A MEASURE FOR BRAIN COMPLEXITY - RELATING FUNCTIONAL SEGREGATION AND INTEGRATION IN THE NERVOUS-SYSTEM
A MEASURE FOR BRAIN COMPLEXITY - RELATING FUNCTIONAL SEGREGATION AND INTEGRATION IN THE NERVOUS-SYSTEM
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DOI：
10.1073/pnas.91.11.5033
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发表时间：
1994-05-24
影响因子：
11.1
通讯作者：
EDELMAN, GM
中科院分区：
文献类型：
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作者：
TONONI, G;SPORNS, O;EDELMAN, GM
在高等脊椎动物的大脑中，在解剖学和生理学上不同的局部区域的功能分离与它们在感知和行为过程中的整体整合形成鲜明对比。在本文中，我们介绍了一种称为神经复杂性（C-N）的测量方法，它可以捕获大脑组织的这两个基本方面之间的相互作用。我们表达的神经系统内的功能隔离的相对统计独立的小子集的系统和功能集成的大子集的独立性的显着偏差。然后，根据大小不断增加的子集的统计独立性平均偏差的估计值获得C-N。C-N被证明是高时，功能隔离与集成共存，并在系统的组件是完全独立的（隔离）或完全依赖（集成）时是低的。我们将这种复杂性度量应用于皮层区域的计算机模拟，以研究神经解剖组织的一些基本原则如何约束大脑动力学。我们发现，大脑皮层的连接模式，如高密度的连接，强大的本地连接组织细胞成神经元组，神经元组之间的连接补丁，和普遍的相互连接，与高值的C-N这里概述的方法可能证明是有用的在其他生物领域，如基因调控和胚胎发生的复杂性分析。
In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (C-N), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. C-N is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. C-N is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of C-N The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.