Drowsy EEG Research Articles

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network.

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

Investigating the role of data preprocessing, hyperparameters tuning, and type of machine learning algorithm in the improvement of drowsy EEG signal modeling

• Electroencephalography measured driver drowsiness in simulator tests. • Driver drowsiness was classified with four modeling approaches. • Six algorithms of DT, ET, KNN, MLP, RF, and SVC were applied to the dataset. • The type of algorithm is more effective than the modeling approach. • Tree-based ensemble algorithms are more appropriate for drowsy EEG signal modeling. Driver drowsiness leads to fatal road traffic accidents. The effects of drowsy driving on electroencephalogram (EEG) signal are well visible. Accordingly, classifying EEG signal with machine learning (ML) is known as a reliable and accurate drowsiness detection technique. The type of ML algorithm, data preprocessing, and tuning the hyperparameters influence classification results greatly. In this paper, the drowsy EEG signal of 20 participants was measured in simulator tests. Six ML algorithms of decision tree, extra trees, K-nearest neighbors, multi-layer perceptron, random forest, and support vector classification were trained and cross-validated with different approaches to understand how data preprocessing and tuning the ML hyperparameters can improve drowsy EEG signal modeling. Results indicated that the type of ML algorithm has a more notable effect on modeling accuracy and modeling error than the modeling approach. Data preprocessing generally improved modeling results. But tuning the hyperparameters with the random search method wasn't helpful. Comparison of algorithms showed that tree-based ensemble algorithms (extra trees and random forest) were the most accurate models. They are more practical for real-time applications of drowsy EEG signal modeling.

Driver Drowsiness Detection Using Machine Learning

In today’s world, the number of road accidents that occur is increasing very rapidly. Some of these road accidents are minor whereas some of them may seriously injure people or even take their lives. These road accidents may involve collision of vehicles with each other or crashing the vehicles on to the buildings or others. Road accidents may even result in the death of people. It is estimated that the total deaths due to road accidents in India is around 1,50,000 per year which is approximately 400 accidents per day. Almost 3 lives are lost in every 10 minutes due to road accidents. one of the main factors that lead to road accidents are fatigue and drowsiness. Some popular methods that detect drowsiness use ECG and EEG, which are very complex. Even though these approaches are very precise, they require human involvement and have limitations that make them unsuitable for real-time driving. We’re working on a technique to detect drowsiness in driver by monitoring the eyes and mouth while they’re driving. The proposed method uses python along with two libraries OpenCV and dlib for facial landmark detection. A mathematical values Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) is then calculated for determining whether the eyes are closed or not and weather the person is Yawning, The proposed systems detects these signs and plays an alarm.

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network Computer Methods and Programs in Biomedicine Manuscript No. (Will Be Inserted by the Editor)

Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable Network Computer Methods and Programs in Biomedicine Manuscript No. (Will Be Inserted by the Editor)

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

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. 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. 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. According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

Acute encephalitis in pediatric multisystem inflammatory syndrome associated with COVID-19.

ObjectiveTo characterize neurological involvement in multisystem inflammatory syndrome in children (MIS-C) related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.MethodsRetrospective analysis of the clinical, electroencephalographic, CSF and neuroradiological parameters recorded in seven children (3 males, aged 3–10 years) affected by MIS-C with acute neurological involvement.ResultsAll cases presented acute encephalopathy with drowsiness, irritability, mood deflection and diffuse EEG slowing with periodic posterior complexes. Focal neurological signs, normal brain MRI and CSF, were present in four patients; these patients received intravenous methylprednisolone at 30 mg/kg/day for 3 days. In all cases, the clinical picture rapidly improved in the first three days, and all neurological symptoms and EEG abnormalities disappeared within 10 and 30 days respectively. The severity and duration of the EEG abnormalities was proportional to the extent of the neurological involvement.ConclusionsPatients with MIS-C may present acute encephalitis characterized by rapid-onset encephalopathy and EEG abnormalities (slow wave activity and/or epileptic abnormalities), in some cases associated with focal neurological signs that disappear with immunomodulatory therapy. The detection through neurological evaluation of sentinel neurological signs and distinctive EEG patterns documentable at disease onset will allow timely diagnosis and treatment of these cases.

