Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Extracting drug mechanism and pharmacodynamic information from clinical electroencephalographic data using generalised semi-linear canonical correlation analysis

Brain, P., Strimenopoulou, F., Diukova, A., Berry, E., Jolly, A., Hall, J. E., Wise, Richard Geoffrey, Ivarsson, M. and Wilson, F. J. 2014. Extracting drug mechanism and pharmacodynamic information from clinical electroencephalographic data using generalised semi-linear canonical correlation analysis. Physiological Measurement 35 (12) , pp. 2459-2474. 10.1088/0967-3334/35/12/2459

Full text not available from this repository.

Abstract

Conventional analysis of clinical resting electroencephalography (EEG) recordings typically involves assessment of spectral power in pre-defined frequency bands at specific electrodes. EEG is a potentially useful technique in drug development for measuring the pharmacodynamic (PD) effects of a centrally acting compound and hence to assess the likelihood of success of a novel drug based on pharmacokinetic-pharmacodynamic (PK-PD) principles. However, the need to define the electrodes and spectral bands to be analysed a priori is limiting where the nature of the drug-induced EEG effects is initially not known. We describe the extension to human EEG data of a generalised semi-linear canonical correlation analysis (GSLCCA), developed for small animal data. GSLCCA uses data from the whole spectrum, the entire recording duration and multiple electrodes. It provides interpretable information on the mechanism of drug action and a PD measure suitable for use in PK-PD modelling. Data from a study with low (analgesic) doses of the μ-opioid agonist, remifentanil, in 12 healthy subjects were analysed using conventional spectral edge analysis and GSLCCA. At this low dose, the conventional analysis was unsuccessful but plausible results consistent with previous observations were obtained using GSLCCA, confirming that GSLCCA can be successfully applied to clinical EEG data.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Neuroscience and Mental Health Research Institute (NMHRI)
Psychology
Subjects: R Medicine > R Medicine (General)
Publisher: IOP Publishing: Hybrid Open Access
ISSN: 0967-3334
Last Modified: 09 Jan 2018 17:44
URI: http://orca.cf.ac.uk/id/eprint/74802

Citation Data

Cited 2 times in Google Scholar. View in Google Scholar

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Cited 1 time in Web of Science. View in Web of Science.

Actions (repository staff only)

Edit Item Edit Item