Frontal gamma-alpha ratio reveals neural oscillatory mechanism of attention shifting in tinnitus.

In clinical practice, the symptoms of tinnitus patients can be temporarily alleviated by diverting their attention away from disturbing sounds. However, the precise mechanisms through which this alleviation occurs are still not well understood. Here, we aimed to directly evaluate the role of attention in tinnitus alleviation by conducting distraction tasks with multilevel loads and resting-state tests among 52 adults with tinnitus and 52 healthy controls.

"Pre-train, prompt" Framework to Boost Graph Neural Networks Performance in EEG Analysis.

Electroencephalography (EEG) is a vital noninvasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex nonEuclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a "pre-train, prompt" framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL).

Enhancing medical English proficiency: the current status and development potential of peer-assisted learning in medical education.

This narrative review investigates the current implementation and future potential of Peer-Assisted Learning (PAL) in medical education, specifically emphasizing its role in enhancing medical English proficiency. The article analyzes the effectiveness of PAL across various medical education contexts, including primary medical courses, doctor-patient communication, and standardized residency training. The findings indicate that PAL positively impacts student learning outcomes and promotes professional development, highlighting the necessity of its application in medical English instruction.

Impaired network organization in mild age-related hearing loss.

Age-related hearing loss (ARHL) is considered one of the most common neurodegenerative disorders in the elderly; however, how it contributes to cognitive decline is poorly understood. With resting-state functional magnetic resonance imaging from 66 individuals with ARHL and 54 healthy controls, group spatial independent component analyses, sliding window analyses, graph-theory methods, multilayer networks, and correlation analyses were used to identify ARHL-induced disturbances in static and dynamic functional network connectivity (sFNC/dFNC), alterations in global network switching and their links to cognitive performances. ARHL was associated with decreased sFNC/dFNC within the default mode network (DMN) and increased sFNC/dFNC between the DMN and central executive, salience (SN), and visual networks.

Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture.

Objectives: Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus.

Reorganization of the cortical connectome functional gradient in age-related hearing loss.

Age-related hearing loss (ARHL), one of the most common sensory deficits in elderly individuals, is a risk factor for dementia; however, it is unclear how ARHL affects the decline in cognitive function. To address this issue, a connectome gradient framework was used to identify critical features of information integration between sensory and cognitive processing centers using resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ARHL and 36 healthy controls (HCs). The first three functional gradient alterations associated with ARHL were investigated at the global, network and regional levels.

Abnormal Functional Connectivity Within Default Mode Network and Salience Network Related to Tinnitus Severity.

Background: Previous studies have demonstrated that tinnitus is associated with neural changes in the cerebral cortex. This study is aimed at investigating the central nervous characteristics of tinnitus patients with different severity by using a rs-EEG.

Participants And Methods: rs-EEG was recorded in fifty-seven patients with chronic tinnitus and twenty-seven healthy controls.

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning.

Electroencephalogram (EEG) is an important technology to explore the central nervous mechanism of tinnitus. However, it is hard to obtain consistent results in many previous studies for the high heterogeneity of tinnitus. In order to identify tinnitus and provide theoretical guidance for the diagnosis and treatment, we propose a robust, data-efficient multi-task learning framework called Multi-band EEG Contrastive Representation Learning (MECRL).

Age-related hearing loss accelerates the decline in fast speech comprehension and the decompensation of cortical network connections.

Patients with age-related hearing loss face hearing difficulties in daily life. The causes of age-related hearing loss are complex and include changes in peripheral hearing, central processing, and cognitive-related abilities. Furthermore, the factors by which aging relates to hearing loss via changes in auditory processing ability are still unclear.

Efficacy of Tailor-Made Notched Music Training Versus Tinnitus Retraining Therapy in Adults With Chronic Subjective Tinnitus: A Randomized Controlled Clinical Trial.

Objectives: Chronic subjective tinnitus can have a serious effect on daily life, even causing serious psychological disorders. Currently there are no specific effective solutions or cures. Tailor-made notched music training (TMNMT) is a recently proposed sound therapy that has simpler processes and a higher compliance rate than tinnitus retraining therapy (TRT), a widely used treatment for chronic subjective tinnitus.

A deep learning approach to the diagnosis of atelectasis and attic retraction pocket in otitis media with effusion using otoscopic images.

Background: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images.

Method: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level.

Comparison between 3D-FLAIR and 3D-real IR MRI sequences with visual classification method in the imaging of endolymphatic hydrops in Meniere's disease.

Background: Clinically, the evidence of endolymphatic hydrops (EH) in Meniere's disease (MD) primarily relies on audiological examinations, such as glycerol tests and electrocochleography, to suggest the presence of EH indirectly. However, these techniques lack sensitivity and specificity, and they do not sufficiently assess the degree of EH. This study aims to explore the application of three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) and three-dimensional real inversion recovery (3D-real IR) sequence imaging of EH in MD and to assess the image quality and grading of EH.

