阿尔茨海默病中基于血流的脑连接网络测量
FLOW-BASED NETWORK MEASURES OF BRAIN CONNECTIVITY IN ALZHEIMER'S DISEASE.
作者信息
Prasad Gautam, Joshi Shantanu H, Nir Talia M, Toga Arthur W, Thompson Paul M
机构信息
Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2013;2013:258-261. doi: 10.1109/ISBI.2013.6556461.
We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A flow network is first created using a lattice graph by connecting all lattice points (voxel centers) to all their neighbors by edges. Edge weights are based on the orientation distribution function (ODF) value in the direction of the edge. The maximum-flow is computed based on this flow graph using the flow or the capacity between each region of interest (ROI) pair by following the connected tractography fibers projected onto the flow graph edges. Network measures such as global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity are computed from the flow connectivity matrix. We applied our method to diffusion-weighted images (DWIs) from 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD) and segmented co-registered anatomical MRIs into cortical regions. Experimental results showed better performance compared to the standard fiber-counting methods when distinguishing Alzheimer's disease from normal aging.
我们提出了一种用于构建脑结构连接模型的基于流的新方法。该方法使用了一种改进的最大流算法，该算法对扩散数据中的噪声具有鲁棒性，并由大脑中生物学上可行的通路和结构引导。首先使用晶格图创建一个流网络，通过边将所有晶格点（体素中心）与其所有邻居相连。边权重基于边方向上的方向分布函数（ODF）值。基于此流图，通过沿着投影到流图边上的连接纤维束追踪，计算每个感兴趣区域（ROI）对之间的流或容量，从而计算最大流。从流连接矩阵计算诸如全局效率、传递性、路径长度、平均度、密度、模块化、小世界和 assortativity 等网络度量。我们将我们的方法应用于 110 名受试者的扩散加权图像（DWI）（28 名正常老年人、56 名早期和 11 名晚期轻度认知障碍患者以及 15 名患有阿尔茨海默病的患者），并将共同配准的解剖学 MRI 分割为皮质区域。实验结果表明，在区分阿尔茨海默病与正常衰老方面，与标准纤维计数方法相比，该方法具有更好的性能。
Zotero 插件