Differences between Physostigmine- and Yohimbine-induced States Are Visualized in Canonical Space Constructed from EEG during Natural Sleep-wake Cycle in Rats.
- Author:
Maan Gee LEE
1
;
Minji KIM
;
Mootaek ROH
;
Il Sung JANG
;
Seung Hee WON
Author Information
- Publication Type:Original Article
- Keywords: EEG; power spectral analysis; factor analysis; canonical correlation analysis; physostigmine; yohimbine
- MeSH: Animals; Electroencephalography; Factor Analysis, Statistical; Physostigmine; Rats; Yohimbine
- From:Experimental Neurobiology 2011;20(1):54-65
- CountryRepublic of Korea
- Language:English
- Abstract: Although quantitative EEG parameters, such as spectral band powers, are sensitive to centrally acting drugs in dose- and time-related manners, changes of the EEG parameters are redundant. It is desirable to reduce multiple EEG parameters to a few components that can be manageable in a real space as well as be considered as parameters representing drug effects. We calculated factor loadings from normalized values of eight relative band powers (powers of 0.5, 1.0~2.0, 2.5~4.0, 4.5~5.5, 6.0~8.0, 8.5~12.0, 12.5~24.5, and 25~49.5 Hz bands expressed as ratios of the power of 0.5-49.5 Hz band) of EEG during pre-drug periods (11:00~12:00) by factor analysis and constructed a two-dimensional canonical space (reference canonical space) by canonical correlation analysis. Eight relative band powers of EEG produced by either physostigmine or yohimbine were reduced to two canonical scores in the reference canonical space. While changes of the band powers produced by physostigmine and yohimbine were too redundant to describe the difference between two drugs, locations of two drugs in the reference canonical space represented the difference between two drug's effects on EEG. Because the distance between two locations in the canonical space (Mahalanobis distance) indicates the magnitude of difference between two different sets of EEG parameters statistically, the canonical scores and the distance may be used to quantitatively and qualitatively describe the dose-dependent and time-dependent effects and also tell similarity and dissimilarity among effects. Then, the combination of power spectral analysis and statistical analysis may help to classify actions of centrally acting drugs.
