Introduction. Hypoxia, a state of oxygen deprivation, is common in respiratory diseases and brain injuries. It also affects healthy individuals exposed to extreme operating environments, such as high altitude or low barometric pressure situations. While impairments in cognitive processing and novelty detection have been investigated, there is a lack of observations on its influence on decision making and psychomotor workload, and about the neural networks involved in these functions.

Our study investigated the neurofunctional preparatory processes for a single/double choice button press during four different cue-target visuospatial attentional orienting tasks based on Posner's Attention Network Test and the effects of hypoxia on these processes.

Methods. Healthy participants underwent two experimental sessions in which they breathed either ambient air or 12.5% oxygen-reduced air while performing the four cueing tasks. EEG was recorded from 128 scalp sites and event-related potentials (ERPs), intracerebral sources and behavioural responses (RTs) were computed.

Results. Both the amplitude of pre-target ADAN and LDAP ERP components and post-target RTs were lower for the single choice condition. Moreover, hypoxia had detrimental effects on decision making during valid attentional orienting conditions. Activations in the right anterior cingulate cortex, left superior parietal lobule and dorsolateral prefrontal gyri were enhanced under hypoxia for the double-choice decision task.

Conclusions. Our findings indicate that preparatory processes for a single- or double-choice are associated with the activation of different brain networks more strongly active in the more demanding condition. Overall, hypoxia compromised decision making efficiency. Impaired decision-making erodes accountability, raising ethical concerns about individual responsibility.

Introduction: Since the management of free-floating thrombus (FFT), a critical clinical entity associated with an increased risk of stroke or transient ischemic attack (TIA), is controversial, we performed a systematic review of the efficacy of various medical interventions in the management of FFT and factors associated with FFT resolution and recurrence.

Methods: We included studies from PubMed and EMBASE that reported patients diagnosed with FFT-related stroke or TIA who received anticoagulation or antiplatelet therapy or a combination. The primary outcomes were stroke recurrence and thrombus resolution. Statistical significance was determined at p<0.05 using Fisher's exact test, the chi-square test, the Mann–Whitney test, or the Kruskal–Wallis test.

Results: Our review (61 studies) included 179 patients diagnosed with FFT with a median follow-up duration of 7 months. Resolution of the thrombus occurred in 117 (65%), while 20 (12.6%) experienced recurrence, predominantly manifesting as TIAs. The incidence of cardioembolism was greater in the patients with unresolved thrombi (7.7% (n=9), p=0.025). Patients who received combination therapy involving antiplatelet agents, anticoagulants, and statins showed a higher probability of clot resolution ([OR] 11.4; 95% [CI] 1.436-91.91; p=0.021) compared to those treated with anticoagulant or antiplatelet therapy alone ([OR] 1.201; 95% [CI] 0.601-2.40; p=0.604, [OR] 0.780; 95% [CI] 0.317-1.92; p=0.588), respectively. Notably, ulcerated plaques predicted recurrence ([OR] 8.2; 95% [CI] 1.02-66.07; p=0.048).

Conclusions: Combination medical therapy of antiplatelets, anticoagulants, and statins is superior to anticoagulant or antiplatelet therapy alone. Moreover, the identification of ulcerated plaques as a significant predictor of recurrence underscores the importance of targeted interventions in high-risk patients.

The early detection of Alzheimer's disease (AD) is crucial for effective intervention and management. This study aims to detect AD using electroencephalogram (EEG) signals analyzed through advanced machine learning techniques. The dataset, provided by researchers from Florida State University, consists of EEG recordings from 24 healthy controls and 24 patients diagnosed with AD using a 19-electrode recorder in accordance with the international 10–20 system. The recordings were captured using the Biologic Systems Brain Atlas III Plus workstation. EEG signals were preprocessed to remove noise and artifacts, and features were extracted using a finite impulse response (FIR) filter in the double-time domain, focusing on changes in the power spectrum associated with AD. These features were then used to train and test three machine learning classifiers: the support vector machine (SVM), naive Bayes, and XGBoost. Among these, the XGBoost model demonstrated the highest accuracy, achieving a remarkable 96% accuracy in distinguishing between AD patients and healthy controls. The superior performance of the XGBoost model underscores the potential of EEG signal analysis combined with machine learning for the early detection of Alzheimer's disease. This approach provides a non-invasive and cost-effective diagnostic tool and offers significant promise for improving the timely diagnosis and management of AD. This study highlights the efficacy of leveraging advanced signal processing and machine learning techniques in the field of neurodiagnostics, paving the way for innovative solutions in the detection and monitoring of neurodegenerative diseases.

Background: Several studies highlight the relevance of video games and virtual reality (VR) training in enhancing various attentional processes, specifically selective attention, cognitive flexibility, and attentional blink. Beat Saber, an action virtual reality game, has been used in previous studies to train visual attention, reporting significant effects on visual attention task scores. In our laboratory, we previously obtained significant improvements in dynamic visual acuity performance following one training session with Beat Saber.

Method: Thirty-nine university students were divided into three groups: (1) those trained with the Beat Saber VR exergame, (2) those exposed to a relaxing video using VR, and (3) a control group without any intervention. All the interventions lasted 20 minutes. A pre–post-intervention study design was implemented, applying two attentional tests: the Flanker task and the attentional blink task.

Results: The factorial ANOVA showed no significant differences between the groups in terms of improvement in the attentional tasks. However, significant differences were observed between the pre- and post-intervention assessments within each group, indicating some overall changes in performance over time, irrespective of the type of intervention. Training duration differences could be the reason for the discrepancies in the visual attention improvements between other studies and ours.

Conclusions: A single session of Beat Saber training does not produce an improvement in selective attention or attentional blink. Despite these results, it is important to continue studying the potential of VR in different contexts and with varied durations and frequencies of training sessions.

Background: According to the dual-competition framework, inhibitory control is influenced by emotional arousal, with highly arousing stimuli potentially impairing response inhibition due to increased cognitive resource allocation toward stimulus processing. This study aimed to investigate how stimuli depicting fearful scenes affected inhibitory processing compared to non-fearful stimuli. Virtual reality (VR) was utilized to enhance emotional intensity, and two versions of the Go/No-Go task were employed: one with explicit processing of stimuli and another with masked stimuli requiring implicit processing.

Methods: Stimuli selection involved an image evaluation process, resulting in two task versions for the Go/No-Go paradigm. Sixty-four participants were randomly assigned to conditions involving exposure or no exposure to VR, combined with task version variations. Participants performed inhibitory tasks under these conditions to assess differences in inhibitory control influenced by stimulus type and processing method.

Results: Fear stimuli with higher arousal levels significantly impaired response inhibition compared to non-fearful stimuli. Explicit stimulus processing in the Go/No-Go task also showed greater inhibition impairment than implicit processing. Additionally, prior exposure to stimuli in VR conditions affected response times, indicating extended processing demands for inhibitory tasks.

Conclusions: This study confirmed that stimulus intensity, particularly emotional arousal, played a critical role in modulating inhibitory control. Fearful stimuli and explicit stimulus processing significantly reduced the ability to inhibit responses. Moreover, VR-induced stimulus exposure extended processing times, highlighting implications for understanding inhibitory processes under varying emotional and environmental conditions.

Introduction. Dementia is a clinical syndrome mainly characterized by progressive cognitive decline. Alzheimer's Disease (AD), the most prevalent type of dementia, is a priority in research as stated by the World Health Organization, given the annual surge of AD cases and its high economic impact. With unknown causes, dementia remains incurable. Mansorin A (MA), mansonone G (MG), and 6-paradol (PD) are plant-derived compounds with potential in AD treatment.

Methods. MA, MG, and PD were administered chronically via immersion in concentrations of 1, 3, and 6 μg/L to the novel okadaic acid-induced zebrafish model of AD obtained by immersion of animals in 10 nM okadaic acid. A battery of behavioral tests was conducted to assess the effect of administered substances on short-term recognition and spatial memory (Novel Object Recognition and Ymaze tasks, respectively) and anxiety (Novel Tank Diving and Novel Object Approach tests) in the animal model. Following, biochemical analysis was conducted to assess the oxidative stress and acetylcholinesterase levels in the brain. In silico analysis of absorption, distribution, metabolism, and excretion (ADME) parameters was also performed.

Results. The obtained results indicate promnesic, anxiolytic, and acetylcholinesterase inhibitory properties of MA, MG, and PD, as well as antioxidant effects by increasing the activity of catalase, superoxide dismutase, reduced glutathione, and glutathione peroxidase while reducing the levels of carbonylated proteins and malondialdehyde. Analysis of ADME parameters indicates that MA, MG, and PD are blood-brain barrier permeant and are drug-like molecules.

Conclusions. The results pose MA, MG, and PD as promising anti-AD agents.

An important and urgent problem of neuroscience and medicine is the understanding of the mechanisms of reorganization of motor function under conditions accompanied by a restriction on the functional use of skeletal muscles. The aim of the study was to assess the condition of the neuromotor apparatus of the rat's calf muscles in conditions of simulated disuse.

All experiments were carried out in strict accordance with generally accepted bioethics norms. The animals were divided into experimental groups: unilateral compression of the sciatic nerve (n=5); unilateral tenotomy (n=5); antiorthostatic hanging (n=5). After 7 days, the electromyographic characteristics of the gastrocnemius, soleus, and tibialis anterior muscles were recorded. Data from intact animals served as a control (n=5). An increase in the reflex excitability of spinal cord motor neurons and a violation of the reliability of synaptic transmission were detected, regardless of the procedure for modeling disuse of the muscle. The transformations were more pronounced in the neuromotor apparatus of the extensor muscles. In addition, in nerve injury and tenotomy, changes were also recorded in the contralateral (undamaged) motor system.

Thus, the disuse of skeletal muscle initiates the transformation of the functional state of all links of symmetrical neuromotor systems. The main reason for the detected effects is assumed to be the restriction of peripheral afferentation, including support, and the activation of intraspinal bilateral connections.

This research was funded by the subsidy allocated to Kazan Federal University for the state assignment in the sphere of scientific activities, project no. FZSM-2023-0009.

During social communication, individuals adapt their behavior based on the actions of the other person. Nonverbal communication, particularly through gestures, is a crucial aspect of this interaction. Gesture imitation helps individuals perceive similarity, synchronize with others, learn, and recognize patterns. This review aims to highlight the most recent studies between 2019 and 2024 that used hyperscanning configurations during social interaction to investigate inter-brain synchronization. The selected papers are selected based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and addressed based on paradigms and findings. The studies indicated that increased brain synchronization is linked to improved social interactions during eye contact, especially between parents and infants. Eye contact can modulate neural synchronization and enhance cooperation and competition. In humans, certain brain regions, including the frontopolar area and dorsolateral prefrontal cortex (dlPFC), are activated when observing hand actions in videos, while areas like the orbitofrontal cortex, dorsomedial prefrontal cortex, anterior cingulate cortex, and amygdala play a role in social gaze. In bats, the power of brain activity varies across different frequency bands, with low-frequency bands being more active during rest and high-frequency bands being more active during active behaviors. In conclusion, the reviewed studies underscore the importance of inter-brain synchronization in facilitating social interactions. Eye contact plays a significant role in modulating neural activity and promoting both cooperation and competition. Understanding the neural mechanisms underlying these interactions, across both humans and animals, can provide deeper insights into the complexities of social behavior and communication.

A broad range of neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's
disease (PD), and Huntington's disease (HD), are associated with the aggregation of proteins
into amyloid fibrils. For many amyloid-forming proteins, the aggregation process proceeds
through a complex mixture of intermediates predominately comprising oligomers. Due to thecomplex heterogeneity associated with amyloid formation, it is difficult to assign specific toxic
functions to different aggregate species, particularly when expressing aggregating proteins within
cells or organisms. To overcome this challenge, a mutant Huntington protein [htt-exon1(46Q)],
which readily aggregates and is associated with HD, was employed as a model system to
create a protocol that exposes the N2 strain of C. elegans to well-characterized, specific
aggregates to assess toxicity. Viause of controlled aggregation conditions and separation
techniques, uniform populations of htt-exon1 (46Q) aggregates were obtained and characterized
using atomic force microscopy and dynamic light scattering. Upon exposure to these different
aggregate populations, the viability and motility of C. elegans were determined. Htt-
exon1 (46Q) oligomers represented the most potently toxic aggregate form. Fibrils did not invoke
any toxicity in N2 worms; however, exposure of C. elegans resulting in them expressing a nonpathogenic htt
fragment that does not aggregate results of reduced viability. To demonstrate the utility of this
approach, several additional experiments were performed with oligomers. First, chemically
cross-linking htt-exon1 (46Q) oligomers and stabilizing oligomers
via the incorporation of truncated small peptides derived from the first 17 amino acids of
Huntington's disease induced toxicity differently. These representative methods of controlling the type of aggregate species introduced into a living system to assess toxicity and methods that alter that toxicity are discussed.

Introduction: Artificial Intelligence (AI) is having a revolutionary impact across various sectors, transforming industries, enhancing capabilities, driving innovation and efficiency, and creating new opportunities. In the health sector, AI algorithms have been used to analyse medical images and data more speedily and accurately than humans, aiding in the early detection of diseases such as cancer. It has also been used for the development of personalised treatment plans based on an individual’s genetic makeup and lifestyle. Furthermore, surgical robots powered by AI have been used to perform complex procedures with precision. Neuroimaging and AI are converging to offer transformative advancements in understanding the human brain and diagnosing neurological conditions.

Methods: We implement a deep learning neural network model that forecasts the time series of a functional magnetic resonance imaging (fMRI) dataset. However,, applying deep learning models to fMRI is not trivial due to the high-dimensional nature of fMRI images. We execute a long short-term memory (LSTM) recurrent neural network (RNN) model that forecasts the time series of fMRI datasets.

Results: The training of the LSTM-RNN model, which includes the root-mean-squared error (RMSE) and the loss, is shown. Also, a histogram depicting the RMSE and the errors is shown. The mean RMSE over all test observations was calculated, and the predictions were compared with the target values.

Conclusion: We have successfully implemented an LSTM-based RNN model for the forecasting of fMRI time series. The model will enable the prediction of future brain states and facilitate advancements in neuroscience research and clinical applications.