Advertisement

Brain Topography

, Volume 27, Issue 1, pp 72–83 | Cite as

A Tutorial on Data-Driven Methods for Statistically Assessing ERP Topographies

  • Thomas Koenig
  • Maria Stein
  • Matthias Grieder
  • Mara Kottlow
Original Paper

Abstract

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called “atoms of thoughts”, that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.

Keywords

Microstates Timing Statistics Randomization Topography Model selection 

References

  1. Arzy S, Mohr C, Michel CM, Blanke O (2007) Duration and not strength of activation in temporo-parietal cortex positively correlates with schizotypy. Neuroimage 1:326–333. doi: 10.1016/j.neuroimage.2006.11.027 CrossRefGoogle Scholar
  2. Brandeis D, Naylor H, Halliday R, Callaway E, Yano L (1992) Scopolamine effects on visual information processing, attention, and event-related potential map latencies. Psychophysiology 3:315–336CrossRefGoogle Scholar
  3. Brandeis D, Lehmann D, Michel CM, Mingrone W (1995) Mapping event-related brain potential microstates to sentence endings. Brain Topogr 2:145–159CrossRefGoogle Scholar
  4. Brunet D, Murray MM, Michel CM (2011) Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci. doi: 10.1155/2011/813870 PubMedCentralPubMedGoogle Scholar
  5. Chouiter L, Dieguez S, Annoni JM, Spierer L (2013) High and low stimulus-driven conflict engage segregated brain networks, not quantitatively different resources. Brain Topogr. doi: 10.1007/s10548-013-0303-0 PubMedGoogle Scholar
  6. Darque A, Del ZM, Khateb A, Pegna AJ (2012) Attentional modulation of early ERP components in response to faces: evidence from the attentional blink paradigm. Brain Topogr 2:167–181. doi: 10.1007/s10548-011-0199-5 CrossRefGoogle Scholar
  7. De Lucia M, Michel CM, Murray MM (2010) Comparing ICA-based and single-trial topographic ERP analyses. Brain Topogr 2:119–127. doi: 10.1007/s10548-010-0145-y CrossRefGoogle Scholar
  8. De Lucia M, Tzovara A, Bernasconi F, Spierer L, Murray MM (2012) Auditory perceptual decision-making based on semantic categorization of environmental sounds. Neuroimage 3:1704–1715. doi: 10.1016/j.neuroimage.2012.01.131 CrossRefGoogle Scholar
  9. Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice-Hall, LondonGoogle Scholar
  10. Grieder M, Crinelli RM, Koenig T, Wahlund LO, Dierks T, Wirth M (2012) Electrophysiological and behavioral correlates of stable automatic semantic retrieval in aging. Neuropsychologia 50(1):160–171. doi: 10.1016/j.neuropsychologia.2011.11.014 PubMedCrossRefGoogle Scholar
  11. Kikuchi M, Koenig T, Wada Y, Higashima M, Koshino Y, Strik W, Dierks T (2007) Native EEG and treatment effects in neuroleptic-naïve schizophrenic patients: time and frequency domain approaches. Schizophr Res 97(1-3):163–172. doi: 10.1016/j.schres.2007.07.012 PubMedCrossRefGoogle Scholar
  12. Knebel JF, Murray MM (2012) Towards a resolution of conflicting models of illusory contour processing in humans. Neuroimage 3:2808–2817. doi: 10.1016/j.neuroimage.2011.09.031 CrossRefGoogle Scholar
  13. Koenig T, Melie-Garcia L (2010) A method to determine the presence of averaged event-related fields using randomization tests. Brain Topogr 3:233–242. doi: 10.1007/s10548-010-0142-1 CrossRefGoogle Scholar
  14. Koenig T, Lehmann D, Merlo MC, Kochi K, Hell D, Koukkou M (1999) A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest. Eur Arch Psychiatry Clin Neurosci 4:205–211CrossRefGoogle Scholar
  15. Koenig T, Prichep L, Lehmann D, Sosa PV, Braeker E, Kleinlogel H, Isenhart R, John ER (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage 1:41–48. doi: 10.1006/nimg.2002.1070 CrossRefGoogle Scholar
  16. Koenig T, Melie-Garcia L, Stein M, Strik W, Lehmann C (2008) Establishing correlations of scalp field maps with other experimental variables using covariance analysis and resampling methods. Clin Neurophysiol 6:1262–1270. doi: 10.1016/j.clinph.2007.12.023 CrossRefGoogle Scholar
