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    科学领域:

    • 神经科学是一个神经科学.
    • 图形理论 图形理论
    • 机器学习 机器学习

    背景情况:

    • 大脑网络分析通常使用图形理论,但往往忽略了边缘权重信息.
    • 目前用于表示大脑网络的现有方法在捕捉复杂关系方面存在局限性.

    研究的目的:

    • 提出一种新的脑网络表示方法,即使用边缘权重和层次结构的顺序模式树 (OPT).
    • 开发一种基于运输的最佳顺序模式树 (OT-OPT) 内核,用于测量大脑网络相似性.
    • 评估OT-OPT方法在对分类和回归任务的基准数据集的有效性.

    主要方法:

    • 使用顺序模式树 (OPT) 来表示大脑网络,其顺序边缘来自加权边缘关系.
    • 开发基于最佳运输 (OT) 的顺序模式树 (OT-OPT) 内核,以计算OPT之间的相似性.
    • 利用最佳运输距离来计算OT-OPT内核中的节点运输成本,证明其正确性.

    主要成果:

    • 拟议的顺序模式树 (OPT) 方法有效地利用大脑网络中的权重信息和层次节点关系.
    • 与现有的基于图形的方法相比,OT-OPT内核在测量大脑网络相似性方面表现出卓越的性能.
    • 对ADHD-200,ABIDE和ADNI数据集的实验结果显示,分类和回归任务的显著改善.

    结论:

    • 顺序模式树 (OPT) 为大脑网络表示和分析提供了一种强大的新方法.
    • OT-OPT 内核提供了一个强大的和有效的测量大脑网络相似性.
    • 拟议的方法推进了用于临床应用的神经成像分析的最新技术.