AUTHOR=Pieramico Giulia , Makkinayeri Saeed , Guidotti Roberto , Basti Alessio , Voso Domenico , Lucarelli Delia , D’Andrea Antea , L’Abbate Teresa , Romani Gian Luca , Pizzella Vittorio , Marzetti Laura 

TITLE=Robustness of brain state identification in synthetic phase-coupled neurodynamics using Hidden Markov Models

JOURNAL=Frontiers in Systems Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2025.1548437

DOI=10.3389/fnsys.2025.1548437

ISSN=1662-5137

ABSTRACT=Hidden Markov Models (HMMs) have emerged as a powerful tool for analyzing time series of neural activity. Gaussian HMMs and their time-resolved extension, Time-Delay Embedded HMMs (TDE-HMMs), have been instrumental in detecting discrete brain states in the form of temporal sequences of large-scale brain networks. To assess the performance of Gaussian HMMs and TDE-HMMs in this context, we conducted simulations that generated synthetic data representing multiple phase-coupled interactions between different cortical regions to mimic real neural data. Our study demonstrates that TDE-HMM performs better than Gaussian HMM in accurately detecting brain states from synthetic phase-coupled interaction data. Finally, for TDE-HMMs, we manipulated key parameters such as phase coupling variability, state duration, and influence of volume conduction effect to evaluate the models’ performance under varying conditions.