Sub-anesthetic doses of ketamine increase single cell entrainment in the rat auditory cortex during auditory steady-state response.

BACKGROUND: Understanding the effects of the N-methyl-D-aspartate receptor (NMDA-R) antagonist ketamine on brain function is of considerable interest due to the discovery of its fast-acting antidepressant properties. It is well known that gamma oscillations are increased when ketamine is administered to rodents and humans, and increases in the auditory steady-state response (ASSR) have also been observed. AIMS: To elucidate the cellular substrate of the increase in network activity and synchrony observed by sub-anesthetic doses of ketamine, the aim was to investigate spike timing and regularity and determine how this is affected by the animal's motor state. METHODS: Single unit activity and local field potentials from the auditory cortex of awake, freely moving rats were recorded with microelectrode arrays during an ASSR paradigm. RESULTS: Ketamine administration yielded a significant increase in ASSR power and phase locking, both significantly modulated by motor activity. Before drug administration, putative fast-spiking interneurons (FSIs) were significantly more entrained to the stimulus than putative pyramidal neurons (PYRs). The degree of entrainment significantly increased at lower doses of ketamine (3 and 10 mg/kg for FSIs, 10 mg/kg for PYRs). At the highest dose (30 mg/kg), a strong increase in tonic firing of PYRs was observed. CONCLUSIONS: These findings suggest an involvement of FSIs in the increased network synchrony and provide a possible cellular explanation for the well-documented effects of ketamine-induced increase in power and synchronicity during ASSR. The results support the importance to evaluate different motor states separately for more translational preclinical research.

Activity-State Dependent Reversal of Ketamine-Induced Resting State EEG Effects by Clozapine and Naltrexone in the Freely Moving Rat.

Ketamine is a non-competitive N-Methyl-D-aspartate receptor (NMDAR) antagonist used in the clinic to initiate and maintain anaesthesia; it induces dissociative states and has emerged as a breakthrough therapy for major depressive disorder. Using local field potential recordings in freely moving rats, we studied resting state EEG profiles induced by co-administering ketamine with either: clozapine, a highly efficacious antipsychotic; or naltrexone, an opioid receptor antagonist reported to block the acute antidepressant effects of ketamine. As human electroencephalography (EEG) is predominantly recorded in a passive state, head-mounted accelerometers were used with rats to determine active and passive states at a high temporal resolution to offer the highest translatability. In general, pharmacological effects for the three drugs were more pronounced in (or restricted to) the passive state. Specifically, during inactive periods clozapine induced increases in delta (0.1-4 Hz), gamma (30-60 Hz) and higher frequencies (>100 Hz). Importantly, it reversed the ketamine-induced reduction in low beta power (10-20 Hz) and potentiated ketamine-induced increases in gamma and high frequency oscillations (130-160 Hz). Naltrexone inhibited frequencies above 50 Hz and significantly reduced the ketamine-induced increase in high frequency oscillations. However, some frequency band changes, such as clozapine-induced decreases in delta power, were only seen in locomoting rats. These results emphasise the potential in differentiating between activity states to capture drug effects and translate to human resting state EEG. Furthermore, the differential reversal of ketamine-induced EEG effects by clozapine and naltrexone may have implications for the understanding of psychotomimetic as well as rapid antidepressant effects of ketamine.

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

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 5HT2A 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.

The 5-HT3 receptor antagonist ondansetron potentiates the effects of the acetylcholinesterase inhibitor donepezil on neuronal network oscillations in the rat dorsal hippocampus.

Cognitive impairments in Alzheimer's disease (AD) have been associated with alterations in neuronal oscillatory activity, of which hippocampal theta and gamma oscillations are essential for the coordination of neuronal networks during cognitive functions. Cognitive deterioration in AD is delayed by symptomatic treatment with donepezil and other acetylcholinesterase inhibitors (AChEIs). However, the efficacy of symptomatic monotherapy is insufficient. Combining 5-HT receptor antagonists with AChEIs represents a promising new approach for symptomatic treatment of AD. The selective 5-HT3 receptor antagonist ondansetron decreases the activity of interneurons with a concomitant increase in the activity of pyramidal neurons in the hippocampus of freely moving rats. Additionally, 5-HT3 receptor antagonism modulates acetylcholine release in rat cortex and hippocampus. We investigated the effects of ondansetron alone and in combination with donepezil on hippocampal oscillations using in vivo electrophysiology. Neuronal network oscillations were recorded in the dorsal hippocampus during electrical stimulation of the brainstem pedunculopontine tegmental nucleus in urethane-anaesthetised rats. In addition, potential pharmacokinetic interactions between donepezil and ondansetron were assessed. Ondansetron alone did not affect hippocampal network oscillations. Donepezil dose-dependently increased hippocampal theta and gamma power during PPT stimulation. Ondansetron (0.3 mg/kg, i.v.) potentiated theta and gamma responses to 0.2 mg/kg donepezil and prolonged theta and gamma responses to 0.3 mg/kg donepezil. These effects could not be attributed to pharmacokinetic interactions between the compounds. This study demonstrates that ondansetron potentiates the effects of donepezil on elicited neuronal oscillations and suggests that 5-HT3 receptor antagonists may be beneficial as adjunctive therapy to AChEIs for the symptomatic treatment of cognitive deficits in AD.

The 5-HT6 receptor antagonist idalopirdine potentiates the effects of donepezil on gamma oscillations in the frontal cortex of anesthetized and awake rats without affecting sleep-wake architecture.

The 5-HT6 receptor is a promising target for cognitive disorders, in particular for Alzheimer's disease (AD). The high affinity and selective 5-HT6 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.

Vector Symbolic Spiking Neural Network Model of Hippocampal Subarea CA1 Novelty Detection Functionality.

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.

Analysis of Drug Design for a Selection of G Protein-Coupled Neuro- Receptors Using Neural Network Techniques.

A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors µ-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drugdesign. This is done by training on structural features, selected using a "minimum redundancy, maximum relevance"-test, and testing for successful prediction of categorized binding strength. An extensive comparison of the neural network performances was made in order to select the optimal architecture. Deep belief networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew`s coefficient of 0.925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical characteristics could give the lowest observed IC50 values, meaning largest bio-effect pr. nM substance, around 0.03-0.06 nM. These ligand characteristics could be total number of atoms, their types etc. In conclusion, deep belief networks trained on drug-molecule structures were demonstrated as powerful computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques.