Neuropharmacology of human TERA2.cl.SP12 stem cell-derived neurons in ultra-long-term culture for antiseizure drug discovery.

Modeling the complex and prolonged development of the mammalian central nervous system in vitro remains a profound challenge. Most studies of human stem cell derived neurons are conducted over days to weeks and may or may not include glia. Here we have utilized a single human pluripotent stem cell line, TERA2.cl.SP12 to derive both neurons and glial cells and determined their differentiation and functional maturation over 1 year in culture together with their ability to display epileptiform activity in response to pro-convulsant agents and to detect antiseizure drug actions. Our experiments show that these human stem cells differentiate in vitro into mature neurons and glia cells and form inhibitory and excitatory synapses and integrated neural circuits over 6-8 months, paralleling early human neurogenesis in vivo; these neuroglia cultures display complex electrochemical signaling including high frequency trains of action potentials from single neurons, neural network bursts and highly synchronized, rhythmical firing patterns. Neural activity in our 2D neuron-glia circuits is modulated by a variety of voltage-gated and ligand-gated ion channel acting drugs and these actions were consistent in both young and highly mature neuron cultures. We also show for the first time that spontaneous and epileptiform activity is modulated by first, second and third generation antiseizure agents consistent with animal and human studies. Together, our observations strongly support the value of long-term human stem cell-derived neuroglial cultures in disease modeling and neuropsychiatric drug discovery.

Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks.

The high number of fatal crashes caused by driver drowsiness highlights the need for developing reliable drowsiness detection methods. An ideal driver drowsiness detection system should estimate multiple levels of drowsiness accurately without intervening in the driving task. This paper proposes a multi-level drowsiness detection system by a deep neural network-based classification system using a combination of electrocardiogram and respiration signals. The proposed method is based on a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for classifying drowsiness by concurrently using heart rate variability (HRV), power spectral density of HRV, and respiration rate signal as inputs. Two models, a CNN-based model and a hybrid CNN-LSTM-based model were used for multi-level classifications. The performance of the proposed method was evaluated on experimental data collected from 30 subjects in a simulated driving environment. The performance and the results of both models are presented and compared. The best performance for both three-level and five-level drowsiness classifications was achieved by the CNN-LSTM model. The results indicate that the three-level and five-level classifications of drowsiness can be achieved with 91 and 67% accuracy, respectively.

An Efficient Approach for Driver Drowsiness Detection at Moderate Drowsiness Level Based on Electroencephalography Signal and Vehicle Dynamics Data.

Background: Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data. Methods: A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data. Results: According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier. Conclusion: According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

An Electrophysiological and Pharmacological Study of the Properties of Human iPSC-Derived Neurons for Drug Discovery.

Human stem cell-derived neurons are increasingly considered powerful models in drug discovery and disease modeling, despite limited characterization of their molecular properties. Here, we have conducted a detailed study of the properties of a commercial human induced Pluripotent Stem Cell (iPSC)-derived neuron line, iCell [GABA] neurons, maintained for up to 3 months in vitro. We confirmed that iCell neurons display neurite outgrowth within 24 h of plating and label for the pan-neuronal marker, ßIII tubulin within the first week. Our multi-electrode array (MEA) recordings clearly showed neurons generated spontaneous, spike-like activity within 2 days of plating, which peaked at one week, and rapidly decreased over the second week to remain at low levels up to one month. Extracellularly recorded spikes were reversibly inhibited by tetrodotoxin. Patch-clamp experiments showed that iCell neurons generated spontaneous action potentials and expressed voltage-gated Na and K channels with membrane capacitances, resistances and membrane potentials that are consistent with native neurons. Our single neuron recordings revealed that reduced spiking observed in the MEA after the first week results from development of a dominant inhibitory tone from GABAergic neuron circuit maturation. GABA evoked concentration-dependent currents that were inhibited by the convulsants, bicuculline and picrotoxin, and potentiated by the positive allosteric modulators, diazepam, chlordiazepoxide, phenobarbital, allopregnanolone and mefenamic acid, consistent with native neuronal GABAA receptors. We also show that glycine evoked robust concentration-dependent currents that were inhibited by the neurotoxin, strychnine. Glutamate, AMPA, Kainate and NMDA each evoked concentration-dependent currents in iCell neurons that were blocked by their selective antagonists, consistent with the expression of ionotropic glutamate receptors. The NMDA currents required the presence of the co-agonist glycine and were blocked in a highly voltage-dependent manner by Mg2+ consistent with the properties of native neuronal NMDA receptors. Together, our data suggest that such human iPSC-derived neurons may have significant value in drug discovery and development and may eventually largely replace the need for animal tissues in human biomedical research.

A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles.

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

Adolescent Substance Abuse, Transgenerational Consequences and Epigenetics.

Adolescence is the transitional period between childhood and adulthood and a critical period in brain development. Adolescence in humans is also associated with increased expression of risk-taking behaviors. Epidemiological and clinical studies, for example, show a surge of drug abuse and raise the hypothesis that the adolescent brain undergoes critical changes resulting in diminished control. Determining how substance abuse during this critical period might cause longterm neurobiological changes in cognition and behavior is therefore critically important. The present work aims to provide an evaluation of the transgenerational and multi-generational phenotypes derived from parent animals exposed to drugs of abuse only during their adolescence. Specifically, we will consider changes found following the administration of cannabinoids, nicotine, alcohol and opiates. In addition, epigenetic modifications of the genome following drug exposure will be discussed as emerging evidence of the underlying adverse transgenerational effects. Notwithstanding, much of the new data discussed here is from animal models, indicating that future clinical studies are much needed to better understand the neurobiological consequences and mechanisms of drug actions on the human brains' development and maturation.

Adolescent drug exposure: A review of evidence for the development of persistent changes in brain function.

Over the past decade, many studies have indicated that adolescence is a critical period of brain development and maturation. The refinement and maturation of the central nervous system over this prolonged period, however, makes the adolescent brain highly susceptible to perturbations from acute and chronic drug exposure. Here we review the preclinical literature addressing the long-term consequences of adolescent exposure to common recreational drugs and drugs-of-abuse. These studies on adolescent exposure to alcohol, nicotine, opioids, cannabinoids and psychostimulant drugs, such as cocaine and amphetamine, reveal a variety of long-lasting behavioral and neurobiological consequences. These agents can affect development of the prefrontal cortex and mesolimbic dopamine pathways and modify the reward systems, socio-emotional processing and cognition. Other consequences include disruption in working memory, anxiety disorders and an increased risk of subsequent drug abuse in adult life. Although preventive and control policies are a valuable approach to reduce the detrimental effects of drugs-of-abuse on the adolescent brain, a more profound understanding of their neurobiological impact can lead to improved strategies for the treatment and attenuation of the detrimental neuropsychiatric sequelae.

Adolescent morphine exposure induces immediate and long-term increases in impulsive behavior.

RATIONALE: Adolescence in humans represents a unique and critical developmental time point associated with increased risk-taking behavior. Converging clinical and epidemiological studies report a peak of drug use during adolescence, leading to the hypothesis that the developing adolescents brain is at risk to lose control over drug intake. Both adolescence and drug abuse are associated with significant cognitive and psychological changes such as lack of impulse control. A simple definition for impulsive behavior is the tendency to act prematurely without foresight. Increase in impulsivity is evident in acute morphine consumption, but to date, little is known with respect to subchronic morphine administration in impulsive behavior, particularly comparing time-dependent effects in adults, young adults, and adolescents. METHODS: To evaluate this, adult, young adult, and adolescent rats were treated with a subchronic regimen of morphine or saline during 5 days (s.c.). Thereafter, we examined impulsive behavioral effects of morphine administration, 24 h and 25 days after administration in rats, while responding under a five-choice serial reaction time task (5-CSRTT). RESULTS: Subchronic morphine administration increased premature responding 24 h after the last injection of morphine in adult, young adult, and adolescent rats without increasing motor activity but a significant change in motivation in adult and young adult rats only. After 25 days of abstinence, premature responses were significantly increased in comparison with baseline in adolescent rats but not in adults and young adults. CONCLUSION: The main conclusion of this study is that morphine exposure in adolescents has a long-term profound effect on motor impulsive behavior later in adulthood. An implication of our findings might be that we should be especially careful about consuming and prescribing opioid drugs in adolescents.

Chronic adolescent morphine exposure alters the responses of lateral paragigantocellular neurons to acute morphine administration in adulthood.

Accumulating evidence support the growing non-medical use of morphine during adolescence. Despite this concern which has recently been addressed in some studies, cellular mechanisms underlying the long-term neurobiological and behavioral effects of opiate exposure during this critical period have still remained largely unexplored. Several reports have proposed that subtle long-lasting neurobiological alterations might be triggered by exposure to opiate derivatives or drugs of abuse particularly when this occurs during a critical phase of brain maturation such as adolescence. The present study was designed to investigate how chronic adolescent morphine exposure could affect the responsiveness of lateral paragigantocellular (LPGi) neurons to acute morphine administration in adult rats. Male Wistar rats received chronic escalating morphine or saline during adolescence (30-39d) for 10 days. During adulthood (65d), the extracellular unit activities of LPGi neurons were recorded in urethane-anesthetized animals. Results indicated that adolescent morphine treatment enhances the baseline activity of LPGi neurons. In addition, morphine-induced inhibition of spontaneous discharge rate was potentiated in adult rats received morphine during adolescence. However, this pretreatment did not affect the extent of morphine excitatory effect, onset or peak of cellular response and regularity of unit discharge in LPGi neurons. Our study supports the hypothesis that adolescent morphine exposure induces long-lasting neurophysiological alterations in brain regions known to play a role in mediating opiate effects. This finding sheds light on the possible effect of opiate pre-exposure on addiction susceptibility in future.

Adolescent chronic escalating morphine administration induces long lasting changes in tolerance and dependence to morphine in rats.

Adolescence is a gradual period of transition from childhood to adulthood. It is considered as a sensitive developmental time point that long lasting changes occur in the brain. The present study examined adolescent chronic escalating morphine administration on morphine tolerance and dependence in adulthood. Adolescent male Wistar rats (30days old) were administered increasing doses of morphine (2.5 to 25mg/kg, s.c.) every 12h, for 10days. Control rats received saline according to the same protocol. Thereafter, during adulthood (65-75days old), tolerance to antinociceptive effect of morphine was induced by subcutaneous injection of 3mg/kg morphine, once a day for 7days. Morphine analgesia was measured in the animals by tail flick test every two days, 10min before and 30min after morphine administration. Also, in another test, adult rats were administered morphine (10mg/kg, s.c.) twice a day for 9days to become morphine dependent. On day 10, naloxone (2mg/kg, i.p.) was injected 2h after morphine administration. Somatic signs of morphine withdrawal were then recorded in a clear Plexiglas test chamber for 25min. Results showed that adolescent morphine treatment significantly facilitates the development of tolerance to the analgesic effect of morphine and increases morphine withdrawal signs (grooming, head tremor, sniffing, scratching and teeth chattering) in adulthood compared to the saline group. Facilitation of morphine tolerance and potentiation of withdrawal signs suggests that chronic escalating morphine treatment during adolescence causes long-lasting effects on development of morphine tolerance and dependence in adulthood.