Feature extraction method for classification of alertness and drowsiness states EEG signals

Drowsy driving is one of the major causes of road accidents. The road accidents can be avoided by the discrimination of alertness and drowsiness states of the drives. The neurological changes in alertness and drowsiness states can be asses by electroencephalogram (EEG) signals. In this paper, the non-stationary characteristic of the EEG signal is explored by tunable Q-factor wavelet transform (TQWT). TQWT decomposes the EEG signal into sub-bands, which further used for the extraction of features. Statistical features of the Hjorth mobility such as minimum value, maximum value, mean and standard deviation (SD) are used for characterization of the alertness and drowsiness states. Various classifiers such as decision tree, logistic regression, fine Gaussian support vector machine, weighted KNN, ensemble boosted trees and extreme learning machine (ELM) are considered. The alertness and drowsiness EEG signals discriminative performance of TQWT-based features are assessed by the Kruskal-Wallis (KW) test. The results of KW-test show that the proposed features are effectively discriminative of the alertness and drowsiness states. According to the obtained results, the best accuracy score of 91.842% is produced by the ELM classifier.

Normal Awake, Drowsy, and Sleep EEG Patterns That Might Be Overinterpreted as Abnormal.

Knowledge of normal patterns is essential for correct EEG interpretation. The overinterpretation of EEG (i.e., ascribing abnormality to EEG patterns that are not associated with disease) is a common problem and can contribute to misdiagnosis and mismanagement. Here, the authors concisely review normal patterns that might be improperly interpreted as abnormal. These include posterior slow waves of youth, central theta, K complexes, asymmetric sleep spindles, hypnagogic and hypnopompic hypersynchrony, arousal patterns, rhythmic midtemporal theta of drowsiness, and the wicket rhythm. Recognition of these patterns will lead to greater accuracy in EEG interpretation and help avoid incorrect management.

Drowsiness Detection Using Adaptive Hermite Decomposition and Extreme Learning Machine for Electroencephalogram Signals

Automatic drowsiness detection system plays a vital role to prevent the road accidents caused by drowsiness. In this regard, the electroencephalogram (EEG) signal provides valuable information of brain physiology for detection of drowsiness. EEG signals exhibit non-stationary nature which is tough to explore by prior defined and fixed number of basis functions. In this paper, an adaptive Hermite decomposition is proposed for the detection of drowsiness EEG signals. In the proposed decomposition, Hermite functions are used as basis functions, which are adaptively selected for each EEG signal by evolutionary optimization algorithms (EOAs). The mean square error of decomposition is proposed as an objective function to EOAs. The minimum decomposition error provided artificial bee colony EOA is considered to Hermite coefficients-based feature extraction for EEG signals. The extracted features are tested with the extreme learning machine (ELM), decision tree, $k$ -nearest neighbor, least squares support vector machine, artificial neural network, and naive Bayes for detection of alertness and drowsiness EEG signals. The proposed method with ELM classifier obtained 95.45% and 87.92% detection rates for alertness and drowsiness states, respectively, which are better as compared to other existing methods.

Comparison between human awake, meditation and drowsiness EEG activities based on directed transfer function and MVDR coherence methods.

This study examined the electroencephalogram functional connectivity (coherence) and effective connectivity (flow of information) of selected brain regions during three different attentive states: awake, meditation and drowsiness. For the estimation of functional connectivity (coherence), Welch and minimum variance distortionless response (MVDR) methods were compared. The MVDR coherence was found to be more suitable since it is both data and frequency dependent and enables higher spectral resolution, while Welch's periodogram-based approach is both data and frequency independent. The directed transfer function (DTF) method was applied in order to estimate the effective connectivity or brain's flow of information between different regions during each state. DTF enables to identify the main brain areas that initiate EEG activity and the spatial distribution of these activities with time. Analysis was conducted using the EEG data of 30 subjects (ten awake, ten drowsy and ten meditating) focusing on six main electrodes (F3, F4, C3, C4, P3, P4, O1 and O2). For each subject, EEG data were recorded during 5-min baseline and 15min of a specific condition (awake, meditation or drowsiness). Statistical analysis included the Kruskal-Wallis (KW) nonparametric analysis of variance followed by post hoc tests with Bonferroni alpha correction. The results reveal that both states of drowsiness and meditation states lead to a marked difference in the brain's flow of information (effective connectivity) as shown by DTF analyses. In specific, a significant increase in the flow of information in the delta frequency band was found only in the meditation condition and was further found to originate from frontal (F3, F4), parietal (P3, P4) and occipital (O1, O2) regions. Altogether, these results suggest that a change in attentiveness leads to significant changes in the spectral profile of the brain's information flow as well as in its functional connectivity and that these changes can be captured using coherence and DTF analyses.

