This study attempts to identify the error-related negativity (ERN) and correct response negativity (CRN) during e-sport video game match (Dota 2). A game environment requires players to make rapid decisions in a dynamic, evolving, and competitive context with no fixed restrictions on the range of possible actions. Combined with the ability to extract relevant information directly from the digital environment, the implementation of this approach may contribute to solving the problem of the ecological validity of experimental design in cognitive neuroscience.
Game recording files were utilized to obtain objective information regarding the timing and characteristics of game situations To implement this approach, work on the synchronization of EEG time series and the obtained dataset was conducted.
A particular type of game situation was selected for analysis, the objective of which is to strike a character controlled by the game algorithm in a timely manner. The ERN and CRN amplitudes were calculated as the difference between the most negative deflection in the -25–150 ms time window, associated with the moment of action completion by the controlled game avatar, and the preceding positive deflection.
The results of the paired t-test showed a significant difference between the amplitudes of ERN and CRN (t(10) = 2.82, p = 0.018). These empirical findings are consistent with observations, primarily obtained in laboratory conditions, indicating that the ERN amplitude is more pronounced in response to errors than the CRN amplitude is in response to correct actions.

Introduction: Relevant factors underlying major depressive disorder (MDD) are childhood stress and a lack of social support, which are mimicked in animal models by maternal deprivation (MD) and social isolation (SI). The objective was to evaluate depressive-like behaviors in rats subjected to MD and SI and in the female offspring, and to assess the treatment with Centella asiatica and madecassic acid. Methods: This study assessed the effects of MD and SI stress on young adult Wistar male and female rats from the first-generation and non-stressed offspring of stressed mothers. Forced swimming and open-field tests were conducted in 92-94 days, and the possible therapeutic effect of Centella asiatica (30 mg/kg) hydroalcoholic extract and madecassic acid (10 mg/kg) administered by gavage for fourteen days was investigated. Data were analyzed using ANOVA and Tukey's post hoc test (p < 0.05). Results: In the forced swimming test, immobility time was higher in the stress + saline group compared to the stress-free control group, and all treatments reversed this depressive-like behavior in the first phase. In the second phase, immobility time was higher in the offspring + saline group compared to the control group, and treatment with Centella asiatica in males and Centella asiatica and madecassic acid in females reversed this behavior. Conclusions: Stress in the first generation causes depressive-like behavior in rats, which Centella asiatica and madecassic acid can counteract. Mothers' childhood stress can have intergenerational effects on their children, and treatments with Centella asiatica and madecassic acid reduced depressive-like behaviors in stressed mothers and their offspring.

Introduction: Parkinson’s disease (PD) is a degenerative disease causing motor and non-motor symptoms. Animal models reproducing the main cellular processes of PD, such as oxidative stress (OS), neuroinflammation, and DNA damage, which leads to dopaminergic neuronal loss. Studies documented that Centalla asiatica herbal extract enriched with antioxidants exerts cytoprotective effects against aging and age-related neurodegenerative diseases.

Materials & Methods: The present study was designed to investigate whether the CAE would ameliorate MPTP-induced neurotoxicity in aged SD rats. Aged male SD rats (26 months old) were divided into three groups: control, MPTP alone (20mg/kg b.wt, i.p, twice at 20 min intervals), and MPTP with CAE (300mg/kg b.wt and/or quercetin (QN) (100mg/kg b.wt, orally) for 21 days. We invesitgated the aqueous extract of CAE based on OS biomarkers , inflammation, oxidative DNA damage (8 OHdG), DNA, ATP, GSH, neurotransmitter (NT) levels, and DNA repair enzymes in discrete brain regions associated with PD.

Results: MPTP-intoxicated rats elicited a highly significant elevation in the concentration of NO^• (a biomarker of OS), inflammation (IL-6, IL-1β, and TNF-α ), 8-OHdG, XO, nitric oxide synthase, NADPH oxidase, and PARP-1 (p<0.001) when compared with controls. There was a significant decrease in total antioxidant capacity, ATP, GSH, DA, NE, and SN contents with animals treated with MPTP. The co-administration of CAE and/or QN significantly (p<0.01) decreased biomarkers of OS and inflammation, as well as DNA repair enzymes, and significantly increased NT levels.

Conclusion: Knowledge of the epigenetic and molecular mechanisms involved in the neurodegeneration in this model is the key to identifying potential therapeutic targets for PD with antioxidants.

Introduction.

Amperometry is an electrochemical technique that allows the release of oxidizable amines by exocytosis to be studied. Serotonin is a neurotransmitter involved in the control and regulation of motor activity, body temperature, appetite, perception, cognitive function, sexual appetite, emotions and mood. It is important to know how serotonin is released directly in human cells.

Methods.

