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The increasing prevalence of sleep dysregulation cases has prompted the search for effective and safe sleep-enhancing agents. Numerous medications used in the treatment of sleep disorders function by enhancing γ -aminobutyric acid neurotransmitter activity. Unfortunately, these substances may induce significant adverse effects in chronic users, such as dependence and motor behavior impairments. Consequently, there is a growing interest in exploring therapeutic sleep-enhancing agents derived from natural sources, with the anticipation of causing less severe side effects. Prunella vulgaris (PV), a perennial plant indigenous to South Korea, exhibits various pharmacological effects, likely attributed to its chemical composition. Rosmarinic acid, one of its components, has previously demonstrated sleep-potentiating properties, suggesting the potential for PV to exhibit similar pharmacological effects. This study aims to investigate the potential effects of repeated administration of PV extract on the sleep behavior, brainwave activity, sleep–wake cycle, and physiological behavior of mice. Findings indicate that PV extracts exhibit sleep-enhancing effects in mice, characterized by prolonged sleep duration and a reduced onset time of pentobarbital-induced sleep. However, PV extracts only reduced alpha wave powers, with minor alterations in wakefulness and rapid-eye-movement sleep duration. In contrast to diazepam, PV extracts lack adverse effects on locomotor activity, motor coordination, or anxiety in mice. Receptor-binding assay and caffeine treatment support the potential involvement of adenosine A2A receptors in the effects of PV, suggesting distinct mechanisms of action compared to diazepam, despite both exhibiting sleep-altering effects. Overall, our results suggest that PV holds promise as a potential source of sleep-aiding agents. [ABSTRACT FROM AUTHOR]

2024 NOV 16 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- According to news reporting based on a preprint abstract, our journalists obtained [...]

IN THIS ISSUE What is an EEG? EEG is short for electroencephalogram. It is a type of scan that records someone’s brain activity. During an EEG, small metal discs are [...]

The article focuses on evaluating the effectiveness of different therapies for varicella zoster virus (VZV) meningitis. Topics discussed include the varying use of antiviral treatments like oral valacyclovir and intravenous acyclovir; the relatively benign nature of VZV meningitis in most patients; and the associated risk of acute kidney injury with prolonged intravenous (IV) acyclovir use.

The article delves into the resurgence of interest in music's therapeutic potential, spurred by recent advancements in neuroscience, highlighting its role in emotional regulation and cognitive engagement, especially in conjunction with conventional mental health treatments. It also examines the historical and cultural significance of music therapy, its impact on memory recall and emotional responses, and ongoing research efforts to understand the intricate connection.

Cognition Therapeutics announced that results from the SEQUEL study of CT1812, the Company's lead candidate for the treatment of Alzheimer's disease, were published in The Journal of Prevention of Alzheimer's [...]

2024 AUG 3 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- According to news reporting based on a preprint abstract, our journalists obtained [...]

PRACTICE RECOMMENDATIONS > Consider treating a first-time seizure if electroencephalography shows particular epileptiform activity, if the neurologic exam or computerized tomography or magnetic resonance imaging results are abnormal, if the [...]

The article focuses on the paradigm shift towards understanding human health beyond biochemical processes, emphasizing the role of electromagnetic fields and energetic interactions in holistic healing. Topics include the therapeutic use of sound waves, based on resonance and vibrational frequencies, to influence cellular responses and promote healing.

2024 MAR 30 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- According to news reporting based on a preprint abstract, our journalists obtained [...]

2024 MAR 23 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Data detailed on stroke have been presented. According to news reporting originating [...]

2024 MAR 23 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Data detailed on agriculture have been presented. According to news reporting originating [...]

INmune Bio, a clinical-stage immunology company targeting microglial activation and neuroinflammation as a cause of Alzheimers disease (AD), has reported significant improvements in electroencephalography (EEG), a biomarker of brain function, [...]

2024 FEB 3 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- A patent application by the inventors Adom Opare-Addo, Nana (Bronx, NY, US); [...]

2024 JAN 13 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- Researchers detail new data in Science - Electroencephalography and Neuroscience. According to [...]

The article explains how electroencephalography (EEG) uncovers brain-skin axis interactions for beauty. Topics discussed are effects of skin feel on the brain, importance of relaxation to enhancing skin appearance and the mood-boosting effect of propanediol and Sanguisorba officinalis root extract, benefits of aromatherapy for relaxation and brain activity, and real-time brain waves and personalization approach of innovation in proof of efficacy of cosmetics.

