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基于静息态相关性的皮质区域分割的生成与评估

Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations.

作者信息

Gordon Evan M, Laumann Timothy O, Adeyemo Babatunde, Huckins Jeremy F, Kelley William M, Petersen Steven E

机构信息

Department of Neurology.

Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.

出版信息

Cereb Cortex. 2016 Jan;26(1):288-303. doi: 10.1093/cercor/bhu239. Epub 2014 Oct 14.

Abstract

The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.

摘要

大脑皮质表面由大量的皮质区域组成;然而,这些区域在人类中尚未得到全面的测绘。静息态功能连接(RSFC)模式的突然转变可以无创地识别皮质区域之间假定边界的位置(RSFC边界测绘;科恩等人,2008年)。在这里,我们描述了一种利用RSFC边界图来定义代表假定皮质区域的脑区的技术。这些脑区具有高度同质的RSFC模式,表明它们包含一个独特的RSFC信号;此外,在两个独立的数据集中进行测试时,这些脑区比与脑区大小匹配的空模型更加同质。通过这种方式测试了几种替代的脑区划分方案,没有其他脑区划分像其空模型那样同质或与空模型有如此大的差异。由边界图得出的脑区划分包含与17区、2区、3区和4区的架构映射重叠的脑区。这些脑区具有与已知的大脑网络结构相似的网络结构,并且它们的连接模式在个体受试者之间是可靠的。这些观察结果表明,由RSFC边界图得出的脑区提供了有关人类皮质区域位置和范围的信息。使用此方法生成的脑区划分可在http://www.nil.wustl.edu/labs/petersen/Resources.html获取。

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