Brain Topography

, Volume 30, Issue 2, pp 245–248 | Cite as

Association Between Resting-State Microstates and Ratings on the Amsterdam Resting-State Questionnaire

  • Evaldas Pipinis
  • Sigita Melynyte
  • Thomas Koenig
  • Lina Jarutyte
  • Klaus Linkenkaer-Hansen
  • Osvaldas Ruksenas
  • Inga Griskova-Bulanova
Short Communication


There is a gap in understanding on how physiologically observed activity is related to the subjective, internally oriented experience during resting state. Microstate analysis is a frequent approach to evaluate resting-state EEG. But the relationship of commonly observed resting-state microstates to psychological domains of resting is not clear. The Amsterdam Resting-State Questionnaire (ARSQ) was recently introduced, offering an effective way to quantify subjective states after a period of resting and associate these quantifiers to psychological and physiological variables. In a sample of 94 healthy volunteers who participated in closed-eyes 5 min resting session with concurrent EEG recording and subsequent filling of the ARSQ we evaluated parameters of microstate Classes A, B, C, D. We showed a moderate negative association between contribution (r = −0.40) of Class C and experienced somatic awareness (SA). The negative correlation between Class C and SA seems reasonable as Class C becomes more dominant when connections to contextual information (and bodily sensations as assessed with SA) are loosened (in reduced attention states, during sleep, hypnosis, or psychosis). We suggest that the use of questionnaires such as the ARSQ is helpful in exploring the variation of resting-state EEG parameters and its relationship to variation in sensory and non-sensory experiences.


Microstates Amsterdam resting-state questionnaire Somatic awareness Class C 



We would like to thank all study volunteers for their participation. We also appreciate the help with data collection provided by Vaida Genyte. The study was supported by Lithuanian Research Council Grant MIP-009/2014.

Supplementary material

10548_2016_522_MOESM1_ESM.docx (40 kb)
Supplementary material 1 (DOCX 40 kb)


  1. Andrews-Hanna JR, Reidler JS, Huang C, Buckner RL (2010) Evidence for the default network’s role in spontaneous cognition. J Neurophysiol 104:322–335. doi: 10.1152/jn.00830.2009 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Brandeis D, Lehmann D (1989) Segments of event-related potential map series reveal landscape changes with visual attention and subjective contours. Electroencephalogr Clin Neurophysiol 73:507–519CrossRefPubMedGoogle Scholar
  3. Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage 52:1162–1170. doi: 10.1016/j.neuroimage.2010.02.052 CrossRefPubMedGoogle Scholar
  4. Brodbeck V, Kuhn A, von Wegner F et al (2012) EEG microstates of wakefulness and NREM sleep. Neuroimage 62:2129–2139. doi: 10.1016/j.neuroimage.2012.05.060 CrossRefPubMedGoogle Scholar
  5. Diaz BA, Van Der Sluis S, Moens S et al (2013) The Amsterdam resting-state questionnaire reveals multiple phenotypes of resting-state cognition. Front Hum Neurosci 7:446. doi: 10.3389/fnhum.2013.00446 PubMedPubMedCentralGoogle Scholar
  6. Diaz BA, Van Der Sluis S, Benjamins JS et al (2014) The ARSQ 2.0 reveals age and personality effects on mind-wandering experiences. Front Psychol 5:271. doi: 10.3389/fpsyg.2014.00271 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Diaz BA, Hardstone R, Mansvelder HD et al (2016) Resting-state subjective experience and EEG biomarkers are associated with sleep-onset latency. Front Psychol 7:492. doi: 10.3389/fpsyg.2016.00492 PubMedPubMedCentralGoogle Scholar
  8. Katayama H, Gianotti LRR, Isotani T et al (2007) Classes of multichannel EEG microstates in light and deep hypnotic conditions. Brain Topogr 20:7–14. doi: 10.1007/s10548-007-0024-3 CrossRefPubMedGoogle Scholar
  9. Koenig T, Melie-García L (2010) A method to determine the presence of averaged event-related fields using randomization tests. Brain Topogr 23:233–242. doi: 10.1007/s10548-010-0142-1 CrossRefPubMedGoogle Scholar
  10. Koenig T, Lehmann D, Merlo MC et al (1999) A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest. Eur Arch Psychiatry Clin Neurosci 249:205–211CrossRefPubMedGoogle Scholar
  11. Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288CrossRefPubMedGoogle Scholar
  12. Lehmann D, Strik WK, Henggeler B et al (1998) Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts. Int J Psychophysiol 29:1–11CrossRefPubMedGoogle Scholar
  13. Lehmann D, Pascual-Marqui RD, Strik WK, Koenig T (2010) Core networks for visual-concrete and abstract thought content: a brain electric microstate analysis. Neuroimage 49:1073–1079. doi: 10.1016/j.neuroimage.2009.07.054 CrossRefPubMedGoogle Scholar
  14. Milz P, Faber PL, Lehmann D et al (2016) The functional significance of EEG microstates-associations with modalities of thinking. Neuroimage 125:643–656. doi: 10.1016/j.neuroimage.2015.08.023 CrossRefPubMedGoogle Scholar
  15. Rieger K, Diaz Hernandez L, Baenninger A, Koenig T (2016) 15 Years of Microstate research in schizophrenia—where are we? a meta-analysis. Front psychiatry 7:22. doi: 10.3389/fpsyt.2016.00022 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Schlegel F, Lehmann D, Faber PL et al (2012) EEG microstates during resting represent personality differences. Brain Topogr 25:20–26. doi: 10.1007/s10548-011-0189-7 CrossRefPubMedGoogle Scholar
  17. Stoffers D, Diaz BA, Chen G et al (2015) Resting-state fmri functional connectivity is Associated with sleepiness, imagery, and discontinuity of mind. PLoS One 10:e0142014. doi: 10.1371/journal.pone.0142014 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Uddin LQ, Menon V (2010) Introduction to special topic—resting-state brain activity: implications for systems neuroscience. Front Syst Neurosci. doi: 10.3389/fnsys.2010.00037 Google Scholar
  19. van Diessen E, Numan T, van Dellen E et al (2015) Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol 126:1468–1481. doi: 10.1016/j.clinph.2014.11.018 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Neurobiology and BiophysicsVilnius UniversityVilniusLithuania
  2. 2.Translational Research Center, University Hospital of PsychiatryUniversity of BernBernSwitzerland
  3. 3.Department of Integrative NeurophysiologyCenter for Neurogenomics and Cognitive Research (CNCR)AmsterdamThe Netherlands

Personalised recommendations