• This paper presents a lightweight and efficient spatial–temporal transformer network to learn collaboratively and efficiently to detect epileptic seizures. • A hierarchical FL framework is introduced to enable resource-efficient training of the detection network. • The proposed Fed-ESD mitigates the risk of a single point of failure by alleviating reliance on a centralized authority. Epilepsy is a predominant paroxysmal neurological disturbance that is usually recognized as the incidence of impulsive seizures rarely seen in medicine. Automatic detection of epileptic seizures from electroencephalogram (EEG) signals is viewed as an effective diagnosis of patients on the Internet of Medical Things (IoMT). To design a robust detection service in an IoMT environment, the EEG signals of different patients are collected from geographically distributed patients to a centralized server. However, this makes the patient's privacy prone to exposure and adds to the energy and communication costs. Also, the central server can be subject to malevolent attacks, resulting in non-efficient solutions. In this regard, for the first time, this paper presents a privacy-preserving federated learning framework for epileptic seizure detection (called Fed-ESD) from EEG signals in the fog-computing-based IoMT. A lightweight and efficient spatiotemporal transformer network is introduced to collaboratively learn spatial and temporal representations from the local data of each participant. The proposed Fed-ESD employs geographically situated fog nodes as local aggregators to enable sharing of location-based EEG signals for comparable IoMT applications. Moreover, a greedy method is introduced for deciding on the ideal fog node to be the coordinator node responsible for global aggregation during the training, thereby decreasing the reliance on the central server in the IoMT. Experimental evaluations demonstrate the efficiency of the proposed Fed-ESD in terms of detection performance, resource-efficiency, stability, and scalability for deployment in the IoMT. [ABSTRACT FROM AUTHOR]

Early Detection Conundrum. Physicians are uniquely charged to effectively determine the cause of patient complaints and to do so early in the course of the potential disease to maximize convalescence. [...]

2023 OCT 14 (NewsRx) -- By a News Reporter-Staff News Editor at Obesity, Fitness & Wellness Week -- According to news reporting based on a preprint abstract, our journalists obtained [...]

Telemedicine provides remote online services for digital diagnosis and treatment via the Internet. However, there is a risk of data leakage during transmission. Therefore, data protection is an important challenge for telemedicine. Chaos is widely used in image, audio, and EEG encryption because of its unique characteristics of unpredictability, nonlinearity, and sensitivity to an initial state. However, some chaotic maps have various security issues. To solve these problems, this paper proposes a K-sine-transform-based coupled chaotic system (K-STBCCS), combining any two one-dimensional chaotic mappings to generate a new chaos mapping. To demonstrate the reliability of the system, this paper generates three new chaotic mappings using K-STBCCS and analyzes their performance. Using the chaotic mapping generated by K-STBCCS, this paper further proposes an EEG signal encryption scheme based on the confusion-diffusion principle. The purpose of confusion is to separate adjacent EEG signals, while the purpose of diffusion is to change the value of EEG signals. Among them, the diffusion operation uses positive and negative diffusion to reduce the correlation between the ciphertext and the original signals. The experimental results and security analysis show that the proposed EEG signal encryption scheme performs well and passes the rigorous cryptographic security test. [ABSTRACT FROM AUTHOR]

The article discusses the pediatric condition called infantile spasms or West syndrome, including a guideline for pediatricians for its diagnosis and treatment. Other topics include the clinical features, work-up, and management of the disease, and the symptoms of the disease like hypsarrhythmia on electroencephalogram (EEG) and associated neurodevelopmental arrest or regression.

The article discusses several aspects of neuromarketing, including the use of fMRI (functional magnetic resonance imaging) and EEG (electroencephalography) to measure the brain physiology of consumers. The role that neuromarketing plays in helping to develop advertisements (ads), including in regard to ad design and the effectiveness of advertising, is discussed.

Introduction: In a cohort of adult patients with disturbance of consciousness after TBI, we aimed to explore the relationship of continuous video-EEG (cEEG) versus routine EEG (rEEG) with mortality and functional outcome., Methods: This is a post hoc analysis of a randomized controlled trial (CERTA), in which adults with disorder of consciousness and needing EEG (excluding those with proven seizures/SE just before) were randomized 1:1 to cEEG or two rEEG. In TBI patients, correlation between EEG duration, mortality, and modified Rankin score (mRs, good 0-2) at 6 months was assessed., Results: Among 364 patients, 44 presenting with consciousness impairment after TBI were included; 29 randomized to cEEG and 15 to rEEG. Mortality (p = 0.88) and functional outcome (p = 0.58) at 6 months were similar between groups. There was a nonsignificant tendency toward more seizure/status epilepticus detection with cEEG (p = 0.08). In multivariable regression, cEEG was not related to functional outcome (OR: 0.75 [0.13-4.24], p = 0.745) or mortality (OR: 7.11 [0.51-99.32], p = 0.145)., Conclusion: Despite allowing increased seizure detections in TBI patients, cEEG does not seem to be associated with better functional outcome or mortality over rEEG. Pending larger trials, repeated rEEG might be acceptable in post-TBI disorder of consciousness, especially in resource-limited environments., (© 2024 S. Karger AG, Basel.)

