Selecting the number of discrete brain states in clustering-based analyses

Develop principled methods to determine the appropriate number of clusters or states in clustering-based discretizations of neuroimaging time series used to define discrete brain states, providing criteria that are statistically sound and tailored to the properties of neural dynamics.

Background

Clustering approaches are commonly used to define discrete brain states from continuous neural recordings, but many rely on pre-specifying the number of clusters. The choice of this number critically impacts downstream discrete-state modeling (e.g., Markov chains) and estimates of non-equilibrium measures.

Current practice often resorts to generic heuristics (e.g., the Silhouette criterion). A principled approach specific to neural time series could improve robustness, interpretability, and reproducibility of state-based analyses.