Topography of Background EEG Rhythms in Normal Subjects and in Patients with Cerebrovascular Disorders
Abstract
The purpose of this work is to demonstrate that it is useful to study very carefully EEG mapping of background alpha, delta, theta and beta rhythms in patients with supratentorial ischemia and with a normal or subnormal EEG on visual assessment. In normal populations, there is now general agreement that the differences between young and healthy elderly people are very slight. These small differences can be expressed by various methods: multivariate analysis (John 1981; Kopruner and Pfurtscheller 1984; Jonkman et al. 1985; Senant et al. 1966a, b) and topographic EEG mapping (Duffy 1984) are now the most commonly used. To compare normal subjects and patients with brain ischemia, we have chosen to use brain electrical activity mapping. Jonkman et al. (1985), Kopruna and Pfurtscheller (1984), Van Huffelen et al. (1984), Pfurtscheller et al. (1981) and Tolonen et al. (1981) used quantitative EEG in patients with cerebrovascular diseases, but did not at that time use mapping to express their results.
Keywords
Carotid Stenosis Alpha Rhythm Healthy Elderly People Asymptomatic Carotid Stenosis Delta BandPreview
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References
- Duffy FH, Albert MS, McAnulty G, Garvez AJ (1984) Age-related differences in brain electrical activity of healthy subjects. Ann Neurol 16:430–438PubMedCrossRefGoogle Scholar
- John ER (1981) Neurometric evaluation of brain dysfunction related to learning disorders. Acta Neurol Scand [Suppl] 64(89): 87–100CrossRefGoogle Scholar
- Jonkman EJ, Poortvliet DCJ, Veering MM, De Weerd AW, John ER (1985) The use of neurometries in the study of patients with cerebral ischemia. Electroencephalogr Clin Neu-rophysiol 61:333–341CrossRefGoogle Scholar
- Kopruner V, Pfurtscheller G (1984) Multiparametric asymmetry score (MAS) - distinction between normal and ischaemic brains. Electroencephalogr Clin Neurophysiol 57:343–346PubMedCrossRefGoogle Scholar
- Matousek J, Volavka J, Roubicek, Roth Z (1967) EEG frequency analysis related to age in normal adults. Electroencephalogr clin Neurophysiol 23:162–167PubMedCrossRefGoogle Scholar
- Pfurtscheller G, Sager W, Wege W (1981) Correlations between CT scan and sensorimotor EEG rhythms in patients with cerebrovascular disorders. Electroencephalogr Clin Neurophysiol 52:473–485PubMedCrossRefGoogle Scholar
- Senant J, Delapierre G, Samson-Dollfus D, Tsouria Z, Bertoldi I (1986 a) Analyse spectrale de-électroencéphalogramme au cours du vieillissement. Evolution de différents paramètres. In: Court L, Trocherie S, Doucet C (eds) Le traitement du signal en électrophysiologic expérimentale et clinique du systemè nerveux central. Imprimerie Lefranc, Candé, pp 210–219Google Scholar
- Senant J, Samson-Dollfus D, Delapierre G, Menard JF, Bertoldi-Lefever I (1986 b) Analyse automatique de l’eléctroencéphalogramme et vieillissement chez des subjets normaux et vasculaires. Sem Hôp Paris 62:3505–3509Google Scholar
- Tolonen U, Ahonen A, Sulg I A, Kuikka J, Kallanrata T, Koskinen M, Hokkanen E (1981) Serial measurements of quantitative EEG and cerebral blood flow and circulation time after brain infarction. Acta Neurol Scand 63:145–155PubMedCrossRefGoogle Scholar
- Van Huffelen AC, Poortvliet DCJ, van der Wulp CJM (1984) Quantitative electroencephalography in cerebral ischemia. Detection of abnormalities in “normal” EEGs. In: Pfurtscheller G, Jonkman EJ, Lopez DA, Silva FH (eds) Quantitative EEG and imaging techniques. Brain ischemia. Elsevier, Amsterdam, pp 3–28CrossRefGoogle Scholar