The aimof this studywas to assess welfare outcomes of electrical stunning as ameans of restraint in farmed grower saltwater crocodiles (Crocodylus porosus). Physical handling of a stunned, unconscious crocodile is far safer for the operator than handling a fully conscious animal. Electroencephalogram(EEG) was recorded before and after the application of electrical stunning at 50Hz or 400 Hz using an electrical stunner applied to the cranial plate (Position 1: P1-50 Hz; n = 31, P1-400Hz; n = 29) or immediately behind the skull (Position 2: P2-50 Hz; n = 29; P2-400Hz; n = 30). For all electrical stuns, percentage total EEG power in a 10-s epoch decreased in the alpha and beta frequency bands; and increased in the delta and lower frequencies bands. All electrical stuns resulted in increased strength of signal, based on the quadratic mean EEG power in all frequency bands of the EEG. Greatest change in power occurred in the delta frequency band, with P1-50 Hz. This was greater than with P2-50 Hz; while decibel change using 400 Hz at either position was intermediate and not significantly different from either. Application of either electrical stunner at position 1 resulted in seizure-like activity and activation in low frequencies, but at position 2 this was not consistent across all animals. The ability of the electrical stunning equipment to consistently induce recoverable unconsciousness could be ranked in decreasing order as: P1-50 Hz > P1-400Hz = P2-50 Hz > P2-400 Hz. Based on behavioural observations, all animals in the study appeared to stunned however evaluation of duration of EEGchanges indicates that use of the electrical stunning equipment at 50Hzwould allowsomemargin for inaccuracies in tong placement, while achieving a consistently reliable stun. [ABSTRACT FROM AUTHOR]

The article discusses the development of the EEG-VLM Toolbox, a novel algorithm that extends voxel-based lesion mapping to multi-dimensional EEG data in stroke patients. By analyzing neurophysiological changes in relation to precise neuroanatomical injury locations, the algorithm provides insights into the impact of brain lesions on brain function. The study suggests that EEG-VLM could enhance mechanistic understanding, aid in biomarker development, and guide neurorehabilitation strategies for individuals with focal brain lesions and neurological impairments. [Extracted from the article]

The article discusses the use of electroencephalography (EEG) data to predict subject traits, particularly focusing on brain aging. The study explores the application of data-driven kernel methods for prediction from single EEG channels by reinterpreting the spectrogram as a probability distribution. The research suggests that these kernel methods show improved performance in predicting biological age, demonstrating the capacity to generalize across different experiments and acquisition setups. The preprint has not yet undergone peer review and provides valuable insights into the potential of EEG data in neuroscience and clinical research. [Extracted from the article]

A recent study from Islamic Azad University in Tehran, Iran, explores the use of machine-learning techniques in predicting Multiple Sclerosis (MS) using Electroencephalography (EEG). The research suggests that utilizing multimodal machine learning over clinically available data could aid in early diagnosis of MS. By analyzing EEG signals and applying various techniques, the study achieved classification accuracy higher than 99%, indicating the potential for EEG-based machine learning methods to become more widespread in MS diagnosis. Further research is needed to validate and improve these techniques for dynamic and online EEG signal analysis. [Extracted from the article]

Researchers at Graz University of Technology have developed a new method for analyzing EEG data to monitor key spectral components in the brain. The study focused on motor-related brain-computer interface applications and found that the new approach, called pole tracking, outperformed traditional EEG-based features in detecting rest, movement execution, movement imagination, and movement attempts in healthy subjects and individuals with spinal cord injury. While the method effectively captured broad changes in EEG dynamics, its ability to distinguish finer movement details within specific types of movement was limited. The research was published in the IEEE Transactions on Neural Systems and Rehabilitation Engineering. [Extracted from the article]

Researchers at King Saud University in Riyadh, Saudi Arabia, have developed a lightweight neural network model called GCT-EEGNet for EEG-based individual recognition under diverse brain conditions. This model aims to address the challenges faced by existing EEG-based biometric systems, such as high parameter complexity and limited generalization across subjects. The proposed method achieved a correct recognition rate of 99.23% and an equal error rate of 0.0014, demonstrating its robustness against different brain conditions. The research highlights the potential of EEG brain signals as a promising biometric trait due to their sensitivity, non-duplicability, and individual-specific dynamics. [Extracted from the article]

Researchers from Ningxia Medical University conducted a study on the EEG characteristics before and after dexmedetomidine treatment in severe patients in the ICU. The study found that relative spectral energy and alpha variation could be used to evaluate brain function even under light sedation with dexmedetomidine. The research, which was peer-reviewed, provides valuable insights into monitoring brain function in critical patients. For more information, readers can refer to the article published in Clinical EEG and Neuroscience. [Extracted from the article]

Researchers at Tokyo University of Agriculture and Technology have developed a new method for accurately localizing subcortical and cortical sources of M/EEG signals using a convolutional neural network. This approach, which does not rely on traditional regularization priors, showed excellent accuracy in source localization, particularly in subcortical areas. The study suggests that this deep learning-based method could contribute to clinical diagnosis, understanding neuronal diseases, and advancing research on basic brain functions. [Extracted from the article]