Lin Cui, Pai Darshan, Lu Shiyong, Muzik Otto, Hua Jing
Department of Computer Science, Wayne State University, Detroit, MI 48202, USA.
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):514-25. doi: 10.1109/TITB.2010.2040286. Epub 2010 Jan 29.
One of the fundamental goals of computational neuroscience is the study of anatomical features that reflect the functional organization of the brain. The study of physical associations between neuronal structures and the examination of brain activity in vivo have given rise to the concept of anatomical and functional connectivity, which has been invaluable for our understanding of brain mechanisms and their plasticity during development. However, at present, there is no robust and accurate computational framework for the quantitative assessment of cortical connectivity patterns. In this paper, we present a quantitative analysis and modeling tool that is able to characterize anatomical connectivity patterns based on a newly developed coclustering algorithm, termed the business model-based coclustering algorithm (BCA). We apply BCA to diffusion tensor imaging (DTI) data in order to provide an automated and reproducible assessment of the connectivity patterns between different cortical areas in human brains. BCA not only partitions the cortical mantel into well-defined clusters, but also maximizes the connectivity strength between these clusters. Moreover, BCA is computationally robust and allows both outlier detection as well as parameter-independent determination of the number of clusters. Our coclustering results have showed good performance of BCA in identifying major white matter fiber bundles in human brains and facilitate the detection of abnormal connectivity patterns in patients suffering from various neurological diseases.
计算神经科学的基本目标之一是研究反映大脑功能组织的解剖特征。对神经元结构之间物理关联的研究以及对活体大脑活动的检查催生了解剖和功能连接性的概念，这对于我们理解大脑机制及其在发育过程中的可塑性非常宝贵。然而，目前尚无用于定量评估皮质连接模式的强大而准确的计算框架。在本文中，我们提出了一种定量分析和建模工具，该工具能够基于一种新开发的共聚类算法（称为基于商业模式的共聚类算法，BCA）来表征解剖连接模式。我们将BCA应用于扩散张量成像（DTI）数据，以便对人类大脑不同皮质区域之间的连接模式进行自动化且可重复的评估。BCA不仅将皮质幔层划分为定义明确的簇，还能使这些簇之间的连接强度最大化。此外，BCA在计算上很稳健，既允许进行异常值检测，也能独立于参数确定簇的数量。我们的共聚类结果表明，BCA在识别人类大脑主要白质纤维束方面表现良好，并有助于检测患有各种神经疾病的患者的异常连接模式。
