Pharmaco-electroencephalographic responses in the rat differ between active and inactive locomotor states

Abstract

Quantitative electroencephalography from freely moving rats is commonly used as a translational tool for predicting drug-effects in humans. We hypothesized that drug-effects may be expressed differently depending on whether the rat is in active locomotion or sitting still during recording sessions, and proposed automatic state-detection as a viable tool for estimating drug-effects free of hypo-/hyperlocomotion-induced effects. We aimed at developing a fully automatic and validated method for detecting two behavioural states: active and inactive, in one-second intervals and to use the method for evaluating ketamine, DOI, d-cycloserine, d-amphetamine, and diazepam effects specifically within each state. The developed state-detector attained high precision with more than 90% of the detected time correctly classified, and multiple differences between the two detected states were discovered. Ketamine-induced delta activity was found specifically related to locomotion. Ketamine and DOI suppressed theta and beta oscillations exclusively during inactivity. Characteristic gamma and high-frequency oscillations (HFO) enhancements of the NMDAR and 5HT_2A modulators, speculated associated with locomotion, were profound and often largest during the inactive state. State-specific analyses, theoretically eliminating biases from altered occurrence of locomotion, revealed only few effects of d-amphetamine and diazepam. Overall, drug-effects were most abundant in the inactive state. In conclusion, this new validated and automatic locomotion state-detection method enables fast and reliable state-specific analysis facilitating discovery of state-dependent drug-effects and control for altered occurrence of locomotion. This may ultimately lead to better cross-species translation of electrophysiological effects of pharmacological modulations.

Keywords: DOI; amphetamine; d-cycloserine; diazepam; ketamine.

Figure 1

Schematic showing the steps of the development of the locomotive state detection algorithm. Video recording of rats was used to obtain labelled data from manual scoring as well as a mobility signal using EthoVision XT. Labelled data were obtained by having an experienced technician mark the times of state‐change, t _a and t _p, through time‐registrated keyboard presses. The times of state‐change were converted to a continuous discrete signal coding for behaviour where 1‐s before each registration of state‐change was marked with NaN‐values to exclude uncertain transition periods in the subsequent training of the classifier. The developed state‐detection algorithm was based on features of the mobility signal, μ, obtained with EthoVision software which reflected the change in the detected (yellow) area of the rat over video‐frames. A range of features of μ were calculated in windows of 7 s moving in steps of 1 s, and four features were selected for use in the classification. The 32 recordings (manual scoring and feature signals) were shortened to have equal representation of both states before training the classifier. The probability for active state, Pr(A), (and implicitly inactive state) given the features was predicted using binomial logistic regression, and two probability‐thresholds, thr_A and thr_I, were used to assign samples to the active state when Pr(A) > thr_A and the inactive state when Pr(A) < thr_I. Thresholds were selected by sweeping through values from zero to one in steps of 0.01 and subsequently choosing thr_A‐ and thr_I‐values closest to 0.5 for which the resultant state‐detection precisions (based on fourfold cross‐validation) were above 90%. Finally, the logistic regression parameters were estimated based on all 32 recordings. The classification performance of the whole model building procedure was tested using double‐cross‐validation. [Colour figure can be viewed at http://www.wileyonlinelibrary.com]

Figure 2

(a) Example of the manual scoring (activity and inactive periods shown in red and green, respectively) shown alongside the automatic locomotive state classification (activity and inactive periods shown in purple and light blue, respectively). (b) Power spectra (average over 32 rats from left medial prefrontal cortex) of recordings obtained during active (red graph) and inactive (blue graph) states scored manually (left) and using the automatic LS detection (right). (c) Standard error of the mean (SEM) given as the shaded areas above and below the graphs and significant differences between active and inactive states are marked above the graph when p < 0.05 (*), 0.01 (**), and 0.001 (***). [Colour figure can be viewed at http://www.wileyonlinelibrary.com]

Figure 3

Drug effects on locomotion and LFP power spectra from right medial prefrontal cortex. (a) Effects of ketamine (Ket), d‐cycloserine (DCS), DOI, amphetamine (Amp), and diazepam (Dia) on time spent in the active and inactive state (on average over rats) as determined by the automatic LS detector. Standard error of the mean (SEM) given as the shaded areas above and below the graphs. Time‐intervals where drug‐induced changes differ from saline are marked above the graphs when p < 0.05 (*), 0.01 (**), or 0.001 (***). (b) Power spectra based on 10‐min time intervals after injection for Ket (30–40 min after), DCS (80–90 min after), and for DOI, Dia, and Amp (70–80 min after) calculated from active state, inactive state, and total data. The power spectra are baseline‐corrected and averaged over rats, and shaded areas mark the SEM. (c) Heatplots showing averaged baseline‐corrected power spectra during inactivity for each 10‐min time interval of the recordings in mPFCright for administrations of the large doses of ketamine and DCS as well as DOI. The two black lines in the heatplots frame in the time‐intervals shown in part B. [Colour figure can be viewed at http://www.wileyonlinelibrary.com]

Figure 4

Effects of the highest doses of ketamine (Ket), d‐cycloserine (DCS) and DOI in the delta, theta, beta, low gamma, high gamma, and HFO frequency bands across the two hours of recording after injection in right medial prefrontal cortex on average over rats. Standard error of the mean (SEM) given as the shaded areas above and below the graphs. Significant differences to saline are shown in the boxes below the graphs when p < 0.05 (*), 0.01 (**), or 0.001 (***). [Colour figure can be viewed at http://www.wileyonlinelibrary.com]