  17. Koenig T, Kottlow M, Stein M, Melie-Garcia L (2011) Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics. Comput Intell Neurosci. doi: 10.1155/2011/938925 PubMedCentralPubMedGoogle Scholar
  18. Kottlow M, Praeg E, Luethy C, Jancke L (2011) Artists’ advance: decreased upper alpha power while drawing in artists compared with non-artists. Brain Topogr 4:392–402. doi: 10.1007/s10548-010-0163-9 CrossRefGoogle Scholar
  19. Kovalenko LY, Chaumon M, Busch NA (2012) A pool of pairs of related objects (POPORO) for investigating visual semantic integration: behavioral and electrophysiological validation. Brain Topogr 3:272–284. doi: 10.1007/s10548-011-0216-8 CrossRefGoogle Scholar
  20. Kutas M, Hillyard SA (1980) Reading senseless sentences: brain potentials reflect semantic incongruity. Science 207:203–205PubMedCrossRefGoogle Scholar
  21. Laganaro M, Perret C (2011) Comparing electrophysiological correlates of word production in immediate and delayed naming through the analysis of word age of acquisition effects. Brain Topogr 1:19–29. doi: 10.1007/s10548-010-0162-x CrossRefGoogle Scholar
  22. Lehmann D (1990) Brain electric microstates and cognition: the atoms of thought. In: John ER Vol. Machinery of the mind. Birkhäuser, Boston, pp 209–224Google Scholar
  23. Lehmann D (1987) Principles of spatial analysis. In: Gevins A, Remond A (eds) Methods of analysis of brain electrical and magnetic signals: handbook of electroencephalography and clinical neurophysiology, vol 1. Elsevier, Amsterdam, pp 309–354Google Scholar
  24. Lehmann D, Skrandies W (1980) Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr Clin Neurophysiol 6:609–621CrossRefGoogle Scholar
  25. Lehmann D, Skrandies W (1984) Spatial analysis of evoked potentials in man—a review. Prog Neurobiol 3:227–250CrossRefGoogle Scholar
  26. Lehmann D, Wackermann J, Michel CM, Koenig T (1993) Space-oriented EEG segmentation reveals changes in brain electric field maps under the influence of a nootropic drug. Psychiatry Res 4:275–282CrossRefGoogle Scholar
  27. Lehmann D, Faber PL, Galderisi S, Herrmann WM, Kinoshita T, Koukkou M, Mucci A, Pascual-Marqui RD, Saito N, Wackermann J, Winterer G, Koenig T (2005) EEG microstate duration and syntax in acute, medication-naive, first-episode schizophrenia: a multi-center study. Psychiatry Res 2:141–156. doi: 10.1016/j.pscychresns.2004.05.007 CrossRefGoogle Scholar
  28. Manly BFJ (2007) Randomization. Bootstrap and Monte Carlo Methods in Biology. Chapman & Hall, Boca RatonGoogle Scholar
  29. McCarthy G, Wood CC (1985) Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models. Electroencephalogr Clin Neurophysiol 3:203–208CrossRefGoogle Scholar
  30. Megevand P, Quairiaux C, Lascano AM, Kiss JZ, Michel C (2008) A mouse model for studying large-scale neuronal networks using EEG mapping techniques. Neuroimage 42(2):591–602. doi: 10.1016/j.neuroimage.2008.05.016 PubMedCrossRefGoogle Scholar
  31. Michel C, Koenig T, Brandeis D (2009) Electrical neuroimaging in the time domain. In: Michel CM, Koenig T, Brandeis D, Gianotti LRR and Wackermann J, Vol. Electrical Neuroimaging, Cambridge, pp 111–143CrossRefGoogle Scholar
  32. Murray MM, Brunet D, Michel CM (2008) Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr 4:249–264. doi: 10.1007/s10548-008-0054-5 CrossRefGoogle Scholar
  33. Nishida K, Morishima Y, Yoshimura M, Isotani T, Irisawa S, Jann K, Dierks T, Strik W, Kinoshita T, Koenig T (2013) EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and Alzheimer’s disease. Clin Neurophysiol 6:1106–1114. doi: 10.1016/j.clinph.2013.01.005 CrossRefGoogle Scholar
  34. Overney LS, Michel CM, Harris IM, Pegna AJ (2005) Cerebral processes in mental transformations of body parts: recognition prior to rotation. Brain Res Cogn Brain Res 3:722–734. doi: 10.1016/j.cogbrainres.2005.09.024 CrossRefGoogle Scholar
