ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks.
ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks.
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DOI：
10.3389/fnsys.2011.00093
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发表时间：
2011
影响因子：
3
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Khullar S;Michael AM;Cahill ND;Kiehl KA;Pearlson G;Baum SA;Calhoun VD
与群体水平fMRI分析相关的一个常见的预处理挑战是多个受试者到标准空间的空间配准。空间归一化，使用参考图像，如蒙特利尔神经学研究所的大脑模板，是目前最常用的技术，用于实现跨多个主题的空间一致性。该方法校正了全局形状差异，保留了区域不对称性，但没有考虑功能差异。我们提出了一种使用静息状态群- ica网络来共同注册基于任务的fMRI数据的新方法。我们假设这些内在网络（INs）可以为空间归一化过程提供关于每个个体的大脑如何组织功能的重要信息。该算法通过对静息状态fMRI数据进行组水平独立分量分析（ICA）提取INs的单受试者表征来启动。在这个概念验证工作中，选择两个鲁棒的，通常识别的网络作为功能模板。作为估计步骤，利用相关INs为每个主题导出一组归一化参数。最后，将归一化参数分别应用于受试者在执行听觉古怪任务时获得的不同的fMRI数据集。虽然这些归一化参数是使用rest数据导出的，但它们可以成功地推广到通过每个主题的认知范式获得的数据。使用两种广泛应用的fMRI分析方法：一般线性模型和ICA，验证了结果的改进。每种分析方法产生的激活模式在检测灵敏度和组水平上的统计显著性方面都有显著改善。本文的结果提供了初步证据，表明静息状态下大脑的共同功能域可用于改善任务-功能磁共振成像数据的组统计。
A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual’s brain is organized functionally. The algorithm is initiated by the extraction of single subject representations of INs using group level independent component analysis (ICA) on resting state fMRI data. In this proof-of-concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant INs are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods: the general linear model and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics of task-fMRI data.