PDF(Pubmed)
Abstract：
The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models\' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.
摘要：
即使在没有外部刺激的情况下，人脑也会表现出时空复杂的活动，循环通过称为大脑状态的重复活动模式。到目前为止,大脑状态分析主要限于单峰神经成像数据集，导致对状态的定义有限，并且对从不同模态识别的状态之间的空间和时间关系的理解很差。这里,我们将隐马尔可夫模型（HMM）应用于并发脑电图功能磁共振成像（EEG-fMRI）睁眼（EO）和闭眼（EC）静息状态数据，分别对EEG和fMRI数据进行训练模型，并评估了模型区分两种静止条件之间动态的能力。此外,我们采用一般的线性模型方法来识别EEG定义状态的BOLD相关性,以研究fMRI数据是否可用于改善EEG状态的空间定义.最后,我们对状态时间过程进行了基于滑动窗口的分析，以识别时间动态中较慢的变化，然后将这些时间课程与模式相关联。我们发现，与EO休息相比，这两个模型都可以识别EC休息期间的预期变化，通过fMRI模型识别视觉和注意力静息状态网络的活动和功能连通性的变化，而EEG模型正确地识别了闭眼时alpha的典型增加。此外,通过使用功能磁共振成像数据，可以推断EEG状态的空间特性，产生类似于规范α-BOLD相关性的BOLD相关图。最后,滑动窗口分析揭示了来自两个模型的状态的独特分数占用动力学，选择的状态显示出跨模态的强时间相关性。总的来说,这项研究强调了使用HMM进行脑状态分析的功效，确认多模态数据可用于提供更深入的状态定义，并证明跨不同模态定义的状态显示出相似的时间动态。