Cetrizine induced drowsiness: Electroencephalographic concomitants

Background: Cetirizine, 2nd generation antihistamine, has less central adverse effects compared with the first generation but is not completely devoid of sedative effect. Electroencephalography (EEG) is one of the tests to assess sedation. Aims: The aim of this study was to find and compare the EEG changes in drowsy and non-drowsy subjects after cetirizine administration. Methods and Material: A crossover, placebo-control, double-blind study was conducted on consenting 30 healthy male volunteers. We subjected three (baseline, placebo, cetirizine) 5-min EEG recordings in eye closed condition to fast Fourier transformation and divided EEG frequencies into slow (0.5-6.5 Hz), extended alpha (6.5-14 Hz), alpha1 (6.5-8 Hz), alpha2 (8.5-10 Hz), alpha3 (10.5-12 Hz), alpha4 (12.5-14 Hz) and beta (14.5-32 Hz). Statistical analysis used: The statistical analysis was done using Friedman followed by multiple comparisons. Results: Nine out of thirty subjects developed symptoms of drowsiness after cetirizine administration. In drowsy subjects EEG beta, extended alpha and its subsegment alpha 2 and alpha 3 activities significantly decreased in cetirizine treated condition as compared to baseline. In non-drowsy subjects, there was significant increase in EEG slow and alpha1 activity in cetirizine treated condition as compared to baseline. There was significant decrease in EEG alpha3 activity at most of the sites when EEG activities between nondrowsy and drowsy subjects were compared. Conclusion: Our study suggested that cetirizine most likely decreases EEG power of alpha2, alpha3 and beta activities (i.e. above 8.5 Hz) in subjects experiencing drowsiness and increases EEG slow and alpha1 activities (i.e. below 8.5 Hz) despite no symptoms of drowsiness. On comparison, EEG alpha3 activity decreased in symptomatic as compared to asymptomatic subjects.

Neonatal sensory deprivation promotes development of absence seizures in adult rats with genetic predisposition to epilepsy

Absence epilepsy has age-related onset. In a WAG/Rij rat genetic model, absence seizures appear after puberty and they are increased with age. It is known that (1) epileptic activity in WAG/Rij rats is initiated at the perioral area in the somatosensory cortex; (2) sensory deprivation, i.e., whisker trimming during the critical period of development, could enhance excitatory activity in the somatosensory cortex. It is hypothesized that the cortex may become more excitable after neonatal vibrissae removal, and this may precipitate absence seizures in adult rats. We found that whisker trimming during the first postnatal weeks caused more rapid development of EEG seizure activity in adult WAG/Rij rats. Epileptic discharges in the trimmed rats were more numerous (vs control), showed longer duration and often appeared in desynchronized and drowsy EEG. The number of absence-like spindle-shaped EEG events (spike–wave spindles) in the whisker-trimmed rats was higher than in control, especially during the intermediate sleep state. An age-dependent increase of intermediate sleep state was found in the trimmed rats, but not in the intact animals. We discuss epigenetic factors that can modulate absence epilepsy in genetically prone subjects.

Can SVM be used for automatic EEG detection of drowsiness during car driving?

