Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; CIBM Center for Biomedical Imaging, Switzerland.
Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, and University of Geneva, Geneva, Switzerland.
Neuroimage. 2023 Aug 15;277:120196. doi: 10.1016/j.neuroimage.2023.120196. Epub 2023 Jun 5.
Microstates represent electroencephalographic (EEG) activity as a sequence of switching, transient, metastable states. Growing evidence suggests the useful information on brain states is to be found in the higher-order temporal structure of these sequences. Instead of focusing on transition probabilities, here we propose "Microsynt", a method designed to highlight higher-order interactions that form a preliminary step towards understanding the syntax of microstate sequences of any length and complexity. Microsynt extracts an optimal vocabulary of "words" based on the length and complexity of the full sequence of microstates. Words are then sorted into classes of entropy and their representativeness within each class is statistically compared with surrogate and theoretical vocabularies. We applied the method on EEG data previously collected from healthy subjects undergoing propofol anesthesia, and compared their "fully awake" (BASE) and "fully unconscious" (DEEP) conditions. Results show that microstate sequences, even at rest, are not random but tend to behave in a more predictable way, favoring simpler sub-sequences, or "words". Contrary to high-entropy words, lowest-entropy binary microstate loops are prominent and favored on average 10 times more than what is theoretically expected. Progressing from BASE to DEEP, the representation of low-entropy words increases while that of high-entropy words decreases. During the awake state, sequences of microstates tend to be attracted towards "A - B - C" microstate hubs, and most prominently A - B binary loops. Conversely, with full unconsciousness, sequences of microstates are attracted towards "C - D - E" hubs, and most prominently C - E binary loops, confirming the putative relation of microstates A and B to externally-oriented cognitive processes and microstate C and E to internally-generated mental activity. Microsynt can form a syntactic signature of microstate sequences that can be used to reliably differentiate two or more conditions.
微状态将脑电图 (EEG) 活动表示为一系列切换、瞬态、亚稳态。越来越多的证据表明，大脑状态的有用信息存在于这些序列的更高阶时间结构中。在这里，我们提出了“Microsynt”，一种方法，不是专注于转移概率，而是设计用于突出更高阶的相互作用，为理解任何长度和复杂度的微状态序列的语法迈出了初步的一步。Microsynt 根据微状态序列的长度和复杂度提取一个“单词”的最佳词汇。然后将单词按熵分类，并统计比较它们在每个类中的代表性与替代和理论词汇。我们将该方法应用于以前从接受异丙酚麻醉的健康受试者中收集的 EEG 数据，并比较了他们的“完全清醒”（BASE）和“完全无意识”（DEEP）状态。结果表明，微状态序列即使在休息时也不是随机的，而是倾向于以更可预测的方式表现，更倾向于简单的子序列或“单词”。与高熵单词相反，最低熵二进制微状态循环是突出的，平均比理论上预期的多 10 倍。从 BASE 到 DEEP，低熵单词的表示增加，而高熵单词的表示减少。在清醒状态下，微状态序列倾向于被“ A - B - C”微状态枢纽吸引，并且 A - B 二进制循环最为突出。相反，在完全无意识的情况下，微状态序列被“ C - D - E”枢纽吸引，并且 C - E 二进制循环最为突出，这证实了微状态 A 和 B 与外向认知过程的假定关系，以及微状态 C 和 E 与内部生成的心理活动的假定关系。Microsynt 可以形成微状态序列的句法特征，可用于可靠地区分两种或多种状态。
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