Wang Qi, Chen Rong, JaJa Joseph, Jin Yu, Hong L Elliot, Herskovits Edward H
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA.
Department of Radiology, University of Maryland, 22 S Greene St, Baltimore, MD, 21201, USA.
Neuroinformatics. 2016 Jan;14(1):83-97. doi: 10.1007/s12021-015-9280-7.
Defining brain structures of interest is an important preliminary step in brain-connectivity analysis. Researchers interested in connectivity patterns among brain structures typically employ manually delineated volumes of interest, or regions in a readily available atlas, to limit the scope of connectivity analysis to relevant regions. However, most structural brain atlases, and manually delineated volumes of interest, do not take voxel-wise connectivity patterns into consideration, and therefore may not be ideal for anatomic connectivity analysis. We herein propose a method to parcellate the brain into regions of interest based on connectivity. We formulate connectivity-based parcellation as a graph-cut problem, which we solve approximately using a novel multi-class Hopfield network algorithm. We demonstrate the application of this approach using diffusion tensor imaging data from an ongoing study of schizophrenia. Compared to a standard anatomic atlas, the connectivity-based atlas supports better classification performance when distinguishing schizophrenic from normal subjects. Comparing connectivity patterns averaged across the normal and schizophrenic subjects, we note significant systematic differences between the two atlases.
定义感兴趣的脑结构是脑连接性分析中的一个重要前期步骤。对脑结构间连接模式感兴趣的研究人员通常采用手动勾勒的感兴趣区域，或使用现有图谱中的区域，将连接性分析的范围限制在相关区域。然而，大多数脑结构图谱以及手动勾勒的感兴趣区域并未考虑体素级的连接模式，因此可能并非解剖连接性分析的理想选择。我们在此提出一种基于连接性将脑划分为感兴趣区域的方法。我们将基于连接性的划分表述为一个图割问题，并使用一种新颖的多类霍普菲尔德网络算法进行近似求解。我们使用来自一项正在进行的精神分裂症研究的扩散张量成像数据展示了该方法的应用。与标准解剖图谱相比，基于连接性的图谱在区分精神分裂症患者与正常受试者时支持更好的分类性能。比较正常受试者和精神分裂症患者平均的连接模式，我们注意到这两种图谱之间存在显著的系统性差异。
