Using Pareto optimality to explore the topology and dynamics of the human connectome.
Using Pareto optimality to explore the topology and dynamics of the human connectome.
复制标题
DOI：
10.1098/rstb.2013.0530
复制
发表时间：
2014-10-05
期刊：
影响因子：
--
通讯作者：
Sporns O
中科院分区：
文献类型：
--
作者：
Avena-Koenigsberger A;Goñi J;Betzel RF;van den Heuvel MP;Griffa A;Hagmann P;Thiran JP;Sporns O
图论为分析人脑网络结构提供了一个关键的数学框架。该体系结构体现了连接拓扑、网络元素的空间布局以及由此产生的网络成本和功能性能之间的内在复杂关系。对这些相互作用的因素和驱动力的探索可能会揭示出显著的网络特征，这些特征对于塑造和约束大脑的拓扑组织及其进化能力至关重要。一些研究指出了网络成本和网络效率之间的经济平衡，网络组织在一个“经济的”小世界中，有利于以低布线成本获得高通信效率。在这项研究中，我们定义并探索了一种网络形态空间，以表征人脑网络中不同方面的通信效率。使用多目标进化方法，在形态空间内逼近Pareto最优集，我们研究了解剖脑网络在保持网络成本的同时向呈现最佳信息处理特征的拓扑进化的能力。这种方法使我们能够调查在特定选择压力下出现的网络拓扑，从而为可能塑造现有人脑网络架构的选择力量提供了一些洞察。
Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.
登录
查看更多内容
影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
影响因子：
3.7
作者：
Goñi J;Avena-Koenigsberger A;Velez de Mendizabal N;van den Heuvel MP;Betzel RF;Sporns O
通讯作者：
Sporns O
影响因子：
2.4
作者：
Barnett, L.;Buckley, C. L.;Bullock, S.
通讯作者：
Bullock, S.
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：
1.1
作者：
Esteve-Altava, Borja;Rasskin-Gutman, Diego
通讯作者：
Rasskin-Gutman, Diego