Consistent Estimation of Dimensionality for Data-Driven Methods in fMRI Analysis.
Consistent Estimation of Dimensionality for Data-Driven Methods in fMRI Analysis.
复制标题
fMRI 分析中数据驱动方法的维数一致估计。
DOI：
10.1109/tmi.2018.2866640
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发表时间：
2019
影响因子：
10.6
通讯作者：
Shokouhi,Navid
中科院分区：
文献类型：
--
作者：
Seghouane,Abd-Krim;Shokouhi,Navid
主成分分析和独立成分分析等数据驱动方法已成功应用于功能磁共振成像（fMRI）数据和一般神经影像数据。这些方法的核心问题是正确选择因子模型中使用的组件数量的重要性。这个问题通常使用模型选择标准来解决，其中拟合优度项是从对数似然函数获得的。在本文中，提出了选择组件数量的替代标准。与使用对数似然函数的现有模型选择标准不同，所提出的拟合优度术语使用样本协方差矩阵的最小特征值的平方和。所提出的标准是从拟合优度项的渐近分布获得的，并为此建立了一致性。该标准具有直接的实施方式，并且被证明优于功能磁共振成像数据分析中使用的传统模型选择标准。使用模拟和真实功能磁共振成像数据进行实验，其中所提出的标准在数据变化下的准确性和一致性方面都获得了改进的性能。
Data-driven methods, such as principal component analysis and independentcomponent analysis, have been successfully applied to functionalmagnetic resonance imaging (fMRI) data in particular and neuro-imaging data in general. A central issue of thesemethods is the importance of correctly selecting the number of components to be used in the factor model. This issue is often addressed using a model selection criterion, where the goodness-of-fit term is obtained from the log-likelihood function. In this paper, an alternative criterion is proposed for selecting the number of components. Unlike existingmodel selection criteria that use the log-likelihood function, the proposed goodness-of-fit termuses the sum of squares of the smallest eigenvalues of the sample covariance matrix. The proposed criterion is obtained from the asymptotic distribution of the goodness-of-fit term, for which consistency is established. This criterion has a straight-forward implementation and is shown to outperform conventional model selection criteria used in fMRI data analysis. Experiments are conducted using simulated and real fMRI data, in which improved performance is obtained by the proposed criterion, both in terms of accuracy and consistency under data variabilities.