Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA.
Preserving subject variability in group fMRI analysis: performance evaluation of GICA vs. IVA.
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DOI：
10.3389/fnsys.2014.00106
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发表时间：
2014
影响因子：
3
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Michael AM;Anderson M;Miller RL;Adalı T;Calhoun VD
独立成分分析（伊卡）是一种广泛应用的技术，从功能磁共振成像数据中提取功能连接的大脑网络。组伊卡（GICA）和独立向量分析（IVA）是伊卡的扩展，使用户能够进行组功能磁共振成像分析;然而，GICA和IVA的性能限制的全面比较尚未调查。最近的兴趣在静息状态下的功能磁共振成像数据与潜在的更高程度的受试者的变异性，使上述技术的评价很重要。在本文中，我们使用模拟的fMRI数据集比较GICA和IVA的改进版本的分量估计精度。我们系统地改变组件的受试者间空间变异的程度，并评估所有空间地图（SM）和时间过程（TC）的分解的估计精度。我们的结果表明：（1）在低水平的SM变异性或当仅一个SM被改变时，GICA和IVA都表现良好，（2）在较高水平的SM变异性或当多于一个SM被改变时，IVA继续表现良好，但GICA产生的SM估计是具有TC中的误差的其他SM的合成，（3）GICA和IVA都去除了重叠SM的空间相关性，并在它们的TC中引入了人工相关性。（4）如果SM的数量被高估，IVA继续表现良好，但GICA在变化的和额外的SM中引入伪影，在额外成分的TC中具有人工相关性，以及（5）在不存在或存在一个受试者特有的SM的情况下，GICA在TC中产生误差，IVA估计值是准确的。总之，我们的模拟实验（简化和现实）和我们的整体分析方法表明，IVA产生的结果更接近地面真理，从而更好地保留受试者的变异性。改进后的国际脆弱性评估现已纳入全球打击人口贩运举措工具箱（http：//mialab.mrn.org/software/gift）。
Independent component analysis (ICA) is a widely applied technique to derive functionally connected brain networks from fMRI data. Group ICA (GICA) and Independent Vector Analysis (IVA) are extensions of ICA that enable users to perform group fMRI analyses; however a full comparison of the performance limits of GICA and IVA has not been investigated. Recent interest in resting state fMRI data with potentially higher degree of subject variability makes the evaluation of the above techniques important. In this paper we compare component estimation accuracies of GICA and an improved version of IVA using simulated fMRI datasets. We systematically change the degree of inter-subject spatial variability of components and evaluate estimation accuracy over all spatial maps (SMs) and time courses (TCs) of the decomposition. Our results indicate the following: (1) at low levels of SM variability or when just one SM is varied, both GICA and IVA perform well, (2) at higher levels of SM variability or when more than one SMs are varied, IVA continues to perform well but GICA yields SM estimates that are composites of other SMs with errors in TCs, (3) both GICA and IVA remove spatial correlations of overlapping SMs and introduce artificial correlations in their TCs, (4) if number of SMs is over estimated, IVA continues to perform well but GICA introduces artifacts in the varying and extra SMs with artificial correlations in the TCs of extra components, and (5) in the absence or presence of SMs unique to one subject, GICA produces errors in TCs and IVA estimates are accurate. In summary, our simulation experiments (both simplistic and realistic) and our holistic analyses approach indicate that IVA produces results that are closer to ground truth and thereby better preserves subject variability. The improved version of IVA is now packaged into the GIFT toolbox (http://mialab.mrn.org/software/gift).