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Updated: Feb 18, 2026

EEG Mu Rhythm in Typical and Atypical Development
Published on: April 9, 2014
Resting-state EEG microstates across a dimensional spectrum of autistic traits: From typical development to diagnosed
Tania Karina Garcia Vite1, Achilleas Pavlou2, Christina Pari3
1CYENS Centre of Excellence, Lellou Demetriades, Plateia Dimarchou 1, Nicosia 1016, Cyprus; Department of Psychology, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.
Abstract:
Autism spectrum disorder (ASD) has been linked to atypical large-scale brain dynamics, but it is unclear how these alterations extend across the broader autism phenotype. We applied a seven-class resting-state EEG microstate model (A-G) to adults with clinical ASD and to typically developing adults with high (TD-High) and low (TD-Low) autistic traits, quantified with the Autism-Spectrum Quotient. We compared temporal parameters, spatial coverage, explained variance, and both observed and chance-corrected transition probabilities. Across all microstates, the ASD group showed a globally more fragmented regime than both TD groups, with markedly shorter but more frequent microstate episodes and reduced duration variability. By contrast, TD-High and TD-Low were similar on these global indices. At the network level, Microstate C showed reduced explained variance and coverage in ASD relative to both TD groups. In Microstates E and G, explained variance and coverage increased from TD-Low to TD-High to ASD, with TD-High consistently occupying an intermediate position. Mean GFP and GFP variability for Microstate E were also elevated in ASD relative to both TD groups. Transition analyses revealed reduced short-range transitions within an early A-C ensemble and increased transitions from these states into other microstates in ASD, with TD-High again showing an attenuated, intermediate pattern. Chance-corrected transitions confirmed that sensory/self-related routes occurred less often than expected, whereas routes from these states into other microstates were over-expressed. These findings support a dimensional account in which EEG microstates index autism-related network organisation across clinical and subclinical ranges.

