Deep learning-based i-EEG classification with convolutional neural networks for drug-target interaction prediction

Drug-target interaction (DTI) prediction has become a foundational task in drug repositioning, polypharmacology, drug discovery, as well as drug resistance and side-effect prediction. DTI identification using machine learning is gaining popularity in these research areas. Through the years, numerous deep learning methods have been proposed for DTI prediction. Nevertheless, prediction accuracy and efficiency remain key challenges. Pharmaco-electroencephalogram (pharmaco-EEG) is considered valuable in the development of central nervous system-active drugs. Quantitative EEG analysis demonstrates high reliability in studying the effects of drugs on the brain. Earlier preclinical pharmaco-EEG studies showed that different types of drugs can be classified according to their mechanism of action on neural activity. Here, we propose a convolutional neural network for EEG-mediated DTI prediction. This new approach can explain the mechanisms underlying complicated drug actions, as it allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs

Aversion-related effects of kappa-opioid agonist U-50488 on neural activity and functional connectivity between amygdala, ventral tegmental area, prefrontal cortex, hippocampus, and nucleus accumbens

Introduction: Among the various receptor systems in the brain, the opioid receptors have been the subject of extensive research due to their integral role in pain modulation, reward processing, and emotional regulation. The kappa-opioid receptor (KOR) system, in particular, stands apart due to its unique contribution to stress response, aversive behaviors, and dysphoric states. This paper aims to provide an understanding of the neural activity underlying the aversion-associated effects of the KOR agonist U-50488. Materials and Methods: Rats underwent stereotaxic surgery to implant electrodes into the amygdala, ventral tegmental area, prefrontal cortex, hippocampus, and nucleus accumbens. The rats were subjected to conditioned place preference test to measure aversion to U-50488. After that, local field potential (LFP) recordings were made. LFP data were processed and analyzed using spectral and coherence analysis methods. A stepwise multiple linear regression was employed to identify the LFP features most significantly correlated with aversion to U-50488. Results: The administration of U-50488 resulted in significant changes in LFP signals across multiple brain regions. These changes were particularly notable in the theta, gamma, and delta bands of brain waves (p<0.05). Theta and gamma activities were especially sensitive to the effects of U-50488. Connectivity calculations revealed shifts in coherence between brain regions, particularly highlighting the amygdala's involvement. While changes were also observed in the ventral tegmental area, prefrontal cortex, hippocampus, and nucleus accumbens (p<0.05), they contributed less to aversion. Using the stepwise multiple linear regression method, we established a final model with the 3 most significant variables: (1) coherence between the amygdala and medial prefrontal cortex, (2) coherence between the amygdala and hippocampus, and (3) theta power in the amygdala. Conclusion: Overall, the data provided insights into how electrical neural activity mediates aversion in response to KOR activation. The results showed that the severity of aversion can be reasonably predicted (r = 0.72±0.02, p = 0.0099) using LFP band power and functional connectivity data. We concluded that the amygdala is a brain region that contributes the most to the KOR agonist-induced aversion