Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty.
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty.
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DOI：
10.1016/j.neuroimage.2011.11.054
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发表时间：
2012-02-15
期刊：
影响因子：
5.7
通讯作者：
Menon, Vinod
中科院分区：
文献类型：
--
作者：
Ryali, Srikanth;Chen, Tianwen;Supekar, Kaustubh;Menon, Vinod
描述多个大脑区域之间的相互作用对于理解大脑功能非常重要。基于部分相关性的功能连接性测量提供了在去除其他区域的线性影响之后对脑区域之间的线性条件依赖的估计。然而，当区域的数量很大时，偏相关性的估计是困难的，因为现在越来越多的大规模大脑连接研究的情况下。为了解决这个问题，我们开发了新的方法来估计稀疏偏相关多个区域之间的fMRI数据使用弹性净罚（SPC EN），我们证明了SPC-EN中的L1-范数正则化提供了稀疏可解释的解，而L2-范数正则化提供了稀疏可解释的解。范数正则化在区域之间的可能连接的数目大于时间点的数目时提高了该方法的灵敏度，当大脑区域之间的成对相关性很高时。基于正则化的方法的一个问题是选择正则化参数，这反过来又决定了大脑区域之间连接的选择。为了解决这个问题，我们部署了新的稳定性选择方法来推断大脑区域之间的重要联系。我们还比较了SPC-EN的性能与现有的方法，只使用L1范数正则化（SPC-L1）的模拟和实验数据集。详细的模拟表明，性能的SPC-EN，测量的灵敏度和准确性方面是上级SPC-L1，特别是在较高的功能流行率。应用我们的方法从22名健康成年人获得的静息态fMRI数据表明，SPC-EN揭示了一个模块化的架构，其特征在于强大的半球间联系，不同的腹侧和背侧流通路，以及后内侧皮质的主要枢纽-传统方法错过的功能。综上所述，我们的研究结果表明，SPC-EN提供了一个强大的工具，用于表征连接，涉及大量的相关区域，跨越整个大脑。
Characterizing interactions between multiple brain regions is important for understanding brain function. Functional connectivity measures based on partial correlation provide an estimate of the linear conditional dependence between brain regions after removing the linear influence of other regions. Estimation of partial correlations is, however, difficult when the number of regions is large, as is now increasingly the case with a growing number of large-scale brain connectivity studies. To address this problem, we develop novel methods for estimating sparse partial correlations between multiple regions in fMRI data using elastic net penalty (SPC-EN), which combines L1- and L2-norm regularization We show that L1-norm regularization in SPC-EN provides sparse interpretable solutions while L2-norm regularization improves the sensitivity of the method when the number of possible connections between regions is larger than the number of time points, and when pair-wise correlations between brain regions are high. An issue with regularization-based methods is choosing the regularization parameters which in turn determine the selection of connections between brain regions. To address this problem, we deploy novel stability selection methods to infer significant connections between brain regions. We also compare the performance of SPC-EN with existing methods which use only L1-norm regularization (SPC-L1) on simulated and experimental datasets. Detailed simulations show that the performance of SPC-EN, measured in terms of sensitivity and accuracy is superior to SPC-L1, especially at higher rates of feature prevalence. Application of our methods to resting-state fMRI data obtained from 22 healthy adults show that SPC-EN reveals a modular architecture characterized by strong inter-hemispheric links, distinct ventral and dorsal stream pathways, and a major hub in the posterior medial cortex- features that were missed by conventional methods. Taken together, our findings suggest that SPC-EN provides a powerful tool for characterizing connectivity involving a large number of correlated regions that span the entire brain.
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影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET
DOI：
10.1109/tpami.2010.186
发表时间：
2011-07-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Pekar, JJ
DOI：
10.1523/jneurosci.4557-10.2011
发表时间：
2011-03-09
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
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作者：
Liang Z;King J;Zhang N
通讯作者：
Zhang N