This study aims to develop an automatic method to detect drowsiness onset while driving. Support vector machines (SVM) represents a superior signal classification tool based on pattern recognition. The usefulness of SVM in identifying and differentiating electroencephalographic (EEG) changes that occur between alert and drowsy states was tested. Twenty human subjects underwent driving simulations with EEG monitoring. Alert EEG was marked by dominant beta activity, while drowsy EEG was marked by alpha dropouts. The duration of eye blinks corresponded well with alertness levels associated with fast and slow eye blinks. Samples of EEG data from both states were used to train the SVM program by using a distinguishing criterion of 4 frequency features across 4 principal frequency bands. The trained SVM program was tested on unclassified EEG data and subsequently checked for concordance with manual classification. The classification accuracy reached 99.3%. The SVM program was also able to predict the transition from alertness to drowsiness reliably in over 90% of data samples. This study shows that automatic analysis and detection of EEG changes is possible by SVM and SVM is a good candidate for developing pre-emptive automatic drowsiness detection systems for driving safety.

Depression, personality, and gender influence EEG, cortisol, beta-endorphin, heart rate, and subjective responses to smoking multiple cigarettes

EEG, hormonal, and subjective effects of smoking multiple cigarettes were assessed in 8 males and 8 females who smoked their own habitual brand of cigarette on one occasion, a nicotine-free control cigarette via a quantified smoke delivery system (QSDS) on another occasion, and standard cigarettes by means of a QSDS on two other occasions. Smoking nicotine-containing cigarettes decreased drowsiness and delta and theta EEG magnitude while it increased serum cortisol. Nicotine enhanced beta2 magnitude significantly more in the right than left hemisphere. Nicotine-induced changes in serum cortisol, drowsiness, and EEG magnitude correlated with each other, such that increases in cortisol correlated with increased arousal. Prior to smoking, nicotine-deprived female smokers displayed more drowsiness and delta and theta EEG activity than males and female nonsmokers, but did not differ in this regard after smoking. Female smokers also reported more trait depression than other participants. Extraversion correlated positively with nicotine-induced decreases in drowsiness, theta, and alpha EEG magnitudes, while neuroticism and depression correlated negatively with these changes. BDI depression score correlated with greater nicotine-free baseline EEG activation of the right than left hemisphere. Nicotine tended to eliminate (normalize) this frontal EEG asymmetry that is characteristic of depressed individuals.

新規非ベンゾジアゼピン系抗不安薬，ＳＣ‐４８２７４の脳波学的研究

The electroencephalographic (EEG) effects of SC-48274 were investigated in conscious rabbits and compared with those of buspirone and diazepam. SC-48274 (20-30 mg/kg, i.v.) evoked an increase in drowsy EEG pattern period. Diazepam (2-3 mg/kg, i.v.) also increased the drowsy EEG pattern, while buspirone (0.5 mg/kg, i.v.) increased the arousal EEG pattern period. Neither SC-48274 nor buspirone affected the EEG arousal response to both auditory stimulation and electrical stimulation of the midbrain reticular formation of the posterior hypothalamus, while diazepam markedly suppressed the responses to both stimulations. The recruiting response to centromedian thalamic stimulation was not significantly affected by SC-48274 and buspirone, and it was slightly enhanced by diazepam. Neither SC-48274 nor buspirone had any effect on the photic driving response to flash light in the occipital cortex, while the response was markedly suppressed by diazepam. The duration of afterdischarges induced by electrical stimulation of the dorsal hippocampus was slightly inhibited by SC-48274 and markedly inhibited by diazepam, while buspirone enhanced the duration. These results suggest that the EEG effect of SC-48274 is quite different to those of buspirone and diazepam with respect to the qualitative aspects. We also suggest that SC-48274, unlike diazepam, is an effective anxiolytic drug with weak sedation.

