On testing for spatial correspondence between maps of human brain structure and function.

On testing for spatial correspondence between maps of human brain structure and function.
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DOI:
10.1016/j.neuroimage.2018.05.070
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发表时间:
2018-09
期刊:
影响因子:
5.7
通讯作者:
Raznahan A
Raznahan A
中科院分区:
医学1区
文献类型:
--
作者:
Alexander-Bloch AF;Shou H;Liu S;Satterthwaite TD;Glahn DC;Shinohara RT;Vandekar SN;Raznahan A

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许多神经影像学研究中的一个关键问题是大脑地图之间的比较。尽管如此,人们仍然不清楚应该如何测试两个或多个大脑地图之间的重叠或空间对应关系的假设。例如,这种“对应问题”影响了对基于任务的功能激活模式、静息状态网络或模块以及神经解剖学标志之间的比较的解释。迄今为止,在处理这一问题的方法和统计严谨性方面存在显著差异。在本文中,我们解决的对应问题,使用空间置换框架,以产生空模型的重叠,通过应用随机旋转的皮质表面的球形表示,我们也提供了一个理论统计基础的方法。我们使用这种方法来获得集群的认知功能相关的功能neuroatomical基板。此外,使用公开可用的数据,我们正式证明了基于任务的功能活动,静息态功能磁共振成像网络和基于脑回的解剖标志之间的映射的对应关系。我们提供了开放访问的代码来实现两个常用的工具,基于表面的皮层分析的方法。这种空间排列的方法构成了一个有用的进步,广泛使用的方法比较皮层地图,从而开辟了新的可能性,整合不同的神经影像数据。
A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This “correspondence problem” affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap, by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task- based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis. This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data.
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