Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference
Detecting changes in dynamical structures in synchronous neural oscillations using probabilistic inference
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DOI：
10.1016/j.neuroimage.2022.119052
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发表时间：
2022-03
期刊：
影响因子：
5.7
通讯作者：
Hiroshi Yokoyama;K. Kitajo
中科院分区：
文献类型：
--
作者：
Hiroshi Yokoyama;K. Kitajo
最近的神经科学研究表明，认知功能和学习能力反映在大脑网络随时间演化的动态中。然而，尚未建立一种利用神经数据检测动态大脑结构变化的有效方法。为了解决这个问题，我们开发了一种新的基于模型的方法，通过将基于模型的网络估计与相位耦合振荡器模型和顺序贝叶斯推理相结合来检测动态网络结构中的变化点。通过将模型参数作为先验分布，应用贝叶斯推理可以通过使用信息理论标准将先验分布与后验分布进行比较来量化动态大脑网络的时间变化程度。为此，我们使用 Kullback-Leibler 散度作为此类变化的指标。为了验证我们的方法，我们将其应用于数值数据和脑电图数据。结果，我们证实，只有当动态网络结构发生变化时，Kullback-Leibler 散度才会增加。我们提出的方法成功地估计了数值和脑电图数据中的定向网络耦合和动态结构的变化点。这些结果表明我们提出的方法可以揭示动态大脑网络的神经基础。
Recent neuroscience studies have suggested that cognitive functions and learning capacity are reflected in the time-evolving dynamics of brain networks. However, an efficient method to detect changes in dynamical brain structures using neural data has yet to be established. To address this issue, we developed a new model-based approach to detect change points in dynamical network structures by combining the model-based network estimation with a phase-coupled oscillator model and sequential Bayesian inference. By giving the model parameter as the prior distribution, applying Bayesian inference allows the extent of temporal changes in dynamic brain networks to be quantified by comparing the prior distribution with the posterior distribution using information theoretical criteria. For this, we used the Kullback-Leibler divergence as an index of such changes. To validate our method, we applied it to numerical data and electroencephalography data. As a result, we confirmed that the Kullback-Leibler divergence only increased when changes in dynamical network structures occurred. Our proposed method successfully estimated both directed network couplings and change points of dynamical structures in the numerical and electroencephalography data. These results suggest that our proposed method can reveal the neural basis of dynamic brain networks.