Brain Topography

, Volume 4, Issue 4, pp 249–257 | Cite as

QEEG profiles of psychiatric disorders

  • L. S. Prichep
  • E. R. John


While reports of EEG correlates of psychiatric disorders date back five decades, clinical sensitivity of the EEG to psychiatric disorders has been greatly enhanced with the advent of quantitative methods of analysis (QEEG). Using a QEEG methodology known as neurometrics we have identified distinctive electrophysiological profiles associated with different psychiatric disorders. With this method quantitative features are extracted from 2 minutes of artifact- free eyes closed resting EEG data, log transformed to obtain Gaussianity, age-regressed, and Z-transformed relative to population norms. Using small subsets of neurometric features, multiple stepwise discriminant analyses were used to construct mathematical classifier functions, the values of which are different for members of different a priori defined diagnostic groups. Using this approach, we have demonstrated high discriminant accuracy in independent replications separating many populations of psychiatric patients from normal as well as from each other, including major affective disorder, schizophrenia, dementia, alcoholism, and learning disabilities, as well as high accuracy of discrimination between known subtypes of depression (unipolar vs bipolar). The use of classification accuracy curves (CACs) which allow one to assess the sensitivity and specificity achieved by the discriminant functions is discussed. In addition, using cluster analysis, neurometric subtypes can be identified in several clinically homogenous populations. Preliminary results suggest that baseline membership in some neurometric subtypes may be highly correlated with response to treatment.

Key words

Quantitative electroencephalography QEEG Neurometrics QEEG profiles Psychiatric discriminants QEEG subtyping 


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  1. Ahn, H., Prichep, L., John, E., Baird, H., Trepetin, M., and Kaye, H. Developmental equations reflect brain dysfunction. Science, 1980, 210: 1259–1262.Google Scholar
  2. Alvarez, A., Pascual, R., and Valdez, P. U.S. EEG developmental equations confirmed for Cuban schoolchildren. Electroenceph. clin. Neurophysiol., 1987, 67: 330–332.Google Scholar
  3. Berger, H. Electroencephalogram of man. Arch. Psychiat. Nervenkr., 1937, 106: 577–584.Google Scholar
  4. Czobor, P. and Volavka, J. Pretreatment EEG predicts shortterm response to haloperidol treatment. Biolog. Psychiat., 1991, 30: 927–942.Google Scholar
  5. Fitz-Gerald, M. J. and Patrick, G. Longitudinal quantitative eeg findings after acute carbon monoxide exposure: Two case studies. Clin. Electroencephalography, 1991, 22(4): 217–224.Google Scholar
  6. Gasser, T., Bacher, P., and Mochs, J. Transformation towards the normal distribution of broadband spectral parameters of the EEG. Electroenceph. clin. Neurophysiol., 1982, 53: 119–124.Google Scholar
  7. Harmony, T. Neurometría y maduracion cerebral. Neurol. Neurocir. Psiquiatr., 1984, 25(7).Google Scholar
  8. Harmony, T. Psychophysiological evaluation of children's neuropsychological disorders. In Reynolds, C., editor, Handbook of Child Clinical Neuropsychology, 1988, chapter 15, pages 265–290. Plenum, New York.Google Scholar
  9. Harmony, T., Alvarez, A., Pascual, R., Ramos, A., Marosi, E., Leon, A. D. D., Valdes, P., and Becker, J. EEG maturation on children with different economic and psychosocial characteristics. Intl. J. Neurosci., 1987, 31: 103–113.Google Scholar
  10. John, E., Ahn, H., Prichep, L., Trepetin, M., Brown, D., and Kaye, H. Developmental equations for the electroencephalogram. Science, 1980, 210: 1255–1258.Google Scholar
  11. John, E., Harmony, T., and Valdes-Sosa, P. The use of statistics in electrophysiology. In Remond, A., editor, Handbook of Electroencephalography and Clinical Neurophysiology, Vol III, Computer Analysis of the EEG and Other Neurophysiological Signals, 1987, pages 497–540. Elsevier, Amsterdam.Google Scholar
  12. John, E. and Prichep, L. Neurometric studies of aging and cognitive impairment. In Uylings, H., Eden, C. V., Bruin, J. D., Corner, M., and Feenstra, M., editors, The Prefrontal Cortex, its Structure, Function and Pathology. Progress in Brain Research, 1990, pages 545–555. Elsevier, Amsterdam.Google Scholar
  13. John, E., Prichep, L., Ahn, H., Easton, P., Fridman, J., and Kaye, H. Neurometric evaluation of cognitive dysfunctions and neurological disorders in children. Progress in Neurobiol., 1983, 21: 239–290.Google Scholar
  14. John, E., Prichep, L., Fridman, J., and Easton, P. Neurometrics: Computer assisted differential diagnosis of brain dysfunctions. Science, 1988a, 293: 162–169.Google Scholar
  15. John, E., Prichep, L., Friedman, J., and Essig-Peppard, T. Neurometric classification of patients with different psychiatric disorders. In Samson-Dollfus, D., editor, Statistics and Topography in Quantitative EEG, 1988b, pages 88–95. Elsevier, Paris.Google Scholar
  16. Jonkman, E., Poortvliet, D., Veering, M., deWeerd, A., and John, E. The use of neurometrics in the study of patients with cerebral ischemia. Electroenceph. and clin. Neurophysiol., 1985, 51: 333–341.Google Scholar
  17. Mas, F., Prichep, L., John, E., and Levine, R. Neurometric Q-EEG subtyping of obsessive compulsive disorder. In Maurer, K., editor, Imaging of the Brain in Psychiatry and Related Fields. Springer Verlag, Berlin, Heidelberg, 1991.Google Scholar
  18. Matousek, M. and Petersén, I. Norms for the EEG. In Kellaway, P. and Petersen, I., editors, Automation of Clinical Electroencephalography, 1973, pages 75–102. Raven, New York.Google Scholar
  19. Oken, B. and Chiappa, K. Statistical issues concerning computerized analysis of brainwave topography. Annals of Neurology, 1986, 19: 493–494.Google Scholar
  20. Prichep, L. Neurometric quantitative EEG measures of depressive disorders. In Takahashi, R., Flor-Henry, P., Gruzelier, J., and Niwa, S., editors, Cerebral Dynamics, Laterality and Psychopathology, 1987, pages 55–69. Elsevier, Amsterdam, New York, Oxford.Google Scholar
  21. Prichep, L., John, E., Essig-Peppard, T., and Alper, K. Neurometric subtyping of depressive disorders. In Cazzullo, C., Invernizzi, G., Sacchetti, E., and Vita, A., editors, Plasticity and Morphology of the Central Nervous System. M.T.P. Press, London, 1990a.Google Scholar
  22. Prichep, L., John, E., Ferris, S., Reisberg, B., Alper, K., and Cancro, R. Quantitative EEG correlates of cognitive deterioration in the elderly. In New Research Program and Abstracts: American Psychiatric Association 143rd. Annual Meeting, 1990b, page 301, New York, New York.Google Scholar
  23. Prichep, L., Mas, F., John, E., and Levine, R. Neurometric subtyping of obsessive compulsive disorders. In Stefanis, C., Rabavilas, A., and Soldatos, C., editors, Psychiatry: A World Perspective - Volume 1, 1991, pages 557–562. Elsevier.Google Scholar
  24. Roemer, R., Shagass, C., Dubin, W., Joffe, R., and Katz, R. Relationship between pretreatment electroencephalographic coherence measures and subsequent response to electroconvulsive therapy: A preliminary study. Neuropsychobiol., 1991, 180.Google Scholar
  25. Senf, G. Neurometric brain mapping in the diagnostic rehabilitation of cognitive brain dysfunction. Cognitive Rehabilitation, 1988, 6: 20–37.Google Scholar
  26. Shagass, C., Roemer, R., Straumanis, J., and Josiassen, R. Psychiatric diagnostic discriminations with combinations of quantitative EEG variables. Brit. J. Psychiat., 1984, 144: 581–592.Google Scholar
  27. Struve, F., Straumanis, J., Patrick, G., and Price, L. Topographic mapping of quantitative EEG variables in chronic heavy marijuana users: Empirical findings with psychiatric patients. Clin. Electroenceph., 1989, 20(1): 6–23.Google Scholar
  28. Struve, F., Straumanis, J., Patrick, G., and Raz, Y. Quantitative EEG and cognitive evoked potentials in chronic marijuana users. Electroenceph. clin. Neurophyiol., 1990, 75(1): 145.Google Scholar
  29. Swets, J. Measuring the accuracy of diagnostic systems. Science, 1988, 240: 1285–1293.Google Scholar
  30. Thatcher, R., Walker, R., Gerson, I., and Geisler, F. EEG discriminant analyses of mild head trauma. Electroenceph. clin. Neurophysiol., 1989, 73: 94–106.Google Scholar
  31. Veering, N., Jonkman, E., Poortvliet, D., de Weerd, A., Tans, J., and John, E. The effect of reconstructive vascular surgery on clinical status, quantitative EEG and cerebral blood flow in patients with cerebral ischaemia. A three month follow-up study in operated and unoperated stroke patients. Electroenceph. clin. Neurophysiol., 1986, 64(5): 383–393.Google Scholar
  32. de Weerd, A., Veldhuizen, R., Veering, M., and Poortvliet, D. Long-term clinical and neurophysiological effects of reconstructive vascular surgery for cerebral ischemia. Acta Neurologica Scandinavica, 1989, 79: 311–315.Google Scholar
  33. Yingling, C., Galin, D., Fein, G., Peltzman, D., and Davenport, L. Neurometrics does not detect ‘pure’ dyslexics. Electroenceph. clin. Neurophysiol., 1986, 63: 426–430.Google Scholar

Copyright information

© Human Sciences Press, Inc 1992

Authors and Affiliations

  • L. S. Prichep
    • 1
    • 2
  • E. R. John
    • 1
  1. 1.Brain Research Laboratories, Department of PsychiatryNYU Medical Center at Old BellevueNew YorkUSA
  2. 2.Nathan S. Kline Research InstituteOrangeburgUSA

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