Brain network evolution modeling based on Alzheimer’s disease

Bingjie NI; Wei LI; Xi CHEN

doi:10.11959/j.issn.2096-6652.201941

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Brain network evolution modeling based on Alzheimer’s disease

Regular Papers | 更新时间：2024-06-05

- Brain network evolution modeling based on Alzheimer’s disease
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 4, Pages: 369-378(2019)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.201941
  CLC： TP29
- Published Online：2019-12，
  
  Published：20 December 2019
- 稿件说明：
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Bingjie NI, Wei LI, Xi CHEN. Brain network evolution modeling based on Alzheimer’s disease[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 369-378.
DOI：

Bingjie NI, Wei LI, Xi CHEN. Brain network evolution modeling based on Alzheimer’s disease[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 369-378. DOI： 10.11959/j.issn.2096-6652.201941.

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。

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.

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Keywords

references

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