Thought dynamically evolves from one moment to the next even in the absence of external stimulation. The extent to which patterns of spontaneous thought covary with time-varying fluctuations in intrinsic brain activity is of great interest but remains unknown. We conducted novel analyses of data originally reported by Portnova et al. (2019) to examine associations between the intrinsic dynamics of EEG microstates and self-reported thought measured using the Amsterdam Resting-State Questionnaire (ARSQ). Accordingly, the millisecond fluctuations of microstates were associated with specific dimensions of thought. We evaluated the reliability of these findings by combining our results with those of another study using meta-analysis. Importantly, we extended this investigation using multivariate methods to evaluate multidimensional thought profiles of individuals and their links to sequences of successive microstates. Thought profiles were identified based on hierarchical clustering of ARSQ ratings and were distinguished in terms of the temporal ordering of successive microstates based on sequence analytic methods. These findings demonstrate the relevance of assessing spontaneous thought for understanding intrinsic brain activity and the novel use of sequence analysis for characterizing microstate dynamics. Integrating the phenomenological view from within remains crucial for understanding the functional significance of intrinsic large-scale neurodynamics.

即使没有外部刺激，思考也会动态地从一刻发展到下一刻。自发性思维模式随内在大脑活动的时变波动变化的程度引起人们极大的兴趣，但仍然未知。我们对Portnova等人最初报道的数据进行了新颖的分析。（2019）研究了使用阿姆斯特丹静息状态问卷（ARSQ）测得的脑电微状态的内在动力学与自我报告的思想之间的关联。因此，微状态的毫秒波动与思想的特定维度相关。通过将我们的结果与另一项使用荟萃分析的研究相结合，我们评估了这些发现的可靠性。重要的，我们使用多元方法扩展了这项研究，以评估个人的多维思维特征及其与连续微状态序列的联系。思想档案是根据ARSQ等级的层次聚类确定的，并根据序列分析方法根据连续微状态的时间顺序进行了区分。这些发现证明了评估自发性思维以了解内在的大脑活动的相关性以及序列分析用于表征微状态动力学的新颖用途。整合现象学 思想档案是根据ARSQ等级的层次聚类确定的，并根据序列分析方法根据连续微状态的时间顺序进行了区分。这些发现证明了评估自发性思维以了解内在的大脑活动的相关性以及序列分析用于表征微状态动力学的新颖用途。整合现象学 思想档案是根据ARSQ等级的层次聚类确定的，并根据序列分析方法根据连续微状态的时间顺序进行了区分。这些发现证明了评估自发性思维以了解内在的大脑活动的相关性以及序列分析用于表征微状态动力学的新颖用途。整合现象学内部视图对于理解内在的大规模神经动力学的功能意义仍然至关重要。