Pappas Ioannis, Pardalos Panos
Department of Industrial and Systems Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America; Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America.
Department of Industrial and Systems Engineering, College of Engineering, University of Florida, Gainesville, Florida, United States of America.
PLoS One. 2015 Aug 28;10(8):e0137012. doi: 10.1371/journal.pone.0137012. eCollection 2015.
Functional Magnetic Resonance (fMRI) data can be used to depict functional connectivity of the brain. Standard techniques have been developed to construct brain networks from this data; typically nodes are considered as voxels or sets of voxels with weighted edges between them representing measures of correlation. Identifying cognitive states based on fMRI data is connected with recording voxel activity over a certain time interval. Using this information, network and machine learning techniques can be applied to discriminate the cognitive states of the subjects by exploring different features of data. In this work we wish to describe and understand the organization of brain connectivity networks under cognitive tasks. In particular, we use a regularity partitioning algorithm that finds clusters of vertices such that they all behave with each other almost like random bipartite graphs. Based on the random approximation of the graph, we calculate a lower bound on the number of triangles as well as the expectation of the distribution of the edges in each subject and state. We investigate the results by comparing them to the state of the art algorithms for exploring connectivity and we argue that during epochs that the subject is exposed to stimulus, the inspected part of the brain is organized in an efficient way that enables enhanced functionality.
功能磁共振成像（fMRI）数据可用于描绘大脑的功能连接性。已经开发出标准技术来从这些数据构建大脑网络；通常，节点被视为体素或体素集，它们之间的加权边表示相关性度量。基于fMRI数据识别认知状态与在特定时间间隔内记录体素活动有关。利用这些信息，可以应用网络和机器学习技术，通过探索数据的不同特征来区分受试者的认知状态。在这项工作中，我们希望描述和理解认知任务下大脑连接网络的组织。特别是，我们使用一种规则划分算法，该算法找到顶点簇，使得它们彼此之间的行为几乎类似于随机二分图。基于图的随机近似，我们计算三角形数量的下限以及每个受试者和状态下边分布的期望。我们将结果与探索连接性的现有算法进行比较来研究这些结果，并认为在受试者受到刺激的时期，大脑的被检查部分以一种有效的方式组织起来，从而实现增强的功能。
