Abstract

 Dynamic functional connectivity (dFC) analysis has revealed that functional connectivity fluctuates over short timescales, reflecting the intrinsic transitions of brain among multiple states. However, dFC data typically exhibit the characteristics of high dimensionality and noise, making it difficult to extract stable and accurate states. Furthermore, accurately identifying model order (i.e., number of states) is challenging due to lack of prior knowledge. To address the above issues, we propose a model order-free method for extracting stable states. Our method can simultaneously capture multi-scale state information and state stability. Furthermore, our method estimated the number of states adaptively based on data-driven methods. Based on synthetic data, we evaluated the effec tiveness of our method. The results show that, compared to traditional methods, our method not only accurately estimates the number of states but also extracts states with greater robustness and precision. Additionally, we evaluated the ef fectiveness and stability of the method using fMRI data from 602 healthy controls and 519 schizophrenia patients. Results demonstrate that our method exhibits sig nificant consistency among the states extracted by multiple runs. Moreover, we identified reliable biomarkers for schizophrenia. In conclusion, we propose a novel state extraction method that does not rely on predefined state numbers, while accurately and stably identifying states.