Musical experience enhances time discrimination: Evidence from cortical responses.

Time discrimination, a critical aspect of auditory perception, is influenced by numerous factors. Previous research has suggested that musical experience can restructure the brain, thereby enhancing time discrimination. However, this phenomenon remains underexplored. In this study, we seek to elucidate the enhancing effect of musical experience on time discrimination, utilizing both behavioral and electroencephalogram methodologies. Additionally, we aim to explore, through brain connectivity analysis, the role of increased connectivity in brain regions associated with auditory perception as a potential contributory factor to time discrimination induced by musical experience. The results show that the music-experienced group demonstrated higher behavioral accuracy, shorter reaction time, and shorter P3 and mismatch response latencies as compared to the control group. Furthermore, the music-experienced group had higher connectivity in the left temporal lobe. In summary, our research underscores the positive impact of musical experience on time discrimination and suggests that enhanced connectivity in brain regions linked to auditory perception may be responsible for this enhancement.

Airborne Nanoplastics Exposure Inducing Irreversible Glucose Increase and Complete Hepatic Insulin Resistance.

As an emerging type of pollutant, microplastics have become a global environmental problem. Approximately, a fifth of the global burden of type 2 diabetes can be attributed to air particulate pollution. However, scientific knowledge remains limited about the effects of airborne nanoplastics (NPs) exposure on metabolic diseases. In this experiment, a whole-body exposure system was used to simulate the real atmospheric environment, and three exposure concentrations combined with the actual environmental concentration were selected to explore the effects of airborne NPs on metabolic diseases. Based on histological analyses, metabolic studies, gene expression, metabolites, and molecular signaling analyses, mice exposed to airborne NPs were observed to show a phenotype of systemic inflammation and complete insulin resistance featuring excessive drinking and eating, weight loss, elevated blood glucose, and decreased triglyceride levels. After airborne NPs exposure, mice were intolerant to glucose and tolerant to insulin. In addition, airborne NPs exposure could result in long-term irreversible hyperglycemia. Together, the research findings provide a strong basis for understanding the hazards of airborne nanopollution on metabolic disorders.

Electroencephalogram-based objective assessment of cognitive function level associated with age-related hearing loss.

Age-Related Hearing Loss (ARHL) is a common problem in aging. Numerous longitudinal cohort studies have revealed that ARHL is closely related to cognitive function, leading to a significant risk of cognitive decline and dementia. This risk gradually increases with the severity of hearing loss. We designed dual auditory Oddball and cognitive task paradigms for the ARHL subjects, then obtained the Montreal Cognitive Assessment (MoCA) scale evaluation results for all the subjects. Multi-dimensional EEG characteristics helped explore potential biomarkers to evaluate the cognitive level of the ARHL group, having a significantly lower P300 peak amplitude coupled with a prolonged latency. Moreover, visual memory, auditory memory, and logical calculation were investigated during the cognitive task paradigm. In the ARHL groups, the alpha-to-beta rhythm energy ratio in the visual and auditory memory retention period and the wavelet packet entropy value within the logical calculation period were significantly reduced. Correlation analysis between the above specificity indicators and the subjective scale results of the ARHL group revealed that the auditory P300 component characteristics could assess attention resources and information processing speed. The alpha and beta rhythm energy ratio and wavelet packet entropy can become potential indicators to determine working memory and logical cognitive computation-related cognitive ability.

Evidence for modulation of EEG microstates by mental workload levels and task types.

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

The scalp time-varying network of auditory spatial attention in "cocktail-party" situations.

Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.

OCT imaging of endolymphatic hydrops in mice: association with hearing loss.

BACKGROUND: The etiology of Ménière's disease (MD) is still not completely clear, but it is believed to be associated with endolymphatic hydrops (EH), which is characterized by auditory functional disorders. Vasopressin injection in C57BL/6J mice can induce EH and serve as a model for MD. Optical Coherence Tomography (OCT) has shown its advantages as a non-invasive imaging method for observing EH.AimInvestigating the relationship between hearing loss and EH to assist clinical hearing assessments and indicate the severity of hydrops. METHODS: C57BL/6J mice received 50 µg/100g/day vasopressin injections to induce EH. Auditory function was assessed using auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAE). OCT was used to visualize the cochlea. RESULT: OCT observed accumulation of fluid within the scala media in the cochlear apex. ABR showed significant hearing loss after 4 weeks. DPOAE revealed low-frequency hearing loss at 2 weeks and widespread damage across frequencies at 4 weeks. CONCLUSION: The development of hearing loss in mouse models of MD is consistent with EH manifestations.SignificanceThis study demonstrates the possibility of indirectly evaluating the extent of EH through auditory assessment and emphasizes the significant value of OCT for imaging cochlear structures.

EEG-based assessment of temporal fine structure and envelope effect in mandarin syllable and tone perception.

In recent years, speech perception research has benefited from low-frequency rhythm entrainment tracking of the speech envelope. However, speech perception is still controversial regarding the role of speech envelope and temporal fine structure, especially in Mandarin. This study aimed to discuss the dependence of Mandarin syllables and tones perception on the speech envelope and the temporal fine structure. We recorded the electroencephalogram (EEG) of the subjects under three acoustic conditions using the sound chimerism analysis, including (i) the original speech, (ii) the speech envelope and the sinusoidal modulation, and (iii) the fine structure of time and the modulation of the non-speech (white noise) sound envelope. We found that syllable perception mainly depended on the speech envelope, while tone perception depended on the temporal fine structure. The delta bands were prominent, and the parietal and prefrontal lobes were the main activated brain areas, regardless of whether syllable or tone perception was involved. Finally, we decoded the spatiotemporal features of Mandarin perception from the microstate sequence. The spatiotemporal feature sequence of the EEG caused by speech material was found to be specific, suggesting a new perspective for the subsequent auditory brain-computer interface. These results provided a new scheme for the coding strategy of new hearing aids for native Mandarin speakers.

PSIs of EEG With Refined Frequency Decomposition Could Prognose Motor Recovery Before Rehabilitation Intervention.

Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function ( ∆FMU) among participants with chronic stroke. EEG data from resting and motor imagery task were recorded from 13 participants with chronic stroke. Three functional connectivity methods, which were Pearson correlation measure (PCM), weighted Phase Lag Index (wPLI) and phase synchronization index (PSI), were investigated, under three regions of interest (inter-hemispheric, intra-hemispheric, and whole-brain), in two statues (resting and motor imagery), with 15 refined center frequencies. We applied correlation analysis to identify the optimal center frequencies and pairs of synchronized channels that were consistently associated with ∆FMU . Predictive models were generated using regression analysis algorithms based on optimized center frequencies and channel pairs identified from the proposed analysis method, with leave-one-out cross-validation. We found that PSI in the Alpha band (with center frequency of 9Hz) was the most sensitive FC measures for prognosing motor recovery. Strong and significant correlations were identified between the predictions and actual ∆FMU scores both in the resting state ( [Formula: see text], [Formula: see text], N=13) and motor imagery ( [Formula: see text], [Formula: see text], N=13). Our results suggested that EEG connectivity measured with PSI in resting state could be a promising biomarker for quantifying motor recovery before motor rehabilitation intervention.

Self-triggered thermoelectric nanoheterojunction for cancer catalytic and immunotherapy.

The exogenous excitation requirement and electron-hole recombination are the key elements limiting the application of catalytic therapies. Here a tumor microenvironment (TME)-specific self-triggered thermoelectric nanoheterojunction (Bi0.5Sb1.5Te3/CaO2 nanosheets, BST/CaO2 NSs) with self-built-in electric field facilitated charge separation is fabricated. Upon exposure to TME, the CaO2 coating undergoes rapid hydrolysis, releasing Ca2+, H2O2, and heat. The resulting temperature difference on the BST NSs initiates a thermoelectric effect, driving reactive oxygen species production. H2O2 not only serves as a substrate supplement for ROS generation but also dysregulates Ca2+ channels, preventing Ca2+ efflux. This further exacerbates calcium overload-mediated therapy. Additionally, Ca2+ promotes DC maturation and tumor antigen presentation, facilitating immunotherapy. It is worth noting that the CaO2 NP coating hydrolyzes very slowly in normal cells, releasing Ca2+ and O2 without causing any adverse effects. Tumor-specific self-triggered thermoelectric nanoheterojunction combined catalytic therapy, ion interference therapy, and immunotherapy exhibit excellent antitumor performance in female mice.

Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques.

Objective: In this study, we aimed to investigate the classification of symptomatic plaques by evaluating the models generated via two different approaches, a radiomics-based machine learning (ML) approach, and an end-to-end learning approach which utilized deep learning (DL) techniques with several representative model frameworks. Methods: We collected high-resolution magnetic resonance imaging (HRMRI) data from 104 patients with carotid artery stenosis, who were diagnosed with either symptomatic plaques (SPs) or asymptomatic plaques (ASPs), in two medical centers. 74 patients were diagnosed with SPs and 30 patients were ASPs. Sampling Perfection with Application-optimized Contrasts (SPACE) by using different flip angle Evolutions was used for MRI imaging. Repeated stratified five-fold cross-validation was used to evaluate the accuracy and receiver operating characteristic (ROC) of the trained classifier. The two proposed approaches were investigated to train the models separately. The difference in the model performance of the two proposed methods was quantitatively evaluated to find a better model to differentiate between SPs and ASPs. Results: 3D-SE-Densenet-121 model showed the best performance among all prediction models (AUC, accuracy, precision, sensitivity, and F1-score of 0.9300, 0.9308, 0.9008, 0.8588, and 0.8614, respectively), which were 0.0689, 0.1119, 0.1043, 0.0805, and 0.1089 higher than the best radiomics-based ML model (MLP). Decision curve analysis showed that the 3D-SE-Densenet-121 model delivered more net benefit than the best radiomics-based ML model (MLP) with a wider threshold probability. Conclusion: The DL models were able to accurately differentiate between symptomatic and asymptomatic carotid plaques with limited data, which outperformed radiomics-based ML models in identifying symptomatic plaques.

Assessing vestibular function using electroencephalogram rhythms evoked during the caloric test.

Introduction: The vestibular system is responsible for motion perception and balance preservation in the body. The vestibular function examination is useful for determining the cause of associated symptoms, diagnosis, and therapy of the patients. The associated cerebral cortex processes and integrates information and is the ultimate perceptual site for vestibular-related symptoms. In recent clinical examinations, less consideration has been given to the cortex associated with the vestibular system. As a result, it is crucial to increase focus on the expression of the cortical level while evaluating vestibular function. From the viewpoint of neuroelectrophysiology, electroencephalograms (EEG) can enhance the assessments of vestibular function at the cortex level. Methods: This study recorded nystagmus and EEG data throughout the caloric test. Four phases were considered according to the vestibular activation status: before activation, activation, fixation suppression, and recovery. In different phases, the distribution and changes of the relative power of the EEG rhythms (delta, theta, alpha, and beta) were analyzed, and the correlation between EEG characteristics and nystagmus was also investigated. Results: The results showed that, when the vestibule was activated, the alpha power of the occipital region increased, and the beta power of the central and top regions and the occipital region on the left decreased. The changes in the alpha and beta rhythms significantly correlate with nystagmus values in left warm stimulation. Discussion: Our findings offer a fresh perspective on cortical electrophysiology for the assessment of vestibular function by demonstrating that the relative power change in EEG rhythms can be used to assess vestibular function.

Microstructural interactions contribute to the hotspot in the living cochlea.

The mechanism of the active cochlea relies on a complex interaction between microstructures in the organ of Corti. A significant longitudinal vibration "hotspot" was recently observed in the high-frequency region of the living gerbil cochlea between the Deiters cells and the outer hair cells. A similar phenomenon was also found in guinea pigs with a relatively smaller magnitude. The cause is unknown, but one hypothesis is that this phenomenon is due to the structural constraints between different microstructures. It is not easy to explain the mechanism of hotspots directly from experimental observations. It may also be difficult to image or test if the hotspot will occur in the low-frequency region in the cochlea. We built two three-dimensional finite element models corresponding to the high- and low-frequency regions in the guinea pig cochlea. Responses of the organ of Corti to passive acoustic and outer hair cell electrical excitation were calculated. The two excitations were then superimposed to predict the active response of the organ of Corti. The hotspot phenomenon in the experiment was reproduced and analyzed in-depth about influencing factors. Our results indicate that hotspots appear in the low-frequency region of the cochlea as well. We hypothesize that the hotspot is a locally originated phenomenon in the cochlea, and the traveling wave further enhances the response to low-frequency excitation. The movement of outer hair cells inclined in the longitudinal direction is the leading cause of the hotspot.

A multi-frame network model for predicting seizure based on sEEG and iEEG data.

Introduction: Analysis and prediction of seizures by processing the EEG signals could assist doctors in accurate diagnosis and improve the quality of the patient's life with epilepsy. Nowadays, seizure prediction models based on deep learning have become one of the most popular topics in seizure studies, and many models have been presented. However, the prediction results are strongly related to the various complicated pre-processing strategies of models, and cannot be directly applied to raw data in real-time applications. Moreover, due to the inherent deficiencies in single-frame models and the non-stationary nature of EEG signals, the generalization ability of the existing model frameworks is generally poor. Methods: Therefore, we proposed an end-to-end seizure prediction model in this paper, where we designed a multi-frame network for automatic feature extraction and classification. Instance and sequence-based frames are proposed in our approach, which can help us simultaneously extract features of different modes for further classification. Moreover, complicated pre-processing steps are not included in our model, and the novel frames can be directly applied to the raw data. It should be noted that the approaches proposed in the paper can be easily used as the general model which has been validated and compared with existing model frames. Results: The experimental results showed that the multi-frame network proposed in this paper was superior to the existing model frame in accuracy, sensitivity, specificity, F1-score, and AUC in the classification performance of EEG signals. Discussion: Our results provided a new research idea for this field. Researchers can further integrate the idea of the multi-frame network into the state-of-the-art single-frame seizure prediction models and then achieve better results.

Design and optimization of auditory prostheses using the finite element method: a narrative review.

Background and Objective: An auditory prosthesis refers to a device designed to restore hearing. Some parameters of the auditory prosthesis, such as mass, implanted position, and degree, need to be repeatedly designed and optimized based on the realistic geometry of the ear. Numerous auditory prostheses designs were based on animal or specimen experiments involving many complex instruments, and the experimental specimens had low repeatability. The finite element method (FEM) can overcome these disadvantages and be carried out on the computer with substantial flexibility in modifying the prosthetic parameters to optimize them. This narrative review aims to analyze the recent advances in the design and optimization of auditory prostheses using the FEM and provides suggestions for future development. Methods: The literature on the design of auditory prostheses using the FEM has been extensively studied using the PubMed and Web of Science databases, including different ear models and relevant parameters of different auditory prostheses that need to be designed and optimized. Key Content and Findings: The process of designing and optimizing a prosthesis using the FEM includes building an ear model and a prosthesis model to simulate the implantation process. The related parameters of the prosthesis can be designed and modified conveniently. The post-implantation response could be used as an indicator to evaluate the prosthesis's performance. Conclusions: The review concluded that the FEM had been widely studied in designing and optimizing middle ear implants and cochlear implants and obtained good results. FEM can be utilized to explore more effective directions for auditory prosthesis design and optimization in the future.

Auditory attention decoding from EEG-based Mandarin speech envelope reconstruction.

In the cocktail party circumstance, the human auditory system extracts the information from a specific speaker of interest and ignores others. Many studies have focused on auditory attention decoding (AAD), but the stimulation materials were mainly non-tonal languages. We used a tonal language (Mandarin) as the speech stimulus and constructed a Long Short-Term Memory (LSTM) architecture for speech envelope reconstruction based on electroencephalogram (EEG) data. The correlation coefficient between the reconstructed and candidate envelopes was calculated to determine the subject's auditory attention. The proposed LSTM architecture outperformed the linear models. The average decoding accuracy in cross-subject and inter-subject cases varies from 63.02 to 74.29%, with the highest accuracy rate of 89.1% in a decision window of 0.15 s. In addition, the beta-band rhythm was found to play an essential role in identifying the attention and the non-attention state. These results provide a new AAD architecture to help develop neuro-steered hearing devices, especially for tonal languages.

Decoding selective auditory attention with EEG using a transformer model.

The human auditory system extracts valid information in noisy environments while ignoring other distractions, relying primarily on auditory attention. Studies have shown that the cerebral cortex responds differently to the sound source locations and that auditory attention is time-varying. In this work, we proposed a data-driven encoder-decoder architecture model for auditory attention detection (AAD), denoted as AAD-transformer. The model contains temporal self-attention and channel attention modules and could reconstruct the speech envelope by dynamically assigning weights according to the temporal self-attention and channel attention mechanisms of electroencephalogram (EEG). In addition, the model is conducted based on data-driven without additional preprocessing steps. The proposed model was validated using a binaural listening dataset, in which the speech stimulus was Mandarin, and compared with other models. The results showed that the decoding accuracy of the AAD-transformer in the 0.15-second decoding time window was 76.35%, which was much higher than the accuracy of the linear model using temporal response function in the 3-second decoding time window (increased by 16.27%). This work provides a novel auditory attention detection method, and the data-driven characteristic makes it convenient for neural-steered hearing devices, especially those who speak tonal languages.

Screening Tools and Assessment Methods of Cognitive Decline Associated With Age-Related Hearing Loss: A Review.

Strong links between hearing and cognitive function have been confirmed by a growing number of cross-sectional and longitudinal studies. Seniors with age-related hearing loss (ARHL) have a significantly higher cognitive impairment incidence than those with normal hearing. The correlation mechanism between ARHL and cognitive decline is not fully elucidated to date. However, auditory intervention for patients with ARHL may reduce the risk of cognitive decline, as early cognitive screening may improve related treatment strategies. Currently, clinical audiology examinations rarely include cognitive screening tests, partly due to the lack of objective quantitative indicators with high sensitivity and specificity. Questionnaires are currently widely used as a cognitive screening tool, but the subject's performance may be negatively affected by hearing loss. Numerous electroencephalogram (EEG) and magnetic resonance imaging (MRI) studies analyzed brain structure and function changes in patients with ARHL. These objective electrophysiological tools can be employed to reveal the association mechanism between auditory and cognitive functions, which may also find biological markers to be more extensively applied in assessing the progression towards cognitive decline and observing the effects of rehabilitation training for patients with ARHL. In this study, we reviewed clinical manifestations, pathological changes, and causes of ARHL and discussed their cognitive function effects. Specifically, we focused on current cognitive screening tools and assessment methods and analyzed their limitations and potential integration.

Conductive hearing loss in large vestibular aqueduct syndrome -clinical observations and proof-of-concept predictive modeling by a biomechanical approach.

OBJECTIVE: The purpose of this study was to investigate the effect of a dilated vestibular aqueduct on conductive hearing loss (CHL). A biomechanical method was proposed for modeling the patterns of CHL in patients with large vestibular aqueduct syndrome (LVAS). STUDY DESIGN: High resolution computed tomography (CT) scans and pure tone audiometry (PTA) were retrospectively collected from 16 patients who were diagnosed with LVAS. Seventeen ears with measurable air-bone gaps (ABGs) on PTA were applied for model development. The sizes of midpoint, operculum and distal segment were measured from CT to calculate the biomechanical parameters of each vestibular aqueduct. The mechanical effect of the dilated vestibular aqueduct on sound conduction was simulated using a lumped-parameter model. The CHL levels predicted by the model were compared with the observed ABGs at 250, 500 and 1000 Hz respectively. RESULTS: The model was able to predict the trend that greater ABGs occurred at lower frequencies, which were consistent with clinical observations. However, deviations between the predicted and the observed ABGs became larger as the frequency increased. None of the correlation coefficients between the radiologic measures and the observed ABG levels were significant. CONCLUSION: These findings lend support to the feasibility of this approach in modeling CHL in LVAS. The presence of a dilated vestibular aqueduct leads to altered impedance and sound pressure, suggesting the impact of a pathological third window. High individual variability of the observed ABGs implies additional factors may also be involved, especially at 500 Hz and 1000 Hz.