Auditory and Visual Tasks Influence the Temporal Dynamics of EEG Microstates During Post-encoding Rest
- PDF / 2,329,043 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 78 Downloads / 195 Views
ORIGINAL PAPER
Auditory and Visual Tasks Influence the Temporal Dynamics of EEG Microstates During Post-encoding Rest David F. D’Croz‑Baron1 · Lucie Bréchet2,3 · Mary Baker1 · Tanja Karp1 Received: 12 August 2020 / Accepted: 15 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Re-activations of task-dependent patterns of neural activity take place during post-encoding periods of wakeful rest and sleep. However, it is still unclear how the temporal dynamics of brain states change during these periods, which are shaped by prior conscious experiences. Here, we examined the very brief periods of wakeful rest immediately after encoding and recognition of auditory and visual stimuli, by applying the EEG microstate analysis, in which the global variance of the EEG is explained by only a few prototypical topographies. We identified the dominant brain states of sub-second duration during the tasks-dependent periods of rest, finding that the temporal dynamics—represented here by two temporal parameters: the frequency of occurrence and the fraction of time coverage—of three task-related microstate classes changed compared to wakeful rest. This study provides evidence of experience-dependent temporal changes in post-encoding periods of resting brain activity, which can be captured using the EEG microstates approach. Keywords EEG microstates · Topographic EEG · Auditory task · Visual task · Resting-state networks · Large-scale cognitive networks
Introduction In recent years, the turn towards a network-based understanding of the human brain has led to a revival of the well-established multichannel electroencephalography (EEG) method, which examines the brain electric microstates during wakeful rest. (Michel and Koenig 2018). EEG microstates are assumed to reflect short-lasting connectivity patterns defined by common and non-lagged synchronization of extended sets of electric sources in the brain, which remain semi-stable for time intervals of 80–100 ms Handling Editor: Christoph Michel. * David F. D’Croz‑Baron david.dcroz‑[email protected] 1
Department of Electrical and Computer Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA
2
Arthur and Hinda Marcus Institute for Aging Research and Center for Memory Health, Hebrew SeniorLife, and Department of Neurology, Harvard Medical School, Boston, MA, USA
3
Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
(Lehmann et al. 1987). Interestingly, only a few microstate maps (or prototypical topographies) usually dominate the resting-state activity (Koenig et al. 2002), and these maps can be obtained through k-means cluster analysis or other pattern recognition approaches (Pascual-Marqui et al. 1995). The optimal number of EEG microstates that are necessary for capturing the informative features of the data depends on the specificity of the given dataset; however, four dominant EEG microstates (traditionally labeled A, B, C, and D) have often been observed at rest (Koenig et al. 2002; Michel and Koe
Data Loading...
