中国神经再生研究(英文版)

中国神经再生研究(英文版) ›› 2020, Vol. 15 ›› Issue (2): 285-292.doi: 10.4103/1673-5374.265566

• 原著:退行性病与再生 • 上一篇    下一篇

功能磁共振检测分析阿尔茨海默病患者静息态脑白质和灰质区域脑功能连接模式改变的意义

  

  • 出版日期:2020-02-15 发布日期:2020-05-25
  • 基金资助:
    国家自然科学基金资助项目（61401308,61572063）; 中国北京自然科学基金（L172055）; 北京市科学技术委员会中国研究基金（Z171100000417004）; 中国博士后基金（2018M631755, 2018M631755）; 中国中西部河北大学综合实力提升专项基金（801260201011）;高层次人才资助项目 - 河北省选择性博士后研究项目基金(B2018003002）

Alteration of functional connectivity in patients with Alzheimer’s disease revealed by resting-state functional magnetic resonance imaging

Jie Zhao1, 2, 3, Yu-Hang Du1, Xue-Tong Ding1, Xue-Hu Wang1, 2, 3, Guo-Zun Men4   

  1. 1 School of Electronic and Information Engineering, Hebei University, Baoding, Hebei Province, China
    2 Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, Hebei Province, China
    3 Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, Hebei Province, China
    4 School of Economics, Hebei University, Baoding, Hebei Province, China
  • Online:2020-02-15 Published:2020-05-25
  • Contact: Xue-Hu Wang, PhD,wangxuehu_tougao@163.com.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China, No. 61401308, 61572063 (both to XHW); the Natural Science Foundation of Beijing of China, No. L172055 (to XHW); the Beijing Municipal Science & Technology Commission Research Fund of China, No. Z171100000417004 (to XHW); the China Postdoctoral Fund, No. 2018M631755 (to XHW); the Special Fund for Improving Comprehensive Strength of Hebei University in the Midwest of China, No. 801260201011 (to XHW); the High-Level Talent Funding Project — Selective Post-doctoral Research Project Fund of Hebei Province of China, No. B2018003002 (to XHW).

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摘要：

阿尔茨海默病患者主要症状为认知功能障碍，目前的诊断方法主要基于脑结构的变化。脑功能连接可反映不相邻脑区之间功能活动的同步性，其变化比脑结构的变化出现更早，因此实验采用功能磁共振成像技术来检测静息状态脑功能连接变化，希望对阿尔茨海默病预测提供参考证据。(1)为了验证阿尔茨海默病患者的某些脑区是否存在某种功能连接模式，这种连接模式是否随着疾病的严重程度而变化，实验使用功能磁共振成像来检测正常认知，轻度认知障碍早期，轻度认知障碍晚期和阿尔茨海默病患者的血氧水平依赖性信号时间序列上的相关性，通过分析平均相关系数，发现阿尔茨海默病患者在静息状态下白质与灰质之间存在某种功能连接模式，这种连接模式随着病情的严重程度而变化，这种功能连接的变化体现在相关性上，表现在部分区域的相关性增强或减弱。(2)研究重点分析了9个白质区域和对应的灰质区域的相关系数，发现5个区域在正常认知中的相关系数为0.3-0.5，在发生阿尔茨海默病时相关系数为0-0.2；其他4个地区的相关系数范围增加到0.45-0.7。(3)阿尔茨海默病患者在静息态下某些白质和灰质区域存在一些特定的功能连接模式；(4)研究推测这种脑白质和灰质区域连接模式的变化可以用来预测阿尔茨海默病，但具体连接模式的详细信息还需未来进一步研究。研究中所有患者数据均来自The Image＆Data Archive数据库的Alzheimer's Disease Neuroimaging Initiative库，不涉及伦理道德和知情问题。

orcid: 0000-0001-9550-4073 (Xue-Hu Wang)

关键词: 阿尔茨海默病, 功能磁共振成像, 白质, 灰质, 静息状态, 功能连接模式, 相关系数, 血氧依赖水平信号

Abstract: The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer’s disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer’s disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer’s disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3–0.5 in patients with normal cognition and 0–0.2 in those developing Alzheimer’s disease. Moreover, in the other four regions, the range increased to 0.45–0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer’s disease; however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer’s Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

Key words: Alzheimer’s disease, blood oxygen level-dependent signal, correlation coefficient, functional connectivity pattern, functional magnetic resonance imaging, gray matter, resting state, white matter