Graph analysis of the human connectome: Promise, progress, and pitfalls
Graph analysis of the human connectome: Promise, progress, and pitfalls
复制标题
DOI：
10.1016/j.neuroimage.2013.04.087
复制
发表时间：
2013-10-15
期刊：
影响因子：
5.7
通讯作者：
Breakspear, Michael
中科院分区：
文献类型：
--
作者：
Fornito, Alex;Zalesky, Andrew;Breakspear, Michael
人脑是一个复杂、相互关联的卓越网络。对人类连接组进行准确且信息丰富的绘图已成为神经科学的中心目标。这一努力的核心是这样的概念：大脑连接可以抽象为节点图，代表神经元素（例如神经元、大脑区域），通过边缘连接，代表节点之间结构、功能或因果相互作用的某种度量。这种表示将连接组数据带入图论领域，提供丰富的数学工具和概念，可用于表征大脑网络的不同解剖和动力学特性。尽管这种方法具有巨大的潜力，并且在神经影像学界得到了迅速采用，但它也存在许多陷阱和未解决的挑战，如果不谨慎对待，可能会破坏该努力的解释潜力。我们回顾了这些陷阱、克服这些陷阱的普遍解决方案以及该领域前沿的挑战。 (C) 2013 Elsevier Inc. 保留所有权利。
The human brain is a complex, interconnected network par excellence. Accurate and informative mapping of this human connectome has become a central goal of neuroscience. At the heart of this endeavor is the notion that brain connectivity can be abstracted to a graph of nodes, representing neural elements (e.g., neurons, brain regions), linked by edges, representing some measure of structural, functional or causal interaction between nodes. Such a representation brings connectomic data into the realm of graph theory, affording a rich repertoire of mathematical tools and concepts that can be used to characterize diverse anatomical and dynamical properties of brain networks. Although this approach has tremendous potential - and has seen rapid uptake in the neuroimaging community - it also has a number of pitfalls and unresolved challenges which can, if not approached with due caution, undermine the explanatory potential of the endeavor. We review these pitfalls, the prevailing solutions to overcome them, and the challenges at the forefront of the field. (C) 2013 Elsevier Inc. All rights reserved.