EEG Microstates Analysis in Young Adults With Autism Spectrum Disorder During Resting-State

Abstract

Electroencephalography (EEG) is a useful tool to inspect the brain activity in resting state and allows to characterize spontaneous brain activity that is not detected when a subject is cognitively engaged. Moreover, taking advantage of the high time resolution in EEG, it is possible to perform fast topographical reference-free analysis, since different scalp potential fields correspond to changes in the underlying sources within the brain. In this study, the spontaneous EEG resting state (eyes closed) was compared between 10 young adults ages 18–30 years with autism spectrum disorder (ASD) and 13 neurotypical controls. A microstate analysis was applied, focusing on four temporal parameters: mean duration, the frequency of occurrence, the ratio of time coverage, and the global explained variance (GEV). Using data that were acquired from a 65-channel EEG system, six resting-state topographies that best describe the dataset across all subjects were identified by running a two-step cluster analysis labeled as microstate classes A–F. The results indicated that microstates B and E displayed statistically significant differences between both groups among the temporal parameters evaluated. Classes B, D, E, and F were consistently more present in ASD, and class C in controls. The combination of these findings with the putative functional significance of the different classes suggests that during resting state, the ASD group was more focused on visual scene reconstruction, while the control group was more engaged with self-memory retrieval. Furthermore, from a connectivity perspective, the resting-state networks that have been previously associated with each microstate class overlap the brain regions implicated in impaired social communication and repetitive behaviors that characterize ASD.

Beyond broadband: towards a spectral decomposition of EEG microstates

“…Figure 1 illustrates the topographic results of MS segmentations in the different conditions and frequency bands. After visual inspection of optimal clusters at different cluster numbers (k), we identified that a value of k=5 revealed five MS topographies that were similar across all EEG bands and behavioural conditions, consistent with recent findings from our laboratory [21], [26], [27]. MS maps were designated in line with the canonical prototypes from the literature and their respective symbols, featuring a left-right orientation (A), a right-left orientation (B), an anterior-posterior orientation (C), fronto-central maximum (D) and occipito-central (C’)maximum.…”

Early alterations of large-scale brain networks temporal dynamics in young children with autism

“…We found four group cluster maps, very similar to microstates classes A, B, C and D previously described in the literature (Jan et al, 2019). To the best of our knowledge, only three other studies using a microstate approach in individuals with ASD have been published (D'Croz- Baron et al, 2019;Jia & Yu, 2019;Malaia et al, 2016). D'Croz-Baron and colleagues 2019 found six microstate classes that best described their dataset in young adults with ASD and their TD peers.…”

Children With Autism Produce a Unique Pattern of EEG Microstates During an Eyes Closed Resting-State Condition

“… Jia and Yu (2019) found that resting-state microstate parameters differed in adolescents with ASD as compared to age-matched typical controls in data containing mixed periods of eyes-open (EO) and eyes-closed (EC) conditions with increased frequency and coverage of microstate B, decreased duration of microstate A, and decreased duration and coverage of microstate C. Given that the two eye conditions result in functional differences in microstate readouts ( Seitzman et al, 2017 ), it is necessary to investigate these two conditions separately. In the second study, D’Croz-Baron et al, 2019 also found that several microstate parameters differed in ASD adults as compared to the control group in EC resting-state ( D’Croz-Baron et al, 2019 ). This study found an increase in frequency and coverage of microstate B, and a decrease in the duration and coverage of microstate C. However, this study used the updated meta-criterion ( Custo et al, 2017 ; Michel and Koenig, 2018 ) to select more than four classes of microstates so the results of the other microstate classes from these two studies are not comparable.…”