智能科学与技术学报 ›› 2019, Vol. 1 ›› Issue (4): 369-378.doi: 10.11959/j.issn.2096-6652.201941

• 学术论文 • 上一篇    下一篇

阿尔兹海默症脑网络演化建模

倪冰洁1,2,李炜1,2,陈曦1,2()   

  1. 1 华中科技大学人工智能与自动化学院,湖北 武汉 430074
    2 图像信息处理与智能控制教育部重点实验室,湖北 武汉 430074
  • 修回日期:2019-11-20 出版日期:2019-12-20 发布日期:2020-02-29
  • 作者简介:倪冰洁(1998- ),女,华中科技大学人工智能与自动化学院硕士生,主要研究方向为医学图像处理、模式识别|李炜(1975- ),女,博士,华中科技大学人工智能与自动化学院教授,主要研究方向为神经影像分析、模式识别|陈曦(1974- ),男,博士,华中科技大学人工智能与自动化学院教授,主要研究方向为复杂系统的建模与仿真
  • 基金资助:
    国家自然科学基金资助项目(61473131)

Brain network evolution modeling based on Alzheimer’s disease

Bingjie NI1,2,Wei LI1,2,Xi CHEN1,2()   

  1. 1 School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
    2 The Key Laboratory of Image Information Processing and Intelligent Control,Ministry of Education,Huazhong University of Science and Technology,Wuhan 430074,China
  • Revised:2019-11-20 Online:2019-12-20 Published:2020-02-29
  • Supported by:
    The National Natural Science Foundation of China(61473131)

摘要:

阿尔兹海默症作为一种常见且多发的疾病,严重影响着中老年群体的生活质量与水平,深入理解阿尔兹海默症的发病机制和发展进程对于研发相关预防措施及治疗手段非常重要。现有研究大多基于病变前后大脑静态特性的对照分析,忽略了其病变过程中的动态演化机制。基于功能磁共振成像数据构建大脑功能网络,对其病变过程纵向发展中神经系统动态演化过程进行深入研究,提出了一种基于脑网络水平的阿尔兹海默症病变过程的动态演化模型,以模拟大脑在神经系统病变过程中的动态演变及可塑过程,最后,从多个角度对演化结果进行评估,验证了模型的合理性,为阿尔兹海默症的早期诊断、功能评估及预测提供了新思路。

关键词: 阿尔兹海默症, 复杂网络, 脑网络, 网络建模, 动态演化

Abstract:

As a common and frequently-occurring disease,Alzheimer’s disease seriously affects the quality and level of life of the middle-aged and elderly.An in-depth understanding of the pathogenesis and progression of Alzheimer’s disease is important for the development of prevention and treatment options.Most of the existing studies were based on the static property analysis of the brain network before and after the lesion,and dynamic evolutionary mechanism of the lesion process was usually neglected.A dynamic evolution model was proposed based on the level of brain networks of Alzheimer’s disease by analyzing the longitudinal development to simulate plastic changes of the process.Finally,the rationality of the model was verified by evaluating the evolutionary results from multiple perspectives.The study provides a new idea for early diagnosis,functional evaluation and prediction of Alzheimer’s disease.

Key words: Alzheimer’s disease, complex network, brain network, network modeling, dynamic evolution

中图分类号: 

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