Automatic identification of functional clusters in FMRI data using spatial dependence.
Automatic identification of functional clusters in FMRI data using spatial dependence.
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DOI：
10.1109/tbme.2011.2167149
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发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Adalı T
中科院分区：
文献类型：
--
作者：
Ma S;Correa NM;Li XL;Eichele T;Calhoun VD;Adalı T
在功能磁共振成像（fMRI）数据的独立成分分析（伊卡）中，提取大量的最大独立成分可以提供更精细的脑功能分割。然而，这种分割本身并不能建立不同大脑网络之间的关系，并且选择和分类组件也可能具有挑战性。在这项工作中，我们提出了一种多维伊卡（云母）方案来实现自动组件聚类。在这个云母框架中，稳定的组件被分层分组到集群的基础上的空间信息和高阶统计，而不是通常使用的时间信息和二阶相关性。最终的聚类成员是使用统计假设检验方法确定的。从模拟和真实的fMRI数据集的实验结果表明，只使用空间信息与高阶统计导致生理上有意义的依赖结构的大脑网络，这是一致的识别在各种伊卡模型的订单和算法。此外，我们观察到与伪影相关的成分，包括脑脊液（CSF）、动脉和大引流静脉，在它们之间表现出更高程度的依赖性，并且使用我们的云母方法与其他感兴趣的成分明显不同。
In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a more refined functional segmentation of brain. However, such segmentation does not per se establish the relationships among different brain networks, and also selecting and classifying components can be challenging. In this work, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In this MICA framework, stable components are hierarchically grouped into clusters based on spatial information and higher-order statistics, instead of typically used temporal information and second-order correlation. The final cluster membership is determined using a statistical hypothesis testing method. The experimental results from both simulated and real fMRI data sets show that the use of only spatial information with higher-order statistics leads to physiologically meaningful dependence structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid (CSF), arteries, and large draining veins, demonstrate a higher degree of dependence among them and encouragingly distinguished from other components of interest using our MICA approach.