Modelling state‐transition dynamics in resting‐state brain signals by the hidden Markov and Gaussian mixture models. (22nd July 2021)

• Record Type: Journal Article
• Authors: Ezaki, Takahiro; Himeno, Yu; Watanabe, Takamitsu; Masuda, Naoki
• Is Part Of: European journal of neuroscience. Volume 54:Number 4(2021)
• Page Start: 5404
• Page End: 5416
• Publication Date: 2021-07-22
• Subjects: Gaussian mixture model -- hidden Markov model -- resting‐state fMRI -- state‐transition dynamics; Nervous system -- Periodicals; 612.8
• DOI: 10.1111/ejn.15386
• Languages: English
• ISSNs: 0953-816X

Abstract

Abstract: 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. Abstract : The hidden Markov model (HMM) is commonly used for studying state‐transition dynamics of neural data. We have compared the Gaussian mixture model (GMM), which does not assume any temporal structure, with the HMM. We have found that, while the HMM performs better than the GMM in a majority of cases, the GMM performs comparably well when the data are short or the temporal resolution of the data is low.