Microsynt: Exploring the syntax of EEG microstates

Fiorenzo Artoni; Julien Maillard; Juliane Britz; Denis Brunet; Christopher Lysakowski; Martin R Tramèr; Christoph M Michel

doi:10.1016/j.neuroimage.2023.120196

Microsynt: Exploring the syntax of EEG microstates

Neuroimage. 2023 Aug 15:277:120196. doi: 10.1016/j.neuroimage.2023.120196. Epub 2023 Jun 5.

Authors

Fiorenzo Artoni¹, Julien Maillard², Juliane Britz³, Denis Brunet⁴, Christopher Lysakowski², Martin R Tramèr², Christoph M Michel⁴

Affiliations

¹ Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; CIBM Center for Biomedical Imaging, Switzerland. Electronic address: fiorenzo.artoni@unige.ch.
² Division of Anesthesiology, Department of Anesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, and University of Geneva, Geneva, Switzerland.
³ CIBM Center for Biomedical Imaging, Switzerland; Department of Psychology, University of Fribourg, Fribourg, Switzerland.
⁴ Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; CIBM Center for Biomedical Imaging, Switzerland.

PMID: 37286153
DOI: 10.1016/j.neuroimage.2023.120196

Abstract

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.

Keywords: Anesthesia; Complexity; EEG; Entropy; Microstates; Syntax.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain
Brain Mapping
Electroencephalography*
Humans
Propofol*
Wakefulness

Substances

Propofol