Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology Rochester, NY, USA.
Front Syst Neurosci. 2011 Nov 17;5:93. doi: 10.3389/fnsys.2011.00093. eCollection 2011.
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.
与组水平 fMRI 分析相关的一个常见预处理挑战是将多个受试者的空间配准到标准空间。使用参考图像（如蒙特利尔神经学研究所大脑模板）进行空间归一化是目前最常用的技术，用于实现多个受试者之间的空间一致性。这种方法纠正了全局形状差异，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态组独立成分分析（ICA）网络对任务态 fMRI 数据进行共配准的新方法。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大脑如何在功能上组织的重要信息。该算法通过使用静息态 fMRI 数据的组水平独立成分分析（ICA）提取 INs 的单个主题表示来启动。在这项概念验证工作中，选择了两个稳健的、常见的网络作为功能模板。作为估计步骤，利用相关的 INs 为每个受试者推导一组归一化参数。最后，将归一化参数分别应用于受试者执行听觉异类任务时获得的不同 fMRI 数据。这些归一化参数虽然是使用休息数据得出的，但对每个受试者的认知范式获得的数据成功地进行了概括。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。每个分析方法的结果激活模式在组水平的检测灵敏度和统计显著性方面都有显著提高。本文提出的结果提供了初步证据，表明静息态大脑的常见功能域可用于改善任务 fMRI 数据的组统计数据。
