Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.
Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations.
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解决人脑组织中的解剖学和功能结构：在加权网络表示中识别中尺度组织。
DOI：
10.1371/journal.pcbi.1003712
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发表时间：
2014-10
影响因子：
4.3
通讯作者：
Carlson JM
中科院分区：
文献类型：
--
作者：
Lohse C;Bassett DS;Lim KO;Carlson JM
人脑解剖和功能显示出模块化和分层组织的结合，表明内聚结构和可变分辨率在促进健康认知过程中的重要性。然而，同时探测大脑结构这些特征的工具需要进一步发展。我们提出并应用了一套方法，利用多分辨率技术提取大脑连接网络表示中的内聚结构。我们在群体检测中结合了软阈值、窗口阈值和分辨率，使我们能够识别和隔离与不同权重相关的结构。其中一种中尺度结构是双侧性，它量化了大脑被分为两个分区的程度，分区之间的连通性高，分区内的连通性低。第二种互补的中尺度结构是模块化，它量化了大脑分为多个群落的程度，每个群落内部的连通性很强，而群落之间的连通性很弱。我们的方法可以在空间、几何和结构尺度的范围内得到这些网络诊断的多分辨率曲线。为了进行统计比较，我们将我们的结果与几个基准零模型的结果进行了对比。我们的工作表明，多分辨率诊断曲线在加权图中捕获复杂的组织概况。我们将这些方法应用于识别健康加权图结构的分辨率特异性特征和精神疾病中改变的连接概况。人类的大脑是一个迷人的器官，充满了精致的解剖和功能细节。该细节的一个显著特征在于，在组织的层次结构级别上，存在相互嵌套的小模块。在这里，我们开发并应用计算分析工具，通过检查网络表示来探测大脑结构的这些特征，其中大脑区域被视为网络节点，区域之间的链接被视为网络边缘。我们描述的方法类被称为“多分辨率技术”，使我们能够识别和隔离与不同边缘属性相关的神经结构。我们的方法可以在空间、几何和结构尺度的范围内得到这些网络诊断的多分辨率曲线。为了进行统计比较，我们将我们的结果与几个基准零模型的结果进行了对比。我们的工作表明，多分辨率诊断曲线在加权图中捕获复杂的组织概况。我们将这些方法应用于识别健康加权图结构的分辨率特异性特征和精神疾病中改变的连接概况。
Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease. The human brain is a fascinating organ full of exquisite anatomical and functional detail. A striking feature of this detail lies in the presence of small modules nested within one another across hierarchical levels of organization. Here we develop and apply computational analysis tools to probe these features of brain architecture by examining network representations in which brain areas are treated as network nodes and links between areas are treated as network edges. The class of methods that we describe are referred to as “multi-resolution techniques” and enable us to identify and isolate neural structures associated with different edge properties. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.
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DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bassett DS;Bullmore E;Verchinski BA;Mattay VS;Weinberger DR;Meyer-Lindenberg A
通讯作者：
Meyer-Lindenberg A
影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET
影响因子：
5.7
作者：
Bassett, Danielle S.;Nelson, Brent G.;Mueller, Bryon A.;Camchong, Jazmin;Lim, Kelvin O.
通讯作者：
Lim, Kelvin O.
DOI：
10.1146/annurev-clinpsy-040510-143934
发表时间：
2011-01-01
影响因子：
18.4
作者：
Bullmore, Edward T.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.
影响因子：
3.7
作者：
Chen, Zhang J.;He, Yong;Evans, Alan C.
通讯作者：
Evans, Alan C.