Biological Cybernetics

, Volume 98, Issue 4, pp 295–303 | Cite as

Split-test Bonferroni correction for QEEG statistical maps

  • Francois-Benoit Vialatte
  • Andrzej Cichocki
Original Paper


With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer’s disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.


QEEG Split test Conjunction Bonferroni correction Brain imaging Type II error 


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  1. Babiloni C, Binetti G, Cassetta E, Cerboneschi D, Dal Forno G, Del Percio C, Ferreri F, Ferri R, Lanuzza B, Miniussi C, Moretti DV, Nobili F, Pascual-Marqui RD, Rodriguez G, Romani GL, Salinari S, Tecchio F, Vitali P, Zanetti O, Zappasodi F, Rossini PM (2004) Mapping distributed sources of cortical rhythms in mild Alzheimer’s disease. A multicentric EEG study. Neuroimage 22(1): 57–67PubMedCrossRefGoogle Scholar
  2. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57: 289–300Google Scholar
  3. Besthorn C, Zerfass R, Geiger-Kabisch C, Sattel H, Daniel S, Schreiter-Gasser U, Forstl H (1997) Discrimination of Alzheimer’s disease and normal aging by EEG data. Electroencephalogr Clin Neurophysiol 103(2): 241–248PubMedCrossRefGoogle Scholar
  4. Bonferroni CE (1936) Teoria statistica delle classi e calcolo delle probabilit. Pubblicazioni del R. Istituto Superiore di Scienze Economiche e Commerciali di Firenze 8: 3–62Google Scholar
  5. Caplan D, Moo L (2004) Cognitive conjunction and cognitive functions. Neuroimage 21(2): 751–756PubMedCrossRefGoogle Scholar
  6. Claus JJ, Kwa VI, Teunisse S, Walstra GJ, Van Gool WA, Koelman JH, Bour LJ, Ongerboerde Visser BW (1998) Slowing on quantitative spectral EEG is a marker for rate of subsequent cognitive and functional decline in early Alzheimer disease. Alzheimer Dis Assoc Disord 12(3): 167–174PubMedCrossRefGoogle Scholar
  7. Drohocki Z, Goldstein L, Minz B (1956) Quantitative differences in electroencephalography of rabbits between the states of waking and anesthesia. Rev Neurol (Paris) 94(2): 144–145Google Scholar
  8. Freeman WJ (1988) Strange attractors that govern mammalian brain dynamics shown by trajectories of electroencephalographic (EEG) potential. IEEE Trans Circuits Syst 35: 781–783CrossRefGoogle Scholar
  9. Friston KJ, Frith CD, Liddle PF, Frackowiak RS (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13(1): 5–14PubMedGoogle Scholar
  10. Friston KJ, Holmes AP, Price CJ, Buchel C, Worsley KJ (1999) Multisubject fMRI studies and conjunction analyses. Neuroimage 10(4): 385–396PubMedCrossRefGoogle Scholar
  11. Friston KJ, Penny WD, Glaser DE (2005) Conjunction revisited. Neuroimage 25: 661–667PubMedCrossRefGoogle Scholar
  12. Gasser T, Bacher P, Mocks J (1982) Transformations towards the normal distribution of broad band spectral parameters of the EEG. Electroencephalogr Clin Neurophysiol 53(1): 119–124PubMedCrossRefGoogle Scholar
  13. Hochberg Y (1988) A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75: 800–803CrossRefGoogle Scholar
  14. Hogan MJ, Swanwick GR, Kaiser J, Rowan M, Lawlor B (2003) Memory-related EEG power and coherence reductions in mild Alzheimer’s disease. Int J Psychophysiol 49(2): 147–163PubMedCrossRefGoogle Scholar
  15. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6: 65–70Google Scholar
  16. Holmes AP, Blair RC, Watson JD, Ford I (1996) Nonparametric analysis of statistic images from functional mapping experiments. J Cereb Blood Flow Metab 16: 7–22PubMedCrossRefGoogle Scholar
  17. Ihl R, Dierks T, Martin EM, Froolich L, Maurer K (1996) Topography of the maximum of the amplitude of EEG frequency bands in dementia of the Alzheimer type. Biol Psychiatry 39(5): 319–325PubMedCrossRefGoogle Scholar
  18. Kilner JM, Kienel SJ, Friston KJ (2005) Applications of random field theory to electrophysiology. Neurosci Lett 374: 174–178PubMedCrossRefGoogle Scholar
  19. Kowalski JW, Gawel M, Pfeffer A, Barcikowska M (2001) The diagnostic value of EEG in Alzheimer disease: correlation with the severity of mental impairment. J Clin Neurophysiol 18(6): 570–575PubMedCrossRefGoogle Scholar
  20. Leuchter AF, Cook IA, Newton TF, Dunkin J, Walter DO, Rosenberg-Tompson S, Lachenbruch PA, Weiner H (1993) Regional differences in brain electrical activity in dementia: use of spectral power and spectral ratio measures. Electroencephalogr Clin Neurophysiol 87: 385–393PubMedCrossRefGoogle Scholar
  21. Lindman HR (1974) Analysis of variance in complex experimental designs. Edited by W. H. Freeman & Co., San FranciscoGoogle Scholar
  22. Mann HB, Whitney DR (1947) On a test of whether one of 2 random variables is stochastically larger than the other. Ann Math Stat 18: 50–60CrossRefGoogle Scholar
  23. Nichols T, Brett M, Andersson J, Wager T, Poline JB (2005) Valid conjunction inference with the minimum statistic. Neuroimage 25(3): 653–660PubMedCrossRefGoogle Scholar
  24. Nuwer MR (1988) Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping. J Clin Neurophysiol 5(1): 1–43PubMedCrossRefGoogle Scholar
  25. Perneger TV (1998) What is wrong with Bonferroni adjustments. Br Med J 136: 1236–1238Google Scholar
  26. Shaffer JP (1995) Multiple hypothesis testing. Ann Rev Psychol 46: 561–584CrossRefGoogle Scholar
  27. Sidak Z (1967) Rectangular confidence regions for the means of multivariate normal distributions. J Am Stat Assoc 62: 626–633CrossRefGoogle Scholar
  28. Vander Hiele K, Vein AA, Kramer CGS, Reijntjes RHAM, Van Buchem MA, Westendorp RGJ, Bollen ELEM, Van Dijk JG, Middelkoop HAM (2007) Memory activation enhances EEG abnormality on mild cognitive impairment. Neurobiol Aging 28(1): 85–90CrossRefGoogle Scholar
  29. Vialatte F, Cichocki A, Dreyfus G, Musha T, Rutkowski T, Gervais R (2005) Blind source separation and sparse bump modelling of time frequency representation of EEG signals: new tools for early detection of Alzheimer’s disease. Proc IEEE M.L.S.P., Mystic CT, USA, pp 27–32Google Scholar
  30. Westfall PH, Young SS (1993) Resampling-based multiple testing: examples and methods for P value adjustment. Edited by Wiley, New York, pp 162–192Google Scholar
  31. Worsley KJ, Marrett S, Neelin P, Evans AC (1992) Three-dimensional statistical analysis for CBF activation studies in human brain. J Cereb Blood Flow Metab 12: 900–918PubMedGoogle Scholar

Copyright information

© The Author(s) 2008

Authors and Affiliations

  1. 1.RIKEN Brain Science InstituteWako-Shi, Saitama-KenJapan

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