We used novel boron-doped diamond 16-microelectrode array devices (“16Ch BDD-MEA”)^1,2 to carry out amperometric measurements of serotonin release by exocytosis from human platelets. These cells store 90% of the blood serotonin. They are an easy cell model obtained from blood samples. We studied serotonin exocytosis in two different types of “16Ch BDD-MEA” devices: opaque on silicon substrates and transparent on quartz substrates.^1,2

Results.

We detected the exocytosis of serotonin as typical positive deflections called secretory spikes. From these spikes, we could extract kinetics parameters such as Imax (maximum oxidation current, in pA), t_1/2 (spike width at half maximum, in ms), Q (quantum size or spike net charge, in pC) and ascending slope of spike (in pA/ms). Studies were carried out under basal conditions and after loading the platelets with 10 µM serotonin for 2 h to explore the uptake mechanisms.^1,2

Conclusions.

“16Ch BDD-MEA” systems are an effective tool for quantitatively studying exocytosis of neurotransmitter serotonin from human platelets.

References.

¹ González Brito et al., Biosensors 2023, 13, 86.
² González Brito et al., R. Biosensors 2024, 14, 75.

The autonomous sensory meridian response (ASMR) is a tingling sensation in the scalp, neck, and back caused by sensory stimuli triggering the parasympathetic response. With a wide genre of popular online videos created to induce ASMR, viewers have claimed benefits such as improved sleep and relaxation. This study investigates the potential of ASMR to revolutionize productivity, mood, and stress-management in a fast and affordable way. To do so, single-case experimental design was utilized, which turns an observational case report into a hypothesis tested under controlled conditions to highlight an individual’s distinct changes in state. Tests were administered over eight weeks to comprehensively quantify effects of ASMR on mental function, including assessments for mood, memory, and executive function; a smartwatch; and a wearable electroencephalography headset. This indicated gradual improvement in executive function (medium effect size) and long term memory (large effect size) across the testing period as well as immediate improvement of mood (medium effect size) and activation of the parasympathetic response following each intervention. Furthermore, wavelet transform analysis demonstrated statistically significant increase in the power of alpha and delta waves during intervention, which was supported by in silico validation via a convolutional neural network. This model can now be applied to detect the unique brain activity during ASMR stimulation on larger populations, making cognitive enhancement by novel biofeedback interventions time-efficient and globally accessible. Overall, this experiment is the first to demonstrate the physiological basis of ASMR’s potential to improve cognitive function, therefore encouraging a preventative approach to mental health care.

Introduction: Oxidative stress (OS), inflammation, and ultimate irreversible membrane molecular mitochondrial damages and genome instabilities are implicated in aging and age-related progressive neurodegenerative diseases (NDDs), such as Parkinsonism, Senile Dementia and Alzheimer’s Disease (AD). α-Lipoic acid, acetyl-L-carnitine, coenzyme-Q, and niacin are iron-chelating antioxidant ergogenic-aids which play a pivotal role and exert cytoprotective effects against innumerable neurodegenerative diseases (NDDs). The ICV injection of streptozotocin (STZ) leads to neurodegeneration. This present study is used to estimate the neuroprotective effect of selected natural poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors on the biomarkers of OS and genome instability, inflammation, and DNA repair enzymes in STZ-induced neurotoxicity.

Materials and Methods: Male aged albino rats (24 months old, 350 gm body wt) were pretreated with α-lipoic acid and/or, acetyl-L-carnitine, nicotinamide, and coenzyme-Q (started 3 days prior to STZ) (100mg/kg b.wt, i.p for 21 days), followed by bilateral i.c.v injection with the DNA-destabilizing genotoxin STZ (100mg/kg b.wt). At the end of the 21 days, the hippocampus was dissected-out, and relevant biochemical parameters were estimated.

Results: The combined application of ergogenic antioxidants mitigated the toxic onslaught of SZN-induced neurotoxicity and exerted neuroprotection by significantly reducing MDA, 8-OHdG, AChE activity, IL-6, TNF-α, XO, NOS, the augmentation of antioxidants, ATP, DNA and NTs, and the modulation of PARP-1. PARP-1 expression was found to increase exponentially with the severity of OS and was found to decrease significantly with decreased OS.

Conclusion: The combined application of ergogenic antioxidants, such as α-Lipoic acid, acetyl-L-carnitine, coenzyme-Q, and/or niacin, will be effective in the treatment and/or management of progressive NDDS (such as AD).

: Brain tumor detection is crucial for improving patient outcomes through early
diagnosis and precise treatment planning. This research presents an in-depth study of advanced
methodologies for detecting and classifying brain tumors using cutting-edge imaging techniques
and machine learning algorithms. The study emphasizes magnetic resonance imaging (MRI)
due to its superior contrast resolution in soft tissues, essential for identifying brain anomalies.
Our approach leverages convolutional neural networks (CNNs), a deep learning architecture, for
automated brain tumor detection. The model is trained on an extensive dataset of annotated MRI
scans, employing data augmentation to enhance robustness and accuracy. The CNN architecture
is optimized to extract relevant features and classify different brain tumors, including gliomas,
meningiomas, and pituitary adenomas, with high precision. Performance evaluation is conducted
using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating
characteristic curve (AUC-ROC) . The CNN-based method demonstrates significant
improvements over traditional techniques, achieving an accuracy exceeding 96%. Additionally,
the incorporation of transfer learning techniques shows promise for adapting the model to
various medical imaging tasks with minimal retraining. This research highlights the critical role
of integrating advanced computational methods with medical imaging to improve the accuracy
and efficiency of brain tumor detection. The findings contribute to enhanced clinical decision-
making and patient care, underscoring the potential for machine learning to revolutionize
medical diagnostics.

Aim: Oxycodone is a widely used and misused opioid analgesic. CYP2D metabolizes oxycodone; our aim was to investigate brain CYP2D inhibitor pretreatment on oxycodone reward using conditioned place preference (CPP).

Methods: Male Wistar Han rats were habituated to CPP apparatus for 30 minutes. The next day, rats were allowed to explore the apparatus for 60 minutes. The following day, and mid-way through conditioning, rats were pretreated ICV with the CYP2D inhibitor propranolol or vehicle. The next day rats received oxycodone (3 mg/kg) or water via gavage and were confined to one compartment for 60 minutes. The treatment and compartment were alternated daily. Rats then received two 60 min drug-free post-tests and a state-dependent test (oxycodone 3 mg/kg) over three days. CPP was defined as an increase in time in the oxycodone versus water-paired compartment.

Results: In both experiments, little consistent CPP was observed in post-test 1. However, in experiment 1, in post-test 2, and the state-dependent tests, there was CPP in the vehicle-pretreated rats (p=0.047 and p=0.038) but not in the inhibitor-pretreated (p=0.742 and p=0.283) rats. Likewise, in experiment 2, in the post-test 2 and state-dependent tests, there was CPP in the vehicle-pretreated rats (p=0.021 and p=0.001) but not in inhibitor-pretreated (p=0.440 and p=0.387) rats. A trend for CPP (p=0.058) persisted in the vehicle-pretreated rats but not the inhibitor-pretreated rats for 22 days.

Conclusion: The CYP2D inhibitor blunts oral oxycodone-induced CPP, indicating that variations in the brain CYP2D metabolism of oxycodone may contribute to inter-individual differences in oxycodone misuse liability.

A mathematical model is formulated to examine the stochastic synaptic noise dynamics within subthalamic neuron electrophysiology, particularly concerning Parkinson's disease. By simulating the stochastic processes underlying synaptic noise, the model serves as a tool to explore the intricate dynamics of subthalamic neurons, contributing to the elucidation of Parkinson's disease mechanisms. Utilizing the stochastic Ornstein–Uhlenbeck process, we represent excitatory synaptic conductance and integrate it into a comprehensive whole-cell model to generate spontaneous and evoked cellular electrical activities. This single-cell model incorporates numerous biophysically detailed ion channels, depicted by a set of ordinary differential equations in Hodgkin–Huxley and Markov formalisms. Consequently, this approach effectively induces irregular spontaneous depolarizations (SDs) and spontaneous action potentials (sAPs), mirroring electrical activity observed in vitro. The model reveals that alterations in the ability to reach the action potential threshold are observed, alongside significant decreases in input resistance and increased firing rates of spontaneous action potentials. Additionally, background synaptic activity can modify the input/output characteristics of nonneuronal excitatory cells, providing further insights into the role of synaptic noise in modulating neuronal behavior. These findings are critical, as the subthalamic nucleus (STN) plays a central role in the motor circuitry affected by Parkinson's disease, and its abnormal activity is a hallmark of the disease. The stochastic model serves as a robust platform for simulating disease conditions and testing potential interventions, ultimately contributing to a better understanding of how synaptic noise influences neuron behavior and the pathophysiology of Parkinson's disease.

The early detection of Parkinson's disease (PD) is crucial for its effective management and treatment, as it can significantly slow disease progression and improve quality of life. One promising approach for early diagnosis is the analysis of voice signals, which can reveal subtle changes in phonetic features associated with PD. This study explores the use of machine learning techniques to identify PD at an early stage by leveraging a dataset from the UCI Machine Learning Repository, consisting of 147 phonetic samples from PD patients and 48 from healthy controls. The methodology involved preprocessing the data, selecting relevant features using a genetic algorithm, and addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE). Principal Component Analysis (PCA) was employed for dimensionality reduction, followed by the application of Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) classifiers. Cross-validation was performed to evaluate model performance. The results indicate that the KNN classifier achieved the best accuracy of 96.11%, demonstrating its superior capability in distinguishing between PD patients and healthy individuals based on voice features. The high accuracy suggests that voice signal analysis, combined with advanced machine learning techniques, is a promising avenue for the early detection of Parkinson's disease. This research underscores the potential of non-invasive diagnostic tools in clinical settings, paving the way for further studies to refine and validate these methods for broader applications.