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Template based rotation: A method for functional connectivity analysis with a priori templates

机译:基于模板的轮换:使用先验模板进行功能连接性分析的方法

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摘要

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation, a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.
机译:功能连接磁共振成像(fcMRI)是了解研究环境中大脑网络层组织的有力工具,并且越来越多地用于临床试验环境中研究大规模神经元网络变性的研究。当前,通常采用各种技术,包括基于种子的相关性分析和与组无关的组件分析(采用双回归或反向投影)来计算功能连接性度量。在本报告中,我们介绍了基于模板的旋转方式, 一种优化用于先验网络分割的新颖分析方法,在临床试验中可能特别有用。基于模板的旋转被设计为通过将新颖会话中的数据映射到先前定义的先验模板上来利用从样本外数据集获得的固有连接性的稳定空间模式。我们首先证明在连接性分析中使用先前定义的先验模板的可行性,然后通过将这些分析方法应用于正常的年轻人和老年人的fMRI数据集,比较基于模板的旋转与基于种子和双重回归方法的性能。我们观察到,基于模板的旋转和对偶回归在检测年轻受试者和老年受试者之间的fcMRI差异方面大致相等,证明了跨12个皮质网络的组差异和相似的可靠性指标具有相似的效果。与基于种子的相关性分析相比,基于模板的旋转和双重回归均显示出更大的效果尺寸和相当的可靠性,尽管所有三种方法均产生相似的网络差异模式。在执行网络间和子网连接性分析时,我们发现与双重回归和基于种子的分析相比,基于模板的轮换提供更大的灵活性,更大的组差异和更稳定的连接性估计。这种灵活性归因于与双重回归相比,减少了基于模板的旋转的空间和时间正交性约束。这些结果表明,基于模板的旋转可以为现有的fcMRI分析方法提供有用的替代方法,特别是在临床试验设置中,其中预定义的结果度量和各组之间保守的网络描述非常重要。

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