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.
<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>In this paper, we describe the successful implementation of an afterschool LEGO robotics program for elementary and middle school students that is annually offered by the Whitacre College of Engineering at Texas Tech University. Three events are held on campus: the kickoff, a trial run, and the competition, spread over a period of eight weeks. In between the events, participants design their LEGO robots at the school, mostly during afterschool clubs. Through our program, we broaden the participation in hands-on robotics tasks which apply STEM concepts to groups that otherwise might not have the opportunity. Success factors of our implementation are the flexibility of the implementation at the local level, the inclusion of engineering students as mentors and volunteers, and the low cost for organizers and participants. We provide evidence that we have reached a diverse student population in grades K-8 and positively changed their attitudes toward STEM, then we report the benefits that teachers see in regular participation in the event. Finally, we describe the benefit of involving engineering undergraduate students as mentors and volunteers. </span></p></div></div></div>
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