Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data.
Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data.
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DOI：
10.1016/j.neuroimage.2009.12.119
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发表时间：
2010-04-15
期刊：
影响因子：
5.7
通讯作者：
Constable RT
中科院分区：
文献类型：
--
作者：
Shen X;Papademetris X;Constable RT
静息状态功能磁共振成像提供了一种在自发波动下检查大脑功能网络的方法。许多研究建议使用静息状态的粗体数据来将大脑划分为功能亚单位。在这项工作中，我们提出了两种最新的基于图的分割方法，并研究了它们在基于静息状态fMRI的脑网络分割问题中的应用。将归一化割法(NCut)和模块化检测算法与高斯混合模型(GMM)方法进行了比较。我们表明，NCut方法的性能始终好于模块化检测方法，并且对于活体fMRI数据，它也优于GMM方法。采集了43名健康受试者的静息状态fMRI数据，并使用NCut算法来分割几个不同的皮质感兴趣区。基于静息状态fMRI的功能亚基的分组描述与两个基于任务的fMRI研究(一个包括18个受试者，另一个包括20个受试者)的分割结果高度一致。这一发现表明，使用静息状态功能磁共振成像对大脑皮质进行全脑分割是可行的，而NCut算法为这项任务提供了适当的技术。
Resting-state fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using resting-state BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using resting-state fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-state fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on resting-state fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using resting-state fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task.
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影响因子：
3.7
作者：
Sporns O;Honey CJ;Kötter R
通讯作者：
Kötter R
DOI：
10.1109/tpami.2004.60
发表时间：
2004-09-01
影响因子：
23.6
作者：
Boykov, Y;Kolmogorov, V
通讯作者：
Kolmogorov, V
DOI：
10.1073/pnas.0905267106
发表时间：
2009-08-04
影响因子：
11.1
作者：
Smith, Stephen M.;Fox, Peter T.;Beckmann, Christian F.
通讯作者：
Beckmann, Christian F.
DOI：
10.1109/34.868688
发表时间：
2000-08-01
影响因子：
23.6
作者：
Shi, JB;Malik, J
通讯作者：
Malik, J
DOI：
10.1073/pnas.0601602103
发表时间：
2006-06-06
影响因子：
11.1
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.