邻近地形图进行比较。因此，我们能够基于实现任意距离度量的无模型分析方法来检测功能连接。

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