Document Detail

Extracting drug mechanism and pharmacodynamic information from clinical electroencephalographic data using generalised semi-linear canonical correlation analysis.
MedLine Citation:
PMID:  25402261     Owner:  NLM     Status:  Publisher    
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.
P Brain; F Strimenopoulou; A Diukova; E Berry; A Jolly; J E Hall; R G Wise; M Ivarsson; F J Wilson
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-17
Journal Detail:
Title:  Physiological measurement     Volume:  35     ISSN:  1361-6579     ISO Abbreviation:  Physiol Meas     Publication Date:  2014 Nov 
Date Detail:
Created Date:  2014-11-17     Completed Date:  -     Revised Date:  2014-11-18    
Medline Journal Info:
Nlm Unique ID:  9306921     Medline TA:  Physiol Meas     Country:  -    
Other Details:
Languages:  ENG     Pagination:  2459-2474     Citation Subset:  -    
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