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.

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The 5-HT receptor is a promising target for cognitive disorders, in particular for Alzheimer's disease (AD). The high affinity and selective 5-HT receptor antagonist idalopirdine (Lu AE58054) is currently in development for mild-moderate AD as adjunct therapy to acetylcholinesterase inhibitors (AChEIs). We studied the effects of idalopirdine alone and in combination with the AChEI donepezil on cortical function using two in vivo electrophysiological methods. Neuronal network oscillations in the frontal cortex were measured during electrical stimulation of the brainstem nucleus pontis oralis (nPO) in the anesthetized rat and by an electroencephalogram (EEG) in the awake, freely moving rat. In conjunction with the EEG study, we investigated the effects of idalopirdine and donepezil on sleep-wake architecture using telemetric polysomnography. Idalopirdine (2 mg/kg i.v.) increased gamma power in the medial prefrontal cortex (mPFC) during nPO stimulation. Donepezil (0.3 and 1 mg/kg i.v.) also increased cortical gamma power and pretreatment with idalopirdine (2 mg/kg i.v.) potentiated and prolonged the effects of donepezil. Similarly, donepezil (1 and 3 mg/kg s.c.) dose-dependently increased frontal cortical gamma power in the freely moving rat and pretreatment with idalopirdine (10 mg/kg p.o.) augmented the effect of donepezil 1 mg/kg. Analysis of the sleep-wake architecture showed that donepezil (1 and 3 mg/kg s.c.) dose-dependently delayed sleep onset and decreased the time spent in both REM and non REM sleep stages. In contrast, idalopirdine (10 mg/kg p.o.) did not affect sleep-wake architecture nor the effects of donepezil. In summary, we show that idalopirdine potentiates the effects of donepezil on frontal cortical gamma oscillations, a pharmacodynamic biomarker associated with cognition, without modifying the effects of donepezil on sleep. The increased cortical excitability may contribute to the procognitive effects of idalopirdine in donepezil-treated AD patients.

Graphical Abstract

A neural network model is presented of novelty detection in the CA1 subdomain of the hippocampal formation from the perspective of information flow. This computational model is restricted on several levels by both anatomical information about hippocampal circuitry and behavioral data from studies done in rats. Several studies report that the CA1 area broadcasts a generalized novelty signal in response to changes in the environment. Using the neural engineering framework developed by Eliasmith et al., a spiking neural network architecture is created that is able to compare high-dimensional vectors, symbolizing semantic information, according to the semantic pointer hypothesis. This model then computes the similarity between the vectors, as both direct inputs and a recalled memory from a long-term memory network by performing the dot-product operation in a novelty neural network architecture. The developed CA1 model agrees with available neuroanatomical data, as well as the presented behavioral data, and so it is a biologically realistic model of novelty detection in the hippocampus, which can provide a feasible explanation for experimentally observed dynamics.