Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-23T02:24:27.841Z Has data issue: false hasContentIssue false
  • English
  • Français

Neurometric-quantitative EEG as a diagnostic adjunct in clinical psychiatry

Published online by Cambridge University Press:  16 April 2020

F Mas ,
LS Prichep ,
R Cancro ,
ER John  and
K Alper
F Mas
Affiliation:
Brain Research Laboratories, Dept of Psychiatry, New York University Medical Center, New York, NY
LS Prichep
Affiliation:
Brain Research Laboratories, Dept of Psychiatry, New York University Medical Center, New York, NY Nathan S, Kline Institute for Psychiatric Research, Orangeburg, NY, USA
R Cancro
Affiliation:
Brain Research Laboratories, Dept of Psychiatry, New York University Medical Center, New York, NY Nathan S, Kline Institute for Psychiatric Research, Orangeburg, NY, USA
ER John
Affiliation:
Brain Research Laboratories, Dept of Psychiatry, New York University Medical Center, New York, NY Nathan S, Kline Institute for Psychiatric Research, Orangeburg, NY, USA
K Alper
Affiliation:
Brain Research Laboratories, Dept of Psychiatry, New York University Medical Center, New York, NY
Get access

Summary

Computer based quantitative evolution of the electroencephalogram (QEEG) holds promise as an adjunct in the evaluation of psychiatric patients. One such method is neurometrics (N-QEEG) in which quantitative electrophysiological features are evaluated by statistical comparison with age appropriate normative data and compared with the profile of dysfunction seen in different psychiatric populations. This paper is based upon the experiences of the senior author in using this method in a series of 88 patients seen in a clinical setting. Neurometric testing provided a unique and significant contribution to the clinical diagnosis or management of 12% of these cases and gave some clinically useful information in another 44% of this population. In the remaining 44%, the method did not provide any additional contribution to the clinical diagnosis and/or to the management of the patient. Six case histories are provided to illustrate these 3 categories. It must be emphasized that N-QEEG is not a technique that can be substituted for any part of a systematic clinical evaluation, least of all for the process itself, which remains crucial. Once embedded in a solid clinical framework, N-QEEG has the capacity to enhance one’s diagnostic efforts and therapeutic strategies by providing objective quantitative data reflecting brain dysfunction. In such a context, the nominative and cost-effective nature of this technique can further adds to its practicality.

Keywords

neurometricsquantitative EEGcomputer-aided psychiatric diagnosis
Type
Original article
Information
European Psychiatry , Volume 6 , Issue 3 , 1991 , pp. 131 - 139
Copyright
Copyright © Elsevier, Paris 1991

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahn, HPrichep, LJohn, EBaird, HTrepetin, MKaye, H (1980) Developmental equations reflect brain dsyfunction. Science 210, 1259–1262CrossRefGoogle Scholar
Alvarez, APascual, RValdez, P (1987) US EEG developmental equations confirmed for Cuban schoolchildren. Electroencephalogr Clin Neurophysiol 67, 330–332CrossRefGoogle Scholar
Gasser, TBacher, PMochs, J (1982) Transformation towards the normal distribution of broadband spectral parameters of the EEG. Electroencephalogr Clin Neurophysiol 53, 119–124CrossRefGoogle Scholar
Gasser, TMochs, JLenard, HBacher, PVerleger, R (1983) The EEG of mildly retarded children : developmental, classifactory and topographic aspects. Electroencephalogr Clin Neurophysiol 55(151)CrossRefGoogle ScholarPubMed
Harmony, T (1984) Neurometría y maduracion cerebral. Neurol Neurocir Psiquiatr 25(7)Google Scholar
Harmony, T (1988) Psychophysiological evaluation of children’s neuropsychological disorders.In: Handbook of Child Clinical Neuropsychology (Reynolds, C ed) Plenum Press, NY, ch 15, 265–290Google Scholar
John, EKarmel, BCorning, WEaston, PBrown, DAhn, HJohn, MHarmony, TPrichep, LToro, AGerson, IBartlett, FThatcher, RKaye, HValdes, PSchwartz, E(1977) Neurometrics : numerical taxonomy identifies different profiles of brain functions within groups of behaviorally similar people. Science 196, 1383–1410CrossRefGoogle Scholar
John, EAhn, HPrichep, LKaye, HTrepetin, MFridman, J (1981) Neurometric evaluation of EEG in normal, learning disabled and neurologically “at-risk” children.In: Recent Advances in EEG and EMG Data Processing (Yamaguchi, NFujisawa, K ed) Elsevier, Amsterdam, 162–177Google Scholar
John, EHarmony, TValdes-Sosa, ?(1987) The use of statistics in electrophysiology.In: Handbook of Electroencephalography and Clinical Neurophysiology, Vol III, Computer Analysis of the EEG and Other Neurophysiological Signals (Remond, A ed) Elsevier, Amsterdam, 497–540Google Scholar
John, EPrichep, LFridman, JEaston, P (1988a) Neurometrics: computer assisted differential diagnosis of brain dysfunctions. Science 293, 162–169CrossRefGoogle Scholar
John, EPrichep, LFriedman, JEssig-Peppard, T (1988b) Neurometric classification of patients with different psychiatric disorders.In: Statistics and Topography in Quantitative EEG (Samson-Dollfus, D ed) Elsevier, Paris, 88–95Google Scholar
Jonkman, EPoortvliet, DVeering, MdeWeerd, AJohn, E (1985) The use of neurometrics in the study of patients with cerebral ischemia. Electroencephalogr Clin Neurophysiol 51, 333–341CrossRefGoogle Scholar
Mas, FPrichep, LJohn, ELevine, R (1991) Neurometric Q-EEG subtyping of obsessive compulsive disorder.In: Imaging of the Brain in Psychiatry and Related Fields (Maurer, K ed) Springer, VerlagGoogle Scholar
Matousěk, MPetersén, I (1973) Norms for the EEG. In: Automation of Clinical Electroencephalography (Kellaway, PPetersén, I ed) Raven Press, NY, 75–102Google Scholar
Prichep, L (1987) Neurometric quantitative EEG measures of depressive disorders.In: Cerebral Dynamics Laterality and Psychopathology (Takahashi, RFlor-Henry, PGruzelier, JNiwa, S ed) Elsevier Science Publishers, Amsterdam, 55–69Google Scholar
Prichep, LJohn, E (1986) Neurometrics: clinical applications. In: Clinical Applications of Computer Analysis of EEG and Other Neurophysiological Variables, vol 2 :Handbook of Electroencephalography and Clinical Neurophysiology (da Silva, FL van Leeuwen, WSRemond, A eds) Elsevier, Amsterdam, 153–170Google Scholar
Prichep, LJohn, EEssig-Peppard, TAlper, K (1990) Neurometric subtyping of depressive disorders.In: Plasticity and Morphology of the Central Nervous System (Cazzullo, CInvernizzi, GSacchetti, EVita, A eds) MTP Press, LondonGoogle Scholar
Prichep, LMas, FJohn, ELevine, R (1991) Neurometric subtyping of obsessive compulsive disorders.In: Psychiatry: A World Perspective, Vol 1 (Stefanis, CRabavilas, ASoldatos, C eds) Elsevier, Amsterdam, 557–562Google Scholar
Yingling, CGalin, DFein, GPeltzman, DDavenport, L (1986) Neurometrics does not detect “pure” dyslexics. Electroencephalogr Clin Neurophysiol 63, 426–430CrossRefGoogle Scholar
Submit a response

Comments

No Comments have been published for this article.