Hierarchical clustering to measure connectivity in fMRI resting-state data
Hierarchical clustering to measure connectivity in fMRI resting-state data
复制标题
DOI：
10.1016/s0730-725x(02)00503-9
复制
发表时间：
2002-05-01
影响因子：
2.5
通讯作者：
Maravilla, K
中科院分区：
文献类型：
--
作者：
Cordes, D;Haughton, V;Maravilla, K
低频振荡在功能相关的脑区在时间上具有相关性，是哺乳动物大脑的特征，即使在没有执行明确的认知任务时也是如此。功能性连接磁共振成像用于绘制静息大脑中显示脑血流和氧合同步、区域性和缓慢波动的区域。在这项研究中，我们使用一种层次聚类方法来检测低频波动的相似性。我们描述了一种用于静息态功能磁共振成像数据分类的低频范围相关性的测量方法。此外，我们研究了运动和硬件不稳定性对静息态相关性的影响，并提供了一种减少伪影的方法。对于所研究的所有皮质区域和获得的聚类，我们量化了呼吸和心脏周期对功能连接图的污染程度。结果表明，通过层次聚类可以获得类似于已知神经元连接的功能连接模式。相应的体素时间序列在呼吸或心脏频段没有显示出显著的相关性。（C）2002爱思唯尔科学公司。保留所有权利。
Low frequency oscillations, which-are temporally correlated in functionally related brain regions, characterize the mammalian brain, even when no explicit cognitive-tasks are performed. Functional connectivity MR imaging is used to map regions of the resting brain showing synchronous, regional and slow fluctuations in cerebral blood flow and oxygenation. In this study, we use a hierarchical clustering method to detect similarities of low-frequency fluctuations. We describe one measure of correlations in the low frequency range for classification of resting-state. fMRI data. Furthermore, we investigate the contribution of motion and hardware instabilities to resting-state correlations and provide a-method to reduce artifacts. For all cortical regions studied and clusters obtained, we quantify the degree of contamination of functional connectivity maps by the respiratory and cardiac cycle. Results indicate that patterns of functional connectivity can be obtained with hierarchical clustering that resemble known neuronal connections. The corresponding voxel time series do not show significant correlations in the respiratory or cardiac frequency band. (C) 2002 Elsevier Science Inc. All rights reserved.