大脑连接模式来源于动态大脑功能网络与衰老相关的变化,以及它们作为大脑年龄分类特征的潜力
Hongfang Han1, Jiuchuan Jiang2, Lingyun Gu1
1Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, People's Republic of China.
Journal of neural engineering
|March 13, 2024
概括
这项研究引入了一种分析动态大脑功能网络 (BFNs) 的新方法,并揭示了大脑结构中与年龄相关的显著变化. 这些发现有助于量化评估大脑衰老.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 网络科学 网络科学
背景情况:
- 大脑功能网络 (BFNs) 对于理解大脑衰老和神经退行性疾病至关重要.
- 研究大脑衰老中的动态BFN仍然是一个挑战.
研究的目的:
- 开发一种使用动态BFNs探索大脑衰老模式的新方法.
- 从静止状态的功能磁共振成像 (fMRI) 数据构建动态的BFNs.
主要方法:
- 为动态 BFN 构建提出了一个动态的滑窗非负块对角表示 (dNBDR).
- 利用动态的BFN测量,用于群体级别的年龄相关差异和个体级别的大脑年龄分类.
- 用皮尔森相关性方法将dNBDR与滑动时间窗口进行比较.
主要成果:
- 与皮尔森相关性方法相比,dNBDR显示出更高的动态网络结构质量.
- 在整个生命周期中确定了动态BFN结构的与年龄相关的显著变化.
- 节点的灵活性和整合系数随着年龄的增长而增加;招聘系数下降.
- 在使用动态BFN特征对三个大脑年龄组进行分类时,获得了78.7%的准确性.
结论:
- 动态BFN测量,特别是动态社区结构指标,对于定量评估大脑衰老至关重要.
- 开发的dNBDR方法为分析衰老研究中的动态大脑网络提供了一个强大的方法.
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