论文标题
通过隐藏的马尔可夫和高斯混合模型在静止状态大脑信号中建模状态转变动力学
Modeling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models
论文作者
论文摘要
最近的研究提出，可以将大脑活动总结为相对少数隐藏状态的动态，而这种方法是揭示大脑功能的有前途的工具。隐藏的马尔可夫模型（HMM）是一种普遍的方法，用于推断离散大脑状态之间的这种神经动力学。但是，假设马尔可夫结构在神经时间序列数据中的影响尚未得到充分检查。在这里，为了解决这种情况并检查HMM的性能，我们将模型与高斯混合模型（GMM）进行了比较，而高斯混合模型（GMM）没有时间正则化，因此，通过将两个模型应用于经验静止静止的功能性磁共振成像（FMRI）数据而产生的合成时间序列，从而将两个模型比HMM进行了比较。我们比较了各种采样频率，每个参与者的记录长度，参与者数量和独立组件信号的数量的GMM和HMM。我们发现，在大多数情况下，HMM比GMM具有更好的估计隐藏状态的准确性。但是，我们还发现，在采样频率相当低（例如TR = 2.88或3.60 s）或数据相对较短的情况下，GMM的准确性与HMM的准确性相当。这些结果表明，在这种情况下，GMM可以是研究隐藏状态动力学的可行替代方法。
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 is relatively short. These results suggest that the GMM can be a viable alternative to the HMM for investigating hidden-state dynamics under this condition.
