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

• Published in: Frontiers in human neuroscience Vol. 13; p. 173
• Main Authors: D’Croz-Baron, David F., Baker, Mary, Michel, Christoph M., Karp, Tanja
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 12.06.2019; Frontiers Media S.A
• Subjects: Autism; autism spectrum disorder; Computer engineering; Disease control; EEG; EEG microstates; Electrodes; Electroencephalography; Laboratories; Neural networks; Neuroscience; resting state; Statistical analysis; Studies; topographical analysis; Topography; Young adults; Lubbock Texas; United States > US; Texas; electroencephalography; EEG microstates; resting state; autism spectrum disorder; topographical analysis
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2019.00173

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.