Tinnitus Affects Endogenous But Not Exogenous Auditory Attention Orienting.

Purpose: Previous studies have demonstrated that people with tinnitus show attention dysfunctions. In this study, we investigated the influence of tinnitus on attention orienting, especially whether the ability of attention orienting could be modulated by the degree of tinnitus.

Method: Fifty-nine and 54 unilateral tinnitus participants were included in Experiment 1 and Experiment 2, respectively.

Machine Learning in Diagnosing Middle Ear Disorders Using Tympanic Membrane Images: A Meta-Analysis.

Objective: To systematically evaluate the development of Machine Learning (ML) models and compare their diagnostic accuracy for the classification of Middle Ear Disorders (MED) using Tympanic Membrane (TM) images.

Methods: PubMed, EMBASE, CINAHL, and CENTRAL were searched up until November 30, 2021. Studies on the development of ML approaches for diagnosing MED using TM images were selected according to the inclusion criteria.

Aberrant Modulations of Neurocognitive Network Dynamics in Migraine Comorbid With Tinnitus.

Purpose: The possible relationship between migraine and tinnitus still remains elusive although migraine is often accompanied by chronic tinnitus. Several neuroimaging studies have reinforced the cognitive network abnormality in migraine and probably as well as tinnitus. The present work aims to investigate the dynamic neurocognitive network alterations of migraine comorbid with tinnitus.

Prognostic Factors Influencing the Tinnitus Improvement After Idiopathic Sudden Sensorineural Hearing Loss Treatment.

Purpose: Logistic regression analysis was used to explore the factors that influence tinnitus improvement after idiopathic sudden sensorineural hearing loss (ISSNHL) treatment.

Materials And Methods: In this retrospective study, 137 ISSNHL patients with tinnitus were recruited at the Sun Yatsen Memorial Hospital of Sun Yat-sen University, China. They underwent audiological examinations, vestibular assessments, tinnitus examinations, a Tinnitus Handicap Inventory (THI) assessment and ISSNHL treatments.

A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.

Importance: Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients.

Specific brain network predictors of interventions with different mechanisms for tinnitus patients.

Background: The aberrant brain network that gives rise to the phantom sound of tinnitus is believed to determine the effectiveness of tinnitus therapies involving neuromodulation with repetitive transcranial magnetic stimulation (rTMS) and sound therapy utilizing tailor-made notch music training (TMNMT). To test this hypothesis, we determined how effective rTMS or TMNMT were in ameliorating tinnitus in patients with different functional brain networks.

Methods: Resting-state functional MRI was used to construct brain functional networks in patients with tinnitus (41 males/45 females, mean age 49.

Topological features of limbic dysfunction in chronicity of tinnitus with intact hearing: New hypothesis for 'noise-cancellation' mechanism.

Purpose: The reorganization of the limbic regions extend to general cognitive network is believed to exist in the chronicity of tinnitus with particular 'hubs' contributing to a 'noise-cancellation' mechanism. To test this hypothesis, we investigated the topological brain network of tinnitus in different periods.

Methods: Resting-state functional magnetic resonance imaging were obtained from 32 patients with acute tinnitus, 41 patients with chronic tinnitus and 60 age- and gender- matched healthy controls (HC).

Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning.

Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) tools to identify the WAI absorbance characteristics across different frequency-pressure regions in the normal middle ear and ears with otitis media with effusion (OME) to enable diagnosis of middle ear conditions automatically. Data analysis included pre-processing of the WAI data, statistical analysis and classification model development, and key regions extraction from the 2D frequency-pressure WAI images.

Reduced Functional Connectivity in Children With Congenital Cataracts Using Resting-State Electroencephalography Measurement.

Objectives: Numerous task-based functional magnetic resonance imaging studies indicate the presence of compensatory functional improvement in patients with congenital cataracts. However, there is neuroimaging evidence that shows decreased sensory perception or cognition information processing related to visual dysfunction, which favors a general loss hypothesis. This study explored the functional connectivity between visual and other networks in children with congenital cataracts using resting state electroencephalography.

Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.

Objectives: This study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images.

Design: A classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset.

ATM-Dependent Recruitment of BRD7 is required for Transcriptional Repression and DNA Repair at DNA Breaks Flanking Transcriptional Active Regions.

Repair of DNA double-strand breaks (DSBs) is essential for genome integrity, and is accompanied by transcriptional repression at the DSB regions. However, the mechanisms how DNA repair induces transcriptional inhibition remain elusive. Here, it is identified that BRD7 participates in DNA damage response (DDR) and is recruited to the damaged chromatin via ATM signaling.