基于瞬时转变率模型的脑网络状态观测算法

针对K-means等聚类方法在脑网络状态观测中稳定性和鲁棒性较差的缺点,提出了一种基于瞬时转变率模型的脑网络状态观测算法。通过对状态转换临界点进行分组统计和分析,计算每一个临界时间点的状态瞬时转变率,在此基础上构建脑网络状态观测算法,并使用区间估计方法对状态转换的观测效果进行估计和验证。在脑网络数据库样本中的实验结果显示,与K-means等脑网络状态聚类观测算法相比,该算法在不同条件下的聚类稳定性更好,对样本差异的适应性更强,受参数选择的影响更小,能直观地观测到脑网络状态转换趋势。

A brain network state observation algorithm based on instantaneous transition rate model

Aiming at the poor stability and robustness in brain network state observation in clustering methods such as K-means, we propose a brain network state observation algorithm based on instantaneous transition rate model. The algorithm calculates the instantaneous state transition rate of each critical time point by group statistics and analysis. Based on this, we construct a brain network state observation model, and estimate and verify the state transition observation effect by the interval estimation method. Experimental results of the brain network database samples show that compared with the K-means and other brain network state clustering observation algorithms, the proposed algorithm has better cluster stability under different conditions and is more adaptable to individual sample differences. It is less affected by parameter selection and can visually observe the trend of brain network state transition.