The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability.
The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability.
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DOI：
10.1016/j.neuroimage.2019.07.003
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发表时间：
2019-11-15
期刊：
影响因子：
5.7
通讯作者：
Bassett DS
中科院分区：
文献类型：
--
作者：
Betzel RF;Bertolero MA;Gordon EM;Gratton C;Dosenbach NUF;Bassett DS
人脑的网络组织因人而异，随着发育和衰老而变化，并且在疾病中也有所不同。发现这种变异性表现的主要维度仍然是神经科学和临床医学的中心目标。这些努力可以在大脑模块化网络组织的背景下有效地构建，可以使用强大的计算技术对其进行定量评估，并扩展到多尺度分析、降维和生物标记物生成的目的。尽管模块化的概念及其在描述大脑网络组织方面的实用性是明确的，但令人惊讶的是缺乏比较跨个体和时间的多尺度社区的原则方法。在这里，我们提出了一种使用多层网络同时发现许多主题的模块化结构的方法。该方法建立在众所周知的多层模块化最大化技术的基础上，并提供了一种可行且有原则的工具，用于研究个体之间和个体内部跨时间网络社区的差异。我们在两个数据集上测试了这种方法，并确定了受试者间社区变异性的一致模式，证明这种变异性（使用过去的方法无法检测到）与认知表现的测量相关。一般来说，这里提出的多层、多主题框架通过直接映射跨主题的社区分配代表了当前方法的进步，并为未来研究临床人群中的主题间社区变异或由于任务限制而带来的希望。
The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brain’s modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability – which would be undetectable using past approaches – is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.
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DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
通讯作者：
Grafton, Scott T.
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne
影响因子：
16.6
作者：
Betzel, Richard F.;Medaglia, John D.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.
影响因子：
3
作者：
Alexander-Bloch, Aaron F.;Gogtay, Nitin;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
5.7
作者：
Bellec, Pierre;Rosa-Neto, Pedro;Evans, Alan C.
通讯作者：
Evans, Alan C.