Querying functional brain connectomics to discover consistent subgraph patterns
Querying functional brain connectomics to discover consistent subgraph patterns
复制标题
查询功能性大脑连接组学以发现一致的子图模式
DOI：
10.1109/bibe.2013.6701655
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
K. Tsichlas
中科院分区：
文献类型：
--
作者：
Nantia D. Iakovidou;S. Dimitriadis;N. Laskaris;K. Tsichlas
功能活动图的动态记录可以自然和有效地以功能/有效连接网络的形式表示。映射突触连接和记录神经信号的新方法产生了关于大脑网络结构和动力学的丰富而复杂的数据。为了研究自然界中最复杂的网络--大脑，需要将从世界各地的实验室收集的大量大脑网络整合到大型数据库中。人类大脑计划（欧洲和美国）旨在以各种方式探索大脑功能。大脑网络是实现这一雄心勃勃计划目标的核心。然而，成千上万的大脑网络的巨大数量，阻止了一个简单的方法来利用知识。在本文中，我们展示了一个数据驱动的方法，发现一致的模式，从收集的大脑网络通过查询的方法：制定一个查询“找到一个增加或减少一致的子图超过一定数量的主题”后，采取两组图之间的差异称为两个条件（一个积极的和基线）。实验表明，我们的数据驱动的方法可以识别脑电功能连接网络的频率依赖的选择性空间模式的变化在精神任务。这是第一次有一种方法充分利用大脑网络的连接权重来发现一致的子图模式。
Dynamic recordings of functional activity maps can naturally and efficiently be represented in the form of functional/effective connectivity networks. New methods for mapping synaptic connections and recording neural signals generate rich and complex data about the structure and dynamics of brain networks. To study the most complex network in nature, the brain, there is need to integrate a huge amount of brain networks collected from laboratories over the world in large databases. Human Brain Project (Europe and USA) aims to explore brain functionality in various ways. Brain networks are central to achieving the goals of this ambitious plan. However, the immense amount of thousands of brain networks, prevent an easy way to utilizable knowledge. In this paper, we demonstrate a data-driven approach that discovers consistent patterns from a collection of brain networks via a querying approach: formulating a query of “finding an increasing or a decreasing consistent subgraph over an amount of subjects” after taking the difference between two sets of graphs referred as two conditions (an active and a baseline). Experiments demonstrated that our data-driven approach allows identifying frequency-dependent selective spatial pattern changes of the EEG functional connectivity network during a mental task. This is the first time that a method fully exploits the connectivity weights of a brain network to discover consistent subgraph patterns.