title: Statistical Decision Tree: a tool for studying pharmaco-EEG effects of CNS-active drugs.
abstract: Quantitative pharmaco-EEG has become a useful technique for determining pharmacodynamic parameters after CNS-active drug administration. Nevertheless, one of the most important problems faced by practitioners of pharmaco-EEG is the difficulty in evaluating drug-specific effects. In this article, a methodology for comparing two time sequences of pharmacodynamic measurements, the Statistical Decision Tree (SDT), is proposed. This methodology, based on one- and multi-dimensional Wilcoxon signed-rank tests on EEG variables, takes into account vigilance fluctuations and placebo effects in order to pick out effects specifically due to the drug.
authors.affiliation: Formation pour la Recherche en Neurosciences Appliquées à la Psychiatrie (FORENAP), Centre Hospitalier de Rouffach, France.
authors.forename: K T
authors.initials: KT
authors.lastname: Dago
authors.forename: R
authors.initials: R
authors.lastname: Luthringer
authors.forename: R
authors.initials: R
authors.lastname: Lengellé
authors.forename: G
authors.initials: G
authors.lastname: Rinaudo
authors.forename: J P
authors.initials: JP
authors.lastname: Macher
mesh_terms: D002491:Central Nervous System Agents / Q000494:pharmacology*; D003663:Decision Trees; D004569:Electroencephalography / Q000187:drug effects*; D006801:Humans
chemical_list: D002491:Central Nervous System Agents