Research reportPart III: Principal component analysis: bridging the gap between strain, sex and drug effects

- Data from Long-Evans and Wistar rats exposed to THC during adolescence.
- Adult rats were tested in behavioural tasks and estimates of brain volume.
- Used principal component analysis to identify factors contributing to the variance.
- Strain and early life experience accounts for the largest proportion of the variance.
- THC had negligible effects on the variance.

Previous work has identified the adolescent period as particularly sensitive to the short- and long-term effects of marijuana and its main psychoactive component Δ9-tetrahydrocannabinol (THC). However, other studies have identified certain backgrounds as more sensitive than others, including the sex of the individual or the strain of the rat used. Further, the effects of THC may be specific to certain behavioural tasks (e.g. measures of anxiety), and the consequences of THC are not seen equally across all behavioural measures. Here, data obtained from adolescent male and female Long-Evans and Wistar rats exposed to THC and tested as adults, which, using standard ANOVA testing, showed strain- and sex-specific effects of THC, was analyzed using principal component analysis (PCA). PCA allowed for the examination of the relative contribution of our variables of interest to the variance in the data obtained from multiple behavioural tasks, including the skilled reaching task, the Morris water task, the discriminative fear-conditioning to context task, the elevated plus maze task and the conditioned place preference task to a low dose of amphetamine, as well as volumetric estimates of brain volumes and cfos activation. We observed that early life experience accounted for a large proportion of variance across data sets, although its relative contribution varied across tasks. Additionally, THC accounted for a very small proportion of the variance across all behavioural tasks. We demonstrate here that by using PCA, we were able to describe the main variables of interest and demonstrate that THC exposure had a negligible effect on the variance in the data set.