Exploring the link between EEG metastability and autistic traits in neurotypical adults
Poster No:
1666
Submission Type:
Abstract Submission
Authors:
Mebuki Izumiya1,2, Keiichi Kitajo2,1
Institutions:
1Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI, Okazaki, Aichi, Japan, 2National Institute for Physiological Sciences, Okazaki, Aichi, Japan
First Author:
Mebuki Izumiya
Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI|National Institute for Physiological Sciences
Okazaki, Aichi, Japan|Okazaki, Aichi, Japan
Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI|National Institute for Physiological Sciences
Okazaki, Aichi, Japan|Okazaki, Aichi, Japan
Co-Author:
Keiichi Kitajo
National Institute for Physiological Sciences|Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI
Okazaki, Aichi, Japan|Okazaki, Aichi, Japan
National Institute for Physiological Sciences|Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI
Okazaki, Aichi, Japan|Okazaki, Aichi, Japan
Introduction:
EEG phase synchronization networks mediate the integration and segregation of information among brain regions that are pertinent to the ongoing task [1]. The capacity of the phase synchronization network to smoothly transition between different network states is crucial for optimizing cognitive functions. It is suggested that this transient property allows the brain to effectively execute various cognitive processes [1]. We focus on metastable EEG phase synchronization networks and hypothesize that they are associated with individual psychological traits. "Metastability" refers to the temporal transitions occurring between states with relatively weak attraction within a dynamical system [2]. Far from being random activity, these transitions indicate the complexity and nonlinear nature of brain function [3]. The metastable nature of the brain may have implications for understanding the psychological traits of individuals [4, 5]. Therefore, the objective of this study is to establish metastability indices and investigate potential connections between these indices for EEG phase synchronization and psychological tendencies such as autistic spectrum disorder (ASD).
Methods:
The subjects were 88 neurotypical adults (24.4 ± 5.6 years old). Among them, 32 subjects went for the follow-up second recording with an average interval of 101 day s. Subjects sat at rest with their eyes closed. The EEG signals were recorded for 180 sec using an EEG amplifier (BrainAmp MR+, Brain Products GmbH, Gilching, Germany) and a 63-channel EEG cap (Easycap, EASYCAP GmbH, Herrsching, Germany) placed on the scalp in accordance with the International 10/10 system. The sampling rate was set to 1000 Hz, and the band-pass filter was 0.016-250 Hz. In EEG preprocessing,current source density (CSD) transformation was used to obtain more localized topography and to reduce the negative impact of spurious synchrony caused by volume conduction.
After extracting the time series, the temporal variation pattern of the phase synchronization network is calculated. Next, the metastability of the phase synchronization network between brain regions is compared between subjects using Synchrony Coalition Entropy (SCE) and Metastability Index (MSI). Here, SCE is an information-theoretic measure of the diversity of spatiotemporal synchrony patterns per electrode, whereas MSI is a measure of the fluctuations of the degree of global phase synchronization across brain regions [6, 7]. Therefore, we focused on the association between these indicides and autistic traits using the Autism Spectrum Quotient (AQ).
After extracting the time series, the temporal variation pattern of the phase synchronization network is calculated. Next, the metastability of the phase synchronization network between brain regions is compared between subjects using Synchrony Coalition Entropy (SCE) and Metastability Index (MSI). Here, SCE is an information-theoretic measure of the diversity of spatiotemporal synchrony patterns per electrode, whereas MSI is a measure of the fluctuations of the degree of global phase synchronization across brain regions [6, 7]. Therefore, we focused on the association between these indicides and autistic traits using the Autism Spectrum Quotient (AQ).
Results:
The test-retest reliability between twice measurements per subject was assessed using Spearman and interclass correlation coefficients (Figure 1). The results showed that both MSI and SCE exhibited test-retest reproducibility, with individual spatial patterns observed in the SCE values per channel. The association between the SCE values and AQ total scores was analyzed. We observed a positive correlation between the degree of metastability and autistic tendency in the delta (1-4 Hz) and theta (4-8 Hz) bands, although, we found that the alpha (8-14 Hz) and beta (14-30 Hz) bands showed different tendencies.
·Figure 1. Test-retest reliability of MSI and SCE. The figure displays individual MSI and SCE values measured on two separate days and correlation coefficients.
Conclusions:
These findings suggest that the SCE and MSI exhibit test-retest reliability. Notably, the spatial patterns of SCE for each electrode display individual characteristics. These results demonstrate that these metastability indices effectively capture individual brain dynamics robustly. Furthermore, the correlation results between SCE and AQ suggest a connection of metastability with ASD traits. It appears that the metastability of EEG phase synchronization networks is linked to psychological traits. In the future, this research is poised to shed light on the mechanisms that underlie intricate human cognitive processes, such as creative thinking, as well as contribute to our understanding of brain disorders.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis
Keywords:
ADULTS
Autism
Computational Neuroscience
Data analysis
Electroencephaolography (EEG)
1|2Indicates the priority used for review
Provide references using author date format
[2] Heitmann S. & Breakspear M. (2018), ‘Putting the “dynamic” back into dynamic functional connectivity’, Network Neuroscience, 2(2): 150–174.
[3] Deco G. et al. (2017), ‘The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core’, Scientific Reports, 7, 3095
[4] Gili T. et al. (2018), ‘Metastable states of multiscale brain networks are keys to crack the timing problem’, Frontiers Computational Neuroscience, 12:75.
[5] Sase T. & Kitajo K. (2021), ‘The metastable brain associated with autistic-like traits of typically developing individuals’, PLoS Computational Biology, 17(4): e1008929.
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