Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models

Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data. We compared the GMM and HMM for various sampling frequencies, lengths of recording per participant, numbers of participants and numbers of independent component signals. We find that the HMM attains a better accuracy of estimating the hidden state than the GMM in a majority of cases. However, we also find that the accuracy of the GMM is comparable to that of the HMM under the condition that the sampling frequency is reasonably low (e.g., TR = 2.88 or 3.60 s) or the data are relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition.

Automated summary

Recent studies have suggested that summarizing brain activity into hidden states dynamics is a promising tool for understanding brain function. Hidden Markov models (HMMs) are commonly used to infer neural dynamics, but the impact of assuming Markovian structure in neural time series data needs further examination. Comparisons between GMM and HMM show that HMM generally provides better accuracy in estimating hidden states, but GMM can be a viable alternative under certain conditions like low sampling frequency or short data length.