• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Multimodal Brain Image Analysis >Identification of Cortical Landmarks Based on Consistent Connectivity to Subcortical Structures
【24h】

Identification of Cortical Landmarks Based on Consistent Connectivity to Subcortical Structures

机译：基于与皮质下结构的一致连通性的皮质地标识别

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Quantitative assessment of structural connectivities between cortical and subcortical regions has been of increasing interest in recent years. This paper proposes an algorithmic pipeline for identification of reliable cortical landmarks based on the consistent structural connectivity between cortical and subcortical regions. First, twelve subcortical regions are segmented from MRI data, and cortical surface and white matter fibers are reconstructed and tracked from magnetic resonance diffusion tensor imaging (DTI) data. Second, given that subcortical structures are relatively consistent across individual subjects, the structural connectivity from cortical to subcortical regions is extracted as the connectional attribute for each cortical region. Third, the cortex is segmented into different regions based on their cortico-subcortical connection attributes, and regions with the most consistent connectivity patterns across different subjects are selected as cortical landmarks. Experimental results from eight healthy subjects show that our approaches can identify 22 reliable cortical landmarks, a portion of which are validated via task-based fMRI data.
机译：近年来，对皮质和皮质下区域之间的结构连接性进行定量评估的兴趣日益增加。本文提出了一种基于皮质与皮质下区域之间一致的结构连通性的可靠皮质标志物识别算法。首先，从MRI数据中分割出十二个皮质下区域，并从磁共振扩散张量成像（DTI）数据重建并跟踪皮质表面和白质纤维。其次，鉴于皮质下结构在各个受试者之间相对一致，因此提取从皮质到皮质下区域的结构连通性作为每个皮质区域的连接属性。第三，根据皮层-皮层下的连接属性将皮层划分为不同的区域，并选择跨不同对象的连接模式最一致的区域作为皮层界标。来自八个健康受试者的实验结果表明，我们的方法可以识别22个可靠的皮质标志物，其中一部分已通过基于任务的fMRI数据进行了验证。

著录项

  • 来源
    《Multimodal Brain Image Analysis》|2011年|p.68-75|共8页
  • 会议地点 Toronto(CA);Toronto(CA);Toronto(CA);Toronto(CA)
  • 作者

    Degang Zhang; Lei Guo; Dajiang Zhu; Tuo Zhang; Xintao Hu; Kaiming Li; Xi Jiang; Hanbo Chen; Jinglei Lv; Fan Deng; Qun Zhao;

  • 作者单位

    School of Automation, Northwestern Polytechnical University, Xi'an, China,Department of Physics and Bioimaging Research Center, The University of Georgia, Athens, GA, United States;

    School of Automation, Northwestern Polytechnical University, Xi'an, China;

    Department of Computer Science and Bioimaging Research Center,The University of Georgia, Athens, GA;

    School of Automation, Northwestern Polytechnical University, Xi'an, China;

    School of Automation, Northwestern Polytechnical University, Xi'an, China;

    School of Automation, Northwestern Polytechnical University, Xi'an, China,Department of Computer Science and Bioimaging Research Center,The University of Georgia, Athens, GA;

    Department of Computer Science and Bioimaging Research Center,The University of Georgia, Athens, GA;

    Department of Computer Science and Bioimaging Research Center,The University of Georgia, Athens, GA;

    School of Automation, Northwestern Polytechnical University, Xi'an, China;

    Department of Computer Science and Bioimaging Research Center,The University of Georgia, Athens, GA;

    Department of Physics and Bioimaging Research Center, The University of Georgia, Athens, GA, United States;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 医用物理学;脑部疾病;
  • 关键词

    cortical parcellation; subcortical regions; connectivity pattern;

    机译：皮质细胞分裂皮质下区域;连接模式;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号