Metastable EEG phase synchronization associated with autistic traits
Poster No:
297
Submission Type:
Abstract Submission
Authors:
Mebuki Izumiya1,2, Yuka Okazaki2,1, Keiichi Kitajo2,1
Institutions:
1Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI, Aichi, Japan, 2Division of Neural Dynamics, National Institute for Physiological Sciences, Aichi, Japan
First Author:
Mebuki Izumiya
Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI|Division of Neural Dynamics, National Institute for Physiological Sciences
Aichi, Japan|Aichi, Japan
Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI|Division of Neural Dynamics, National Institute for Physiological Sciences
Aichi, Japan|Aichi, Japan
Co-Author(s):
Yuka Okazaki
Division of Neural Dynamics, National Institute for Physiological Sciences|Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI
Aichi, Japan|Aichi, Japan
Division of Neural Dynamics, National Institute for Physiological Sciences|Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI
Aichi, Japan|Aichi, Japan
Keiichi Kitajo
Division of Neural Dynamics, National Institute for Physiological Sciences|Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI
Aichi, Japan|Aichi, Japan
Division of Neural Dynamics, National Institute for Physiological Sciences|Department of Physiological Sciences, The Graduate University for Advanced Studies, SOKENDAI
Aichi, Japan|Aichi, Japan
Introduction:
EEG phase synchronization networks are critical for the integration and segregation of information among brain regions, underpinning efficient cognitive processes [Tognoli, 2014]. The brain's ability to flexibly transition between stable and attracting states is thought to reflect its metastable dynamics, which are essential for adaptive cognitive functions [Tognoli, 2014]. Metastability, characterized by temporal transitions between weakly attracting states in a dynamical system [Heitmann, 2018] including the brain [Cabral, 2022][Deco, 2017], has been associated with individual psychological traits [Sase, 2021], including those associated with autism spectrum disorder (ASD). However, prior research has predominantly focused on static measures of EEG phase synchronization such as average synchronization strength and network topology, leaving a gap in understanding the specific role of synchrony metastability in psychological traits. To address the gap, this study examines the association between EEG phase synchronization network metastability and autistic traits in neurotypical adults.
Methods:
We analyzed 63-ch EEG data from 88 neurotypical adults (24.4 ± 5.6 years old, 45 female) during 180 seconds of eyes-closed resting state [Sase, 2021]. Autism-Spectrum Quotient（AQ）was assessed for each individual. Preprocessing included independent component analysis for artifact removal, current source density transformation to reduce volume conduction effects, and the wavelet transform to extract instantaneous phase values (1–47 Hz, 1 Hz steps). Complementary metastability measures were calculated: synchrony coalition entropy (SCE), which captures the Shannon entropy of phase difference patterns, and metastability index (MSI), defined as the variance of the Kuramoto order parameter across time [Kuramoto, 1984]. We extended these measures, which were originally proposed as key indicators of metastability in coupled oscillator models [Shanahan, 2010], were later adapted to human EEG networks [Schartner, 2015].
Results:
The test-retest reliability analysis demonstrated that MSI and SCE were robust measures across most frequencies, except for MSI in the gamma band. The Spearman correlation between MSI and AQ showed a positive correlation with the "social skill" subscore at 17–19 Hz. Using cluster-based correlation test [Maris, 2007], SCE analysis revealed spatial and frequency relationships with AQ subscores: a negative correlation with "attention switching" in a network involving the right parietal region at 18–24 Hz, and positive correlation with "communication" in a network including the occipital area at 4–8 Hz.
·Cluster-based correlation of SCE - AQ “attention switching”: topography shows the coefficient value detected as significant cluster correlations and scatter shows a link between SCE and AQ subscore.
Conclusions:
This study highlights the importance of metastable dynamics in brain networks and their role in shaping individual psychological traits. The positive correlation between MSI and AQ "social skill" suggests the association between global metastability and social aspects. The negative correlation in the SCE and AQ "attention switching" link is consistent with the delta-alpha phase-amplitude coupling results of a previous study [Sase, 2021]. In contrast, a positive correlation between SCE and AQ "communication" suggests that metastable synchrony networks involving occipital brain areas in the theta band play a role in social communication. The occipital networks, traditionally linked to visual processing, may support social communication by integrating visual social cues, such as facial expressions, with higher-order cognitive processes. These findings suggest that EEG phase synchronization network metastability is a robust feature in capturing individual psychological traits. Specifically, these results imply that the metastable property of large-scale brain networks facilitates flexible switching, which in turn influences brain functions related to autistic traits. Investigating metastable dynamics may further clarify the neural underpinnings of ASD and other disorders involving network abnormalities, potentially guiding new therapeutic strategies.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
Task-Independent and Resting-State Analysis 2
Keywords:
ADULTS
Autism
Computational Neuroscience
Data analysis
Electroencephaolography (EEG)
1|2Indicates the priority used for review
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Provide references using APA citation style.
Deco, G. et al. (2017). The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Scientific Reports 7, 3095.
Heitmann, S. et al. (2018). Putting the “dynamic” back into dynamic functional connectivity. Network Neuroscience, 02(02): 150–174.
Kuramoto, Y. (1984). Chemical Oscillations, Waves, and Turbulence. New York, NY: Springer-Verlag.
Maris, E. et al. (2007). Nonparametric statistical testing of EEG- and MEG-data. Journal of Neuroscience Methods. 164(1):177-90.
Sase, T. et al. (2021). The metastable brain associated with autistic-like traits of typically developing individuals. PLoS Computational Biology, 17(4): e1008929.
Schartner, M. et al. (2015). Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLoS ONE, 10(8): e0133532.
Shanahan, M. (2010). Metastable chimera states in community-structured oscillator networks. Chaos.
Tognoli, E. et al. (2014). The metastable brain. Neuron, 8; 81(1): 35-48.