Utility of Partial Correlation for Characterising Brain Dynamics: MVPA-based Assessment of Regularisation and Network Selection

E. Duff, T. Makin, Sasidhar S. Madugula, Stephen M. Smith, M. Woolrich
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引用次数: 8

Abstract

Correlation and partial correlation are often used to provide a characterisation of the network properties of the human brain, based on functional brain imaging data. However, for partial correlation, the choice of network nodes (brain regions) and regularisation parameters is crucial and not yet well explored. Here we assess a number of approaches by calculating how each approach performs when used to discriminate different ongoing states of brain activity. We find evidence that partial correlation matrices, when estimated with appropriate regularisation, can provide a useful characterisation of brain functional connectivity.
脑动力学特征的部分相关的效用:基于mvpa的正则化和网络选择评估
基于功能性脑成像数据,相关性和部分相关性通常用于提供人脑网络特性的表征。然而,对于部分相关,网络节点(大脑区域)和正则化参数的选择是至关重要的,但尚未得到很好的探索。在这里,我们通过计算每个方法在区分不同的大脑活动状态时的表现来评估许多方法。我们发现部分相关矩阵的证据,当用适当的正则化估计时,可以提供有用的脑功能连接特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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