Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data.
Dimensionality estimation for optimal detection of functional networks in BOLD fMRI data.
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DOI：
10.1016/j.neuroimage.2010.09.034
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发表时间：
2011-05-15
期刊：
影响因子：
5.7
通讯作者：
Strother SC
中科院分区：
文献类型：
--
作者：
Yourganov G;Chen X;Lukic AS;Grady CL;Small SL;Wernick MN;Strother SC
对fMRI数据的固有维数进行估计是数据分析的重要组成部分，有助于将感兴趣的信号从噪声中分离出来。我们研究了文献中提出的多种维数估计方法，并使用这些估计来选择随后通过线性判别分析（LDA）处理的主成分子集。使用模拟的多变量高斯数据，我们表明，优化信号检测的维度（在接收器工作特性（ROC）度量方面）经历了从多维到一维的过渡，作为信噪比的函数。当激活位点被组织成一个空间网络，并且网络化的任务相关信号的方差足够高，可以在数据中轻松检测到信号时，就会发生这种转变。我们发现，激活地图的再现性是一个度量，捕捉这种开关的内在维度。除了可重复性，我们考虑的所有维度估计方法都未能捕捉到这种转变：贝叶斯证据的优化，最小描述长度，监督和无监督LDA预测，以及Stein的无偏风险估计。这种失败导致LDA在空间分布网络存在下的次优ROC性能，并且可能导致LDA在文献中的许多报告比较中表现不佳。使用真实的fMRI数据集，包括多主题组和主题内的纵向分析，我们证明了这些维度转换的存在，在真实的数据。
Estimation of the intrinsic dimensionality of fMRI data is an important part of data analysis that helps to separate the signal of interest from noise. We have studied multiple methods of dimensionality estimation proposed in the literature and used these estimates to select a subset of principal components that was subsequently processed by linear discriminant analysis (LDA). Using simulated multivariate Gaussian data, we show that the dimensionality that optimizes signal detection (in terms of the receiver operating characteristic (ROC) metric) goes through a transition from many dimensions to a single dimension as a function of the signal-to-noise ratio. This transition happens when the loci of activation are organized into a spatial network and the variance of the networked, task-related signals is high enough for the signal to be easily detected in the data. We show that reproducibility of activation maps is a metric that captures this switch in intrinsic dimensionality. Except for reproducibility, all of the methods of dimensionality estimation we considered failed to capture this transition: optimization of Bayesian evidence, minimum description length, supervised and unsupervised LDA prediction, and Stein's unbiased risk estimator. This failure results in sub-optimal ROC performance of LDA in the presence of a spatially distributed network, and may have caused LDA to underperform in many of the reported comparisons in the literature. Using real fMRI data sets, including multi-subject group and within-subject longitudinal analysis we demonstrate the existence of these dimensionality transitions in real data.
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影响因子：
5.7
作者：
LaConte, S;Anderson, J;Strother, S
通讯作者：
Strother, S
影响因子：
3.2
作者：
Grady, CL;Springer, MV;Winocur, G
通讯作者：
Winocur, G
影响因子：
5.7
作者：
Cordes, D;Nandy, RR
通讯作者：
Nandy, RR
DOI：
10.1088/0305-4470/27/6/015
发表时间：
1994-03-21
期刊：
JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL
影响因子：
--
作者：
BIEHL, M;MIETZNER, A
通讯作者：
MIETZNER, A
影响因子：
2.4
作者：
Hoyle, D. C.;Rattray, M.
通讯作者：
Rattray, M.