Gene Electrotransfer via Conductivity-Clamped Electric Field Focusing Pivots Sensori-Motor DNA Therapeutics: "A Spoonful of Sugar Helps the Medicine Go Down".

Viral vectors and lipofection-based gene therapies have dispersion-dependent transduction/transfection profiles that thwart precise targeting. The study describes the development of focused close-field gene electrotransfer (GET) technology, refining spatial control of gene expression. Integration of fluidics for precise delivery of "naked" plasmid deoxyribonucleic acid (DNA) in sucrose carrier within the focused electric field enables negative biasing of near-field conductivity ("conductivity-clamping"-CC), increasing the efficiency of plasma membrane molecular translocation. This enables titratable gene delivery with unprecedently low charge transfer. The clinic-ready bionics-derived CC-GET device achieved neurotrophin-encoding miniplasmid DNA delivery to the cochlea to promote auditory nerve regeneration; validated in deafened guinea pig and cat models, leading to improved central auditory tuning with bionics-based hearing. The performance of CC-GET is evaluated in the brain, an organ problematic for pulsed electric field-based plasmid DNA delivery, due to high required currents causing Joule-heating and damaging electroporation. Here CC-GET enables safe precision targeting of gene expression. In the guinea pig, reporter expression is enabled in physiologically critical brainstem regions, and in the striatum (globus pallidus region) delivery of a red-shifted channelrhodopsin and a genetically-encoded Ca2+ sensor, achieved photoactivated neuromodulation relevant to the treatment of Parkinson's Disease and other focal brain disorders.

The neural representation of an auditory spatial cue in the primate cortex.

Humans make use of small differences in the timing of sounds at the two ears-interaural time differences (ITDs)-to locate their sources. Despite extensive investigation, however, the neural representation of ITDs in the human brain is contentious, particularly the range of ITDs explicitly represented by dedicated neural detectors. Here, using magneto- and electro-encephalography (MEG and EEG), we demonstrate evidence of a sparse neural representation of ITDs in the human cortex. The magnitude of cortical activity to sounds presented via insert earphones oscillated as a function of increasing ITD-within and beyond auditory cortical regions-and listeners rated the perceptual quality of these sounds according to the same oscillating pattern. This pattern was accurately described by a population of model neurons with preferred ITDs constrained to the narrow, sound-frequency-dependent range evident in other mammalian species. When scaled for head size, the distribution of ITD detectors in the human cortex is remarkably like that recorded in vivo from the cortex of rhesus monkeys, another large primate that uses ITDs for source localization. The data solve a long-standing issue concerning the neural representation of ITDs in humans and suggest a representation that scales for head size and sound frequency in an optimal manner.

Prognostic accuracy of eight triage scores in suspected COVID-19 in an Emergency Department low-income setting: An observational cohort study.

Introduction: Previous studies deriving and validating triage scores for patients with suspected COