Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices
Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices
复制标题
DOI：
10.1093/cercor/10.2.127
复制
发表时间：
2000-02-01
期刊：
影响因子：
3.7
通讯作者：
Edelman, GM
中科院分区：
文献类型：
--
作者：
Sporns, O;Tononi, G;Edelman, GM
神经解剖学对大脑皮层的功能连接提出了严格的限制。为了分析这些约束，我们研究了网络的结构特征（以图形表示）与它们作为动态系统实现时所产生的功能连接模式之间的关系。我们使用一些表征功能连接性的全局信息理论测量作为选择标准，在结构不同的图表中进行选择。我们分别选择了在熵（捕获图元素的统计独立性）、集成（捕获其统计依赖性）和复杂性（捕获其功能分离和集成之间的相互作用）方面显着增加的图。我们发现，具有高度复杂性的动力学得到了图的支持，这些图的单元被组织成密集连接的组，这些组是稀疏且相互关联的。基于描述猕猴视觉皮层和猫皮层区域和通路的实际神经解剖数据的连接矩阵显示出与此类复杂图形最一致的结构特征，揭示了不同但相互关联的解剖区域分组的存在。此外，当作为动态系统实现时，这些皮层连接矩阵生成了高度复杂的功能连接，其特征是存在高度一致的功能簇。我们还发现，在响应输入或产生输出时选择图形会导致其动态的复杂性增加。我们假设适应丰富的感觉环境和运动需求需要复杂的动力学，并且这些动力学受到大脑皮层特征的神经解剖学图案的支持。
Neuroanatomy places critical constraints on the functional connectivity of the cerebral cortex. To analyze these constraints we have examined the relationship between structural features of networks (expressed as graphs) and the patterns of functional connectivity to which they give rise when implemented as dynamical systems. We selected among structurally varying graphs using as selective criteria a number of global information-theoretical measures that characterize functional connectivity. We selected graphs separately far increases in measures of entropy (capturing statistical independence of graph elements), integration (capturing their statistical dependence) and complexity (capturing the interplay between their functional segregation and integration). We found that dynamics with high complexity were supported by graphs whose units were organized into densely linked groups that were sparsely and reciprocally interconnected. Connection matrices based on actual neuroanatomical data describing areas and pathways of the macaque visual cortex and the cat cortex showed structural characteristics that coincided best with those of such complex graphs, revealing the presence of distinct but interconnected anatomical groupings of areas. Moreover, when implemented as dynamical systems, these cortical connection matrices generated functional connectivity with high complexity, characterized by the presence of highly coherent functional clusters. We also found that selection of graphs as they responded to input or produced output led to increases in the complexity of their dynamics. We hypothesize that adaptation to rich sensory environments and motor demands requires complex dynamics and that these dynamics are supported by neuroanatomical motifs that are characteristic of the cerebral cortex.