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Brain Topography

, Volume 2, Issue 1–2, pp 81–89 | Cite as

Inter- and intra-individual probability maps in EEG cartography by use of nonparametric fisher tests

  • P. Etévenon
  • A. Bertaut
  • F. Mitermite
  • F. Eustache
  • J. Lepaisant
  • B. Lechevalier
  • E. Zarifian
Article

Summary

The three types of non-parametric permutation Fisher tests have been applied to inter-individual group studies and further to intra-individual multiple EEG recording sequences, providing computations of EEG probability maps testing two ordinal hypotheses. Two examples of previous group studies with "EEG local cerebral activation" are given: mental computation in a group of 20 controls and caffeine effects versus placebo in a group of 10 controls. For the intra-individual study, two successive recordings of 2.3 min eyes closed (EC1 and EC2), obtained at 50 min intervals, were compared by paired exact permutation Fisher tests (over 15 or 42 synchronous EEG sequences). These tests were applied to descriptive spectral parameters: RMS and % amplitudes, mean frequencies, resonance coefficient, for raw unfiltered EEG and delta, theta, alpha, alpha 1, alpha 2, beta 1, beta 2 frequency bands. Two hypotheses were tested for each of the computed 31 parameters, providing two probability maps indicating if the parameter was greater or lower in the first EEG recording or in the second. The second EEG sequence, EC2, was "EEG activated" compared to the first sequence EC1 if the following were present: decreased amplitudes mainly in raw EEG, low activity and alpha bands; increased frequencies mainly, in raw EEG, delta and beta 1 fast activities; increased fast activity percentages; decreased coefficient of resonance. The effect of choice of reference was also evaluated: probability maps for a frontal reference were different than other probability maps obtained after computation of average reference or source derivation. This new ordinal method can be applied to different types of multiple EEG recordings protocols, for intra-individual statistical comparisons before validating a group study.

Keywords

EEG cartography Fisher tests Probability maps Mental computation, Caffeine effects 

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References

  1. Abt, K. Problems of repeated significance testing. Controlled Clinical Trials, 1981, 1: 377–381.Google Scholar
  2. Abt, K. Significance testing of many variables - Problems and solutions. Neuropsychobiol., 1983, 9: 47–51.Google Scholar
  3. Abt, K. Descriptive data analyses: A concept between confirmatory and exploratory data analysis. Methods of Information in Medicine, 1987, 26: 77–88.Google Scholar
  4. Anderer P., Saletu B., Kingsperger K. and Semlitsch, H. Topographic Brain Mapping of EEG in Neuropsychopharmacology- Part I. Methodological Aspects. Meth and Find Exptl clin Pharmacol, 1987, 9: (6), 371–384.Google Scholar
  5. Buchsbaum M.S., Hazlett E., Sicotte N., Stein M., Wu J. and Zetin M. Topographic EEG changes with benzodiazepine administration in general anxiety disorder. Biol. Psychiatry, 1985, 20: 832–842.Google Scholar
  6. Coppola R. and Herrmann, W.M., Psychotropic Drug Profiles: comparisons by topographic maps of absolute power, Neuropsychobiology, 1987, 18: 97–104.Google Scholar
  7. F.H. Duffy Clinical decision making in quantified electroencephalographic analysis. In: D. Samson-Dollfus (Ed.), J.D. Guieu, J. Gotman, P. Etévenon (Co-Eds.): Statistics and Topography in Quantitative EEG, Elsevier, Amsterdam, 1988 9–26.Google Scholar
  8. Duffy, F.H., Bartels, P.H. and Burchfiel, J.L. Significance probability mapping: An aid in the topographic analysis of brain electrical activity. Electroenceph. Clin. Neurophysiol. 1981, 51: 455–462.Google Scholar
  9. Etévenon, P. Applications and perspectives of EEG cartography. In: F.H. Duffy (Ed.), Topographic Mapping of Brain Electrical Activity, Butterworths Pub., Stoneham, Mass., 1986, Chp 6, 115–142.Google Scholar
  10. Etévenon P. and Paquet T. A polygraphic laboratory workbench for measuring variability. In: D. Samson-Dollfus (Ed.), J.D. Guieux, J. Gotman and P. Etévenon, (Co-Eds.), Statistics and Topography in Quantitative EEG. Elsevier, Amsterdam, 1988, 96–103.Google Scholar
  11. Etévenon P., Péron-Magnan P., Guillou S., Toussaint M., Gueguen B., Boulenger J.P., Deniker P. and Loo H. Caféine et cartographie EEG: effets d'une tâche visuo-spatiale chez des volontaires sains. Stratégie d'analyse des données électro-pharmacologiques multi-voies et multi-sujets. Neurophysiologie Clinique. Clinical Neuro-physiology, 1988a, 18: 355–367,.Google Scholar
  12. Etévenon P., Péron-Magnan P., Guillou S., Toussaint M., Gueguen B., Deniker P., Loo H. and Zarifian E. A pharmacological model of "cerebral local activation": EEG cartography of caffeine effects in normals. In: Functional Brain Imaging, G. Pfurtscheller and F.H. Lopes da Silva, (Eds.), Hans Huber, Bern, 1988b, 171–180.Google Scholar
  13. Etévenon P., Tortrat D., Guillou S. Benkelfat C. EEG cartography II. By means of statistical group studies. Activation by visual attention. Neuropsychobiology, Vienna, 1985a, 13: 141–146.Google Scholar
  14. Etévenon P., Tortrat D. Guillou S. and Wendling B. Cartographie EEG au cours d'une tâche visuo-spatiale. Cartes moyennes et statistiques de groupes. Rev. EEG Neurophysiol. Clin., 1985b, 15: 139–147.Google Scholar
  15. Fox, T. P., Mintun Mark A., Reiman E.M. and Raichle M.E. Enhanced detection of Focal Brain responses Using Intersubject Averaging and Change-distribution Analysis of Subtracted PET Images. J. Cereb. Blood Flow Metab., 1988, 8: n°5, 642–643.Google Scholar
  16. Gasser T., Bacher P. and Mocks J. Transformations towards the normal distribution of broad band spectral parameters of the EEG, Electroenceph. clin. Neurophysiol., 1982, 53: 119–124.Google Scholar
  17. Goldstein L. Is a Man, a Man, a Man? (or: Is an EEG, an EEG, an EEG?) Some Remarks on the Homogeneity of "Normal Subjects". Pharmakopsychiat. 1979, 12: 74–78.Google Scholar
  18. Hart B.I. Significance levels for the ratio of the mean-square successive difference to the variance. Ann. Math. Stat. 1942, 13: 445–447.Google Scholar
  19. John E.R., Prichep L.S., Fridman J. and Easton, P. Neurometrics: Computer-Assisted Differential Diagnosis of Brain Dysfunctions, Science, 1988, 239: 162–169.Google Scholar
  20. Lebart L., Morineau A. and Fenelon J.P., Traitement des données statistiques, Dunod, Paris, 1982.Google Scholar
  21. Petersen S.E., Fox B.T., Posner M.I., Mintoun M. and Raichle M.E. Positon emission tomographic studies of the cortical anatomy of single-word processing. Nature, 1988, 331: 585–588.Google Scholar
  22. Petsche H., Rappelsberger P., Pockberger H. Sex Differences in the Ongoing EEG: Probability Mapping at Rest and during Cognitive Tasks. In: G. Pfurscheller and F.H. Lopes da Silva (Eds.), Functional Brain Imaging, Hans Huber Pub., Toronto, 1988, 161–169.Google Scholar
  23. Rappelsberger P. and Petsche H. Probability Mapping: Power and Coherence Analyses of Cognitive Processes, Brain Topography, 1988, 1: 1, 46–54.Google Scholar
  24. Röhmel J., Streitberg B. and Herrmann W.H. Example for a test strategy of EEG data using multiple test procedures and exact permutation tests, abstract, IPEG Symposium, Vienna, 1984.Google Scholar
  25. Saletu B., Anderer P., Kingsperger K. and Grünberger J. Topographic Brain Mapping of EEG in neuropsycho-pharmacology- Part II. Clinical applications (Pharmaco-EEG imaging). Meth. and Find. Exptl. Clin. Pharmacol., 1987, 9:(6), 385–408.Google Scholar
  26. Shapiro S.S. and Wilk M.B. An analysis of variance test for normality (complete samples). Biometrika, 1965, 52: 3–4, 591–611.Google Scholar
  27. Sheridan P.H., Sato S., Foster N., Bruno G., Cox C., Fedio P. and Chase T. Relation of EEG alpha background to parietal lobe function in Alzheimer's disease as measured by positron emission tomography and psychometry. Neurology, 1985, 38: 747–750.Google Scholar
  28. Tukey J.W. Exploratory Data Analysis, Addison-Wesley, Reading, MA, 1977.Google Scholar
  29. Tukey J.W. We need both exploratory and confirmatory data analysis. The American Statistician, 1980, 34: 23–25.Google Scholar
  30. Von Neumann J., Kent R.H., Bellinson H.R. and Hart, B.I. The mean square successive difference to the variance. Ann. Math. Statist. 1941, 12: 153–162.Google Scholar
  31. Walter D.O., Etévenon P., Pidoux B., Tortrat D. and Guillou S. Computerized Topo-EEG spectral maps: difficulties and perspectives. Neuropsychobiology, 1984, 11: 264–272.Google Scholar

Copyright information

© Human Sciences Press, Inc. 1989

Authors and Affiliations

  • P. Etévenon
    • 1
  • A. Bertaut
    • 1
  • F. Mitermite
    • 1
  • F. Eustache
    • 1
  • J. Lepaisant
    • 2
  • B. Lechevalier
    • 1
  • E. Zarifian
    • 1
  1. 1.Unité 320 Inserm, Center Esquirol, CHU, Côte de NacreCaen cedexFrance
  2. 2.Boulevard du marechal JuinGERSIC-ISMRACaen cedexFrance

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