邻居结构引导的大脑功能网络对轻度认知障碍的估计识别识别
Lizhong Liang1, Zijian Zhu2, Hui Su3
1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
PeerJ
|August 5, 2024
概括
我们介绍了一种新的方法来构建大脑功能网络 (BFNs),使用K-最近邻居 (KNN) 和Pearson.
科学领域:
- 神经成像和计算神经科学.
- 开发用于大脑功能网络构建的先进分析技术.
背景情况:
- 功能磁共振成像 (fMRI) 和皮尔森相关性 (PC) 对于大脑疾病诊断至关重要,构建大脑功能网络 (BFNs).
- 使用PC的现有方法通常会导致密集的BFN,违反生物原理,并且值或L1-规范规范化忽视空间信息.
- 距离较近的感兴趣区域 (ROI) 通常由于布线成本原则而表现出更高的连接性.
研究的目的:
- 提出一种新的社区结构引导方法,用于估计稀疏和生物可信的BFNs.
- 将空间邻里信息纳入BFN构建,解决当前基于PC的方法的局限性.
- 评估拟议方法在区分患有轻度认知障碍 (MCI) 个体与健康对照的有效性.
主要方法:
- 计算ROI之间的欧几里德距离并对它们进行排序.
- 使用K-最近邻居 (KNN) 来识别每个ROI的K最近邻居.
- 基于这些K邻关系构建一个全球拓邻矩阵,然后使用PC计算连接ROI的相关性,生成BFN.
主要成果:
- 与基线方法相比,拟议的社区结构引导方法 (K-最近邻居-皮尔森相关性,K-PC) 在区分MCI与健康个体方面取得了更好的分类性能.
- K-PC在常用的深度学习时间序列方法上表现出了优势.
- 该方法通过考虑空间近距离,产生更稀疏和可能更具有生物相关性的BFNs.
结论:
- 社区结构引导的BFN估计方法有效地提高了MCI等神经疾病的分类准确性.
- 整合空间邻近信息增强了fMRI衍生出的BFNs的生物相关性和诊断效用.
- 这种方法为用于大脑网络分析的现有方法 (包括深度学习) 提供了一个有希望的替代方案.
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