Frontiers in Human Neuroscience最新文献

Introduction: In high-stakes environments such as aviation, monitoring cognitive, and mental health is crucial, with electroencephalogram (EEG) data emerging as a keytool for this purpose. However traditional methods like linear models Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) architectures often struggle to capture the complex, non-linear temporal dependencies in EEG signals. These approaches typically fail to integrate multi-scale features effectively, resulting in suboptimal health intervention decisions, especially in dynamic, high-pressure environments like pilot training.

Methods: To overcome these challenges, this study introduces PilotCareTrans Net, a novel Transformer-based model designed for health intervention decision-making in aviation students. The model incorporates dynamic attention mechanisms, temporal convolutional layers, and multi-scale feature integration, enabling it to capture intricate temporal dynamics in EEG data more effectively. PilotCareTrans Net was evaluated on multiple public EEG datasets, including MODA, STEW, SJTUEmotion EEG, and Sleep-EDF, where it outperformed state-of-the-art models in key metrics.

Results and discussion: The experimental results demonstrate the model's ability to not only enhance prediction accuracy but also reduce computational complexity, making it suitable for real-time applications in resource-constrained settings. These findings indicate that PilotCareTrans Net holds significant potential for improving cognitive health monitoring and intervention strategies in aviation, thereby contributing to enhanced safety and performance in critical environments.

Introduction: It is generally accepted that during body position changes from horizontal to vertical there is a short-lasting shift of a certain CSF volume from the cranium into the hydrostatically lower parts of the spinal space, which leads to transitory CSF pressure decrease to negative values.

Methods: In order to test this, we performed MRI volumetry of cranial and spinal part of the CSF space in healthy volunteers of both genders (n = 22) in three different body positions [horizontal (H); elevated head and upper body (H-UP) under an angle about 30° from the base; elevated lower body (B-UP) under an angle about 30° from the base].

Results: Volumes of brain and spinal cord tissue did not change during body position changes. Significant CSF volume (ml) changes occur inside the spinal space in the tested body positions, primarily in the lumbosacral segment (H-UP - 38.1 ± 7.0; H - 34.4 ± 6.5; B-UP - 28.7 ± 6.5), while at the same time no significant CSF volume changes have been observed inside the cranium in two tested positions (H and B-UP) in which it was possible to measure intracranial CSF volume changes or if we sum up cervical and cranial CSF volumes in those positions.

Conclusion: Observed results suggest that during the changes of body position CSF volume redistribution occurs, primarily inside the spinal and not the cranial space. This is in accordance with the new hypothesis by which spinal intradural space can significantly change its volume due to its elasticity, thus adjusting to the influence of gravity and pressure changes.

Objective: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment and expanded the range of tumor indications. However, as the usage of this medication has increased, related adverse events are increasingly being identified. Among these, Atezolizumab-induced encephalitis, aseptic meningitis, and meningoencephalitis remain poorly understood regarding clinical features. This study provides a comprehensive reference for classifying, identifying, and managing Atezolizumab-associated neurological adverse events, specifically encephalitis, aseptic meningitis, and meningoencephalitis.

Methods: This study systematically collected published case reports from public databases such as PubMed, Web of Science, Wanfang Database, and CNKI Database. Two independent reviewers screened titles and abstracts and then extracted data in the included article according to the designed table and analyzed the clinical characteristics of Atezolizumab-induced encephalitis, aseptic meningitis, or meningoencephalitis.

Results: A total of 17 articles were included, with 19 patients diagnosed with encephalitis, aseptic meningitis, or meningoencephalitis after Atezolizumab treatment. The most common presenting symptoms included fever, altered consciousness, fatigue, somnolence, and seizures. Diagnosis was primarily based on cerebrospinal fluid analysis, blood tests, and imaging studies, such as computed tomography (CT) scans. Treatment strategies typically involved systemic steroids, antiviral agents, antibiotics, and anti-epileptic medications, as appropriate.

Conclusion: Neurological immune-related adverse events may rapidly progress and impact prognosis. Therefore, clinical practitioners should have a deep understanding of these neurological immune-related adverse events, promptly diagnose them, and provide accurate and timely treatment.

Introduction: Setting the right price is crucial for effectively positioning products in the market. Conversely, setting a "non-optimal price"-one that is perceived as much lower or higher than the product's true market value-can negatively influence consumer decisions and business results.

Methods: We conducted two electroencephalography (EEG) studies and one magnetoencephalography (MEG) study to investigate brain mechanisms underlying the perception of prices during a price judgment task. In each trial, participants were exposed to a mobile phone image (iPhone, Nokia, or Xiaomi) followed by a price, and instructed to judge whether the price was high or low based on a target word ("cheap" or "expensive").

Results: In both EEG experiments, we found a strong N400-like response to the incongruent target words following prices that substantially differed from the real market value of the mobile phone. The MEG experiment extended these findings by localizing the brain source of the price-related, M400-like response, the magnetic counterpart to the N400 component, in the ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) implicated in value-based and reward-based learning, respectively. Our results demonstrate that both the brain sources and the timing of the price-related M400 response differed from those of the standard M400 evoked by semantically incongruent words.

Discussion: Overall, our results suggest that the N400-like response can serve as a neural marker of price-product incongruity, with potential applications in consumer research.

Humans achieve efficient behaviors by perceiving and responding to errors. Error-related potentials (ErrPs) are electrophysiological responses that occur upon perceiving errors. Leveraging ErrPs to improve the accuracy of brain-computer interfaces (BCIs), utilizing the brain's natural error-detection processes to enhance system performance, has been proposed. However, the influence of external and contextual factors on the detectability of ErrPs remains poorly understood, especially in multitasking scenarios involving both BCI operations and sensorimotor control. Herein, we hypothesized that the difficulty in sensorimotor control would lead to the dispersion of neural resources in multitasking, resulting in a reduction in ErrP features. To examine this, we conducted an experiment in which participants were instructed to keep a ball within a designated area on a board, while simultaneously attempting to control a cursor on a display through motor imagery. The BCI provided error feedback with a random probability of 30%. Three scenarios-without a ball (single-task), lightweight ball (easy-task), and heavyweight ball (hard-task)-were used for the characterization of ErrPs based on the difficulty of sensorimotor control. In addition, to examine the impact of multitasking on ErrP-BCI performance, we analyzed single-trial classification accuracy offline. Contrary to our hypothesis, varying the difficulty of sensorimotor control did not result in significant changes in ErrP features. However, multitasking significantly affected ErrP classification accuracy. Post-hoc analyses revealed that the classifier trained on single-task ErrPs exhibited reduced accuracy under hard-task scenarios. To our knowledge, this study is the first to investigate how ErrPs are modulated in a multitasking environment involving both sensorimotor control and BCI operation in an offline framework. Although the ErrP features remained unchanged, the observed variation in accuracy suggests the need to design classifiers that account for task load even before implementing a real-time ErrP-based BCI.

Introduction: Previous researches have often underestimated the diversity and combined effects of risk factors for Parkinson's disease (PD). This study aimed to identify how multiple modifiable risk factors collectively impact PD.

Methods: The study included 452,492 participants from the UK Biobank, utilizing genetic data and 255 phenotypic variables. A broad exposure association study was conducted across seven domains: socioeconomic status, medical history, psychosocial factors, physical measures, early life, local environment, and lifestyle. Risk scores of each domain for each participant were generated. The joint effects of modifiable and genetic risks assessed using Cox proportional hazards model. Population attributable fraction (PAF) was estimated to quantify contribution ratio of risk factors in different domains to the occurrence of PD.

Results: Multiple risk factors significantly (p < 1.96 × 10^-4) associated with PD was observed. The top 5 factors were hand grip strength (hazard ratio (HR) = 0.98, p = 1.59 × 10^-24), long-standing illness (HR = 1.38, p = 3.63 × 10^-20), self-reported nervousness (HR = 1.56, p = 5.9 × 10^-20), ever suffered from mental health concerns (HR = 1.42, p = 5.48 × 10^-18) and chest pain (HR = 1.42, p = 1.43 × 10^-18). Individuals with unfavorable medical history, psychosocial factors, physical measures, and lifestyle had an increased risk of PD by 33 to 51% compared to those with favorable factors (p < 0.001).

Discussion: Results indicated that addressing modifiable risk factors, especially in physical measures and psychological factors, could potentially prevent up to 33.87% of PD cases. In formulating prevention strategies, it is recommended to prioritize domains such as physical measures, psychosocial factors, lifestyle, and medical history.

Unilateral moyamoya disease (U-MMD) is a chronic vascular disease characterized by progressive stenosis and occlusion of the terminal end of the internal carotid artery and its main branches, resulting in the appearance of moyamoya-like blood vessels at the base of the brain. The etiology of U-MMD is unknown, it accounts for 9.7-17.8% of all moyamoya disease, and the family incidence is 5.5-13.3%. The clinical characteristics are similar to those of typical moyamoya disease, but there are some differences. U-MMD can progress to bilateral moyamoya disease with a median probability of 29.01% (ranging from 6.3 to 58.8%), and there are many risk factors that promote its development. Surgical treatment can effectively reduce the incidence of ischemic stroke and improve prognosis. However, the timing and indications for surgery require further investigation. This article reviews the latest research progress on the etiology, epidemiology, clinical and radiological characteristics, progression, treatment, and prognosis of U-MMD.

The potential and utility of inner speech is pivotal for developing practical, everyday Brain-Computer Interface (BCI) applications, as it represents a type of brain signal that operates independently of external stimuli however it is largely underdeveloped due to the challenges faced in deciphering its signals. In this study, we evaluated the behaviors of various Machine Learning (ML) and Deep Learning (DL) models on a publicly available dataset, employing popular preprocessing methods as feature extractors to enhance model training. We face significant challenges like subject-dependent variability, high noise levels, and overfitting. To address overfitting in particular, we propose using "BruteExtraTree": a new classifier which relies on moderate stochasticity inherited from its base model, the ExtraTreeClassifier. This model not only matches the best DL model, ShallowFBCSPNet, in the subject-independent scenario in our experiments scoring 32% accuracy, but also surpasses the state-of-the-art by achieving 46.6% average per-subject accuracy in the subject-dependent case. Our results on the subject-dependent case show promise on the possibility of a new paradigm for using inner speech data inspired from LLM pretraining but we also highlight the crucial need for a drastic change in data recording or noise removal methods to open the way for more practical accuracies in the subject-independent case.

Introduction: Mild-to-moderate traumatic brain injury (mmTBI) that lead to deficits in balance and gait are difficult to resolve through standard therapy protocols, and these deficits can severely impact a patient's quality of life. Recently, translingual neural stimulation (TLNS) has emerged as a potential therapy for mmTBI-related balance and gait deficits by inducing neuroplastic changes in the brain gray matter structure. However, it is still unclear how interactions within and between functional networks in brain are affected by TLNS. The current study aimed to extend our previous resting-state functional connectivity (RSFC) study investigating the effects of TLNS intervention on outcome measures related to gait and balance.

Methods: An experimental PoNS device was utilized to deliver the TLNS. The 2-week TLNS intervention program, specifically stimulation during focused physical therapy focused on recovery of gait and balance, included twice-daily treatment in the laboratory and the same program at home during the intervening weekend. The resting-state fMRI datasets at pre- and post-interventions were collected by 3T MRI scanner with nine mmTBI patients. All participants also received both Sensory Organization Test (SOT) and Dynamic Gait Index (DGI) testing pre- and post-intervention as part of the behavioral assessment.

Results: Compared to baseline, TLNS intervention led to statistically significant improvements in both the SOT [t ₍₈₎ = 2.742, p = 0.028] and the DGI [t ₍₈₎ = 2.855, p = 0.024] scores. Moreover, significant increases in intra- and inter-network RSFC were observed, particularly within the visual, default mode, dorsal attention, frontoparietal (FPN), and somatosensory (SMN) networks. Additionally, there were significant correlations between the SOT and inter-network FC [between FPN and SMN, r ₍₉₎ = -0.784, p = 0.012] and between the DGI and intra-network FC [within SMN, r ₍₉₎ = 0.728, p = 0.026].

Discussion: These findings suggest that TLNS intervention is an effective in increasing somatosensory processing, vestibular-visual interaction, executive control and flexible shifting, and TLNS may be an effective approach to inducing brain network plasticity and may serve as a potential therapy for mmTBI-related gait and balance deficits.