Abstract:

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.

Key words: state observation, dynamic functional connectivity, high dimensional clustering, resting state fMRI