新規抗うつ薬Ｍｉｌｎａｃｉｐｒａｎ ｈｙｄｒｏｃｈｌｏｒｉｄｅ（ＴＮ‐９１２）の脳波および循環器に対する作用

The electroencephalographic (EEG) and cardiovascular effects of milnaciplan hydrochloride (TN-912) were compared with those of imipramine (IMP) and maprotiline (MPT) in rats, guinea pigs and dogs. In conscious rats with chronic electrode implants, TN-912 (10-100 mg/kg, p.o.) had little effect on either the EEG activity or the EEG arousal response to auditory stimulation (2000 Hz). Both IMP (10-100 mg/kg, p.o.) and MPT (10-100 mg/kg, p.o.) tended to increase the drowsy EEG pattern period in the cortical and hippocampal EEG and inhibited the EEG arousal response to auditory stimulation. In conscious rats with a chronic arterial catheter, TN-912 (100 mg/kg, p.o.) slightly elevated the mean blood pressure (MBP) and decreased the heart rate (HR), while both IMP (10-100 mg/kg, p.o.) and MPT (10-100 mg/kg, p.o.) dose-dependently increased MBP and HR. In anesthetized dogs, i.v. injection of TN-912 (1-10 mg/kg), IMP (0.3-10 mg/kg) and MPT (1-20 mg/kg) produced a dose-dependent fall in MBP. IMP and MPT but not TN-912 dose-dependently increased HR. TN-912 did not show typical effects on femoral blood flow. TN-912 (30 mg/kg) had little effect on lead II electrocardiogram (ECG), while IMP and MPT markedly increased height of the T-wave on ECG. In the in vitro study with the isolated guinea pig atrium, TN-912 caused a slight positive inotropic and negative chronotropic effect, while both IMP and MPT showed marked negative inotropic and chronotropic actions. These results suggest that TN-912 has less EEG effect and cardiac toxicity, indicating that TN-912 may be safe in clinical use.

新規非ベンゾジアゼピン系抗不安薬，Ｂｕｓｐｉｒｏｎｅおよびその代謝物１‐（２‐Ｐｙｒｉｍｉｄｉｎｙｌ）ｐｉｐｅｒａｚｉｎｅ（１‐ＰＰ）の脳波学的研究

The electroencephalographic (EEG) effects of buspirone and its major metabolite, 1-(2-pyrimidinyl)piperazine (1-PP), were investigated in rabbits with chronic electrode implants, and the effects were compared with those of diazepam. Intravenous administration of buspirone at 0.1 to 1.0 mg/kg evoked an increase in the arousal EEG pattern period (low amplitude fast waves) in the cortical EEG and synchronization of the hippocampal theta waves with decreased voltages, whereas both 1-PP (0.5 to 2.0 mg/kg, i.v.) and diazepam (1.0 and 2.0 mg/kg) evoked an increase in the drowsy EEG pattern periods: high voltage slow waves and spindle bursts in the cortical EEG and desynchronization of the hippocampal theta waves. Buspirone at higher doses caused behavioral excitation in rabbits, whereas both 1-PP and diazepam produced sedation. Buspirone did not affect EEG arousal responses to both auditory stimulation (2,000 Hz, monotone) and electrical stimulation (100 Hz, 0.1 msec, 3-6 V) of the midbrain reticular formation or the posterior hypothalamus. However, 1-PP tended to inhibit the EEG arousal response to auditory stimulation but not brain stimulation, and diazepam markedly suppressed the responses induced by both stimulations. The recruiting response induced by centromedian thalamic stimulation at a low frequency (7 Hz, 0.1 msec, 4-8 V) was not affected by buspirone, 1-PP and diazepam. Neither buspirone nor 1-PP had an effect on the photic driving response to a flash light (2 Hz) in the occipital cortex of the rabbit, whereas the response was suppressed by diazepam. Both buspirone and 1-PP enhanced the duration of after discharges induced by electrical stimulation (50 Hz, 0.5 msec, 4-15 V) of the dorsal hippocampus, whereas diazepam markedly inhibited the afterdischarges. These results suggest that the EEG effect of buspirone is quite different to those of 1-PP and diazepam in qualitative aspects. It is also suggested that buspirone, unlike diazepam, is an effective anxiolytic drug without a sedative effect.

Electroencephalographic study of Sch 161 (quazepam), a new benzodiazepine hypnotic, in rats and rabbits

The electroencephalographic (EEG) effects of 7-chloro-5-(o-fluorophenyl)-1,3-dihydro-1-(2,2,2-trifluoroethyl)-2H-1, 4-benzodiazepine-2-thione (Sch 161, quazepam) were investigated in unanesthetized rats and rabbits with chronic electrode implants, and the effects were compared with those of flurazepam and diazepam. In freely moving rats, quazepam at doses of 10 to 30 mg/kg, p.o., induced an increase in drowsy EEG pattern periods: high amplitude slow waves and spindle bursts in the cortical EEG and desynchronization of the hippocampal theta waves. Quazepam at doses of 10 to 30 mg/kg significantly decreased the onset of the drowsy EEG pattern after p.o. administration of rats in a dose-dependent manner, while flurazepam and diazepam reduced it only at higher doses of 20 to 30 mg/kg, p.o. In rabbits, quazepam at doses of 0.5 to 5 mg/kg, i.v., evoked a drowsy EEG pattern similar to that of flurazepam (1 to 5 mg/kg) or diazepam (1 to 2 mg/kg). Quazepam at higher doses caused sedation and a slight muscle relaxation in both rats and rabbits, which were weaker than those of flurazepam or diazepam. In both rats and rabbits, quazepam and flurazepam depressed the EEG arousal response to auditory stimulation but not to electrical stimulation of the midbrain reticular formation or the posterior hypothalamus, while the arousal response to either auditory or brain stimulation was markedly suppressed by diazepam. The recruiting response was not altered by quazepam, flurazepam and diazepam. Quazepam has no effect on the photic driving response to flash light in the occipital cortex of the rabbit, while the response was suppressed moderately by flurazepam and markedly suppressed by diazepam. Quazepam (2 mg/kg) suppressed either the hippocampal or amygdaloid afterdischarges. These results suggest that the EEG effect of quazepam is different to those of flurazepam and diazepam in qualitative aspects and that quazepam is likely to be an effective sleep-promoting drug with slight adverse reactions.

Ａｌｐｒａｚｏｌａｍおよび代謝産物の行動薬理学的・脳波学的研究

The behavioral and electroencephalographic effects of alprazolam and its metabolites were investigated in mice, rats and rabbits, and compared with the data on diazepam, lorazepam and nitrazepam. Locomotor activity of mice in an open-field situation was increased with smaller doses of alprazolam and nitrazepam but not with diazepam and lorazepam. Alprazolam increased the hyperactivity induced by methamphetamine in mice. The anticonflict effects of alprazolam in rats were more potent than those of diazepam and lorazepam. In suppressing hyperemotionality and muricide of olfactory bulbectomized rats, alprazolam was more potent than diazepam and fairly equal to that of lorazepam. Muricide of raphe-lesioned rats was markedly inhibited by alprazolam. Mescaline-induced head-twitches in mice were more markedly increased with alprazolam than with nitrazepam and diazepam. Alprazolam, diazepam and lorazepam prevented both maximal electroshock and pentetrazol convulsions in mice. Alprazolam was more potent than diazepam and lorazepam in potentiating thiopental, ether and ethanol anesthesia in mice. In the impaired rotarod performance, alprazolam was more potent than diazepam and lorazepam in mice, but was much less potent than diazepam and lorazepam in mice, but was much less potent than lorazepam in rats. In conscious rabbits with chronically implanted electrodes, alprazolam induced a drowsy EEG pattern and depressed the EEG arousal response not only to auditory stimulation but also to mesencephalic reticular stimulation. The EEG effect of alprazolam was approx. twice that of diazepam. The pharmacological activity of alpha-hydroxyalprazolam was approx. one third that of alprazolam. However, 5-chloro-2-(3-hydromethyl-5-methyl-4H-1, 2,4-triazolo-4-yl) benzophenone showed no pharmacological activity. These results indicate that alprazolam possesses pharmacologic properties characteristic to benzodiazepines and that the activity is more potent than diazepam. In addition, alprazolam seems to have a specific effect on the central serotonergic mechanisms.

5-Hydroxy-L-tryptophan (L-5HTP) の薬理学的研究

Pharmacological properties of L-5HTP in experimental animals were studied and the following results were obtained: L-5HTP produced a decrease in spontaneous locomotor activity in mice, a potentiation of morphine-induced analgesia in mice, an effect of lowering body temp. in mice and a tendency to induce a drowsy EEG pattern in rabbits. Anticonvulsant, anti-tremor, muscle relaxant effects and a potentiation of pentobarbital-induced aneasthesia were not induced by L-5HTP. On the other hand, L-5HTP antagonized the behavioral changes induced by reserpine, tetrabenazine and p-CPA in some experiments.