Betzel Richard F, Bertolero Maxwell A, Gordon Evan M, Gratton Caterina, Dosenbach Nico U F, Bassett Danielle S
Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47401, USA; Cognitive Science Program, Indiana University, Bloomington, IN, 47401, USA.
Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Neuroimage. 2019 Nov 15;202:115990. doi: 10.1016/j.neuroimage.2019.07.003. Epub 2019 Jul 7.
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 computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Although 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 advance 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.
人类大脑的网络组织因人而异，会随着发育和衰老而变化，在疾病状态下也有所不同。找出这种变异性所呈现的主要维度，仍然是神经科学和临床医学的核心目标。这些研究工作可以在大脑模块化网络组织的背景下有效地展开，大脑模块化网络组织可以通过计算技术进行定量评估，并为多尺度分析、降维和生物标志物生成等目的进行扩展。尽管模块化概念及其在描述脑网络组织方面的效用是明确的，但令人惊讶的是，缺乏用于跨个体和跨时间比较多尺度群落的原则性方法。在此，我们提出一种使用多层网络同时发现多个受试者模块化结构的方法。该方法基于著名的多层模块化最大化技术构建，为研究个体间以及个体内随时间变化的网络群落差异提供了一个可行且有原则的工具。我们在两个数据集上测试了该方法，并识别出受试者间群落变异性的一致模式，证明这种变异性（使用过去的方法无法检测到）与认知表现指标相关。总体而言，本文提出的多层、多受试者框架通过直接跨受试者映射群落分配，代表了相对于当前方法的进步，并有望用于未来对临床人群中受试者间群落差异或因任务限制导致的差异的研究。
