Zanesco Anthony P, Denkova Ekaterina, Jha Amishi P
Department of Psychology, University of Miami, United States.
Department of Psychology, University of Miami, United States.
Brain Cogn. 2021 Jun;150:105696. doi: 10.1016/j.bandc.2021.105696. Epub 2021 Mar 8.
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.
即使在没有外部刺激的情况下，思维也会在瞬间动态演变。自发思维模式与大脑内在活动随时间变化的波动之间的共变程度备受关注，但仍不清楚。我们对Portnova等人（2019年）最初报告的数据进行了新颖的分析，以研究脑电图微状态的内在动力学与使用阿姆斯特丹静息状态问卷（ARSQ）测量的自我报告思维之间的关联。相应地，微状态的毫秒级波动与思维的特定维度相关。我们通过荟萃分析将我们的结果与另一项研究的结果相结合，评估了这些发现的可靠性。重要的是，我们使用多变量方法扩展了这项研究，以评估个体的多维思维概况及其与连续微状态序列的联系。基于ARSQ评分的层次聚类确定了思维概况，并基于序列分析方法根据连续微状态的时间顺序进行区分。这些发现证明了评估自发思维对于理解大脑内在活动的相关性，以及序列分析在表征微状态动力学方面的新用途。从内部整合现象学观点对于理解内在大规模神经动力学的功能意义仍然至关重要。