  35. Pannekamp A, van der Meer E, Toepel U (2011) Context- and prosody-driven ERP markers for dialog focus perception in children. Brain Topogr 3–4:229–242. doi: 10.1007/s10548-011-0194-x CrossRefGoogle Scholar
  36. Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Tran Biomed Eng 7:658–665. doi: 10.1109/10.391164 CrossRefGoogle Scholar
  37. Pegna AJ, Khateb A, Spinelli L, Seeck M, Landis T, Michel CM (1997) Unraveling the cerebral dynamics of mental imagery. Hum Brain Mapp 5(6):410–421. doi: 10.1002/(SICI)1097-0193(1997)5:6<410:AID-HBM2>3.0.CO;2-6 PubMedCrossRefGoogle Scholar
  38. Perret C, Laganaro M (2012) Comparison of electrophysiological correlates of writing and speaking: a topographic ERP analysis. Brain Topogr 1:64–72. doi: 10.1007/s10548-011-0200-3 CrossRefGoogle Scholar
  39. Pourtois G (2011) Early error detection predicted by reduced pre-response control process: an ERP topographic mapping study. Brain Topogr 4:403–422. doi: 10.1007/s10548-010-0159-5 CrossRefGoogle Scholar
  40. Pourtois G, Delplanque S, Michel C, Vuilleumier P (2008) Beyond conventional event-related brain potential (ERP): exploring the time-course of visual emotion processing using topographic and principal component analyses. Brain Topogr 20(4):265–277. doi: 10.1007/s10548-008-0053-6 PubMedCrossRefGoogle Scholar
  41. Spierer L, Tardif E, Sperdin H, Murray MM, Clarke S (2007) Learning-induced plasticity in auditory spatial representations revealed by electrical neuroimaging. J Neurosci 20:5474–5483. doi: 10.1523/JNEUROSCI.0764-07.2007 CrossRefGoogle Scholar
  42. Stein M, Dierks T, Brandeis D, Wirth M, Strik W, Koenig T (2006) Plasticity in the adult language system: a longitudinal electrophysiological study on second language learning. Neuroimage 33(2):774–783. doi: 10.1016/j.neuroimage.2006.07.008 PubMedCrossRefGoogle Scholar
  43. Stevenson RA, Bushmakin M, Kim S, Wallace MT, Puce A, James TW (2012) Inverse effectiveness and multisensory interactions in visual event-related potentials with audiovisual speech. Brain Topogr 3:308–326. doi: 10.1007/s10548-012-0220-7 CrossRefGoogle Scholar
  44. Strik W, Fallgatter AJ, Brandeis D, Pascual-Marqui RD (1998) Three-dimensional tomography of event-related potentials during response inhibition: evidence for phasic frontal lobe activation. Electroencephalogr Clin Neurophysiol 4:406–413CrossRefGoogle Scholar
  45. Taha H, Ibrahim R, Khateb A (2013) How does arabic orthographic connectivity modulate brain activity during visual word recognition: an ERP study. Brain Topogr 2:292–302. doi: 10.1007/s10548-012-0241-2 CrossRefGoogle Scholar
  46. Tzovara A, Murray MM, Michel C, De Lucia M (2012a) A tutorial review of electrical neuroimaging from group-average to single-trial event-related potentials. Dev Neuropsychol 6:518–544. doi: 10.1080/87565641.2011.636851 CrossRefGoogle Scholar
  47. Tzovara A, Murray MM, Plomp G, Herzog MH, Michel CM, De Lucia M (2012b) Decoding stimulus-related information from single-trial EEG responses based on voltage topographies. Pattern Recogn 6:2109–2122CrossRefGoogle Scholar
  48. Tzovara A, Rossetti AO, Spierer L, Grivel J, Murray MM, Oddo M, De Lucia M (2013) Progression of auditory discrimination based on neural decoding predicts awakening from coma. Brain 1:81–89. doi: 10.1093/brain/aws264 CrossRefGoogle Scholar
  49. Wackermann J, Lehmann D, Michel CM, Strik WK (1993) Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. Int J Psychophysiol 3:269–283CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Thomas Koenig
    • 1
  • Maria Stein
    • 1
    • 2
  • Matthias Grieder
    • 1
  • Mara Kottlow
    • 1
    • 3
  1. 1.Department of Psychiatric NeurophysiologyUniversity Hospital of Psychiatry, University of BernBernSwitzerland
  2. 2.Department of Clinical Psychology and PsychotherapyInstitute of Psychology, University of BernBernSwitzerland
  3. 3.Institute of Pharmacology and Toxicology, University of ZurichZurichSwitzerland

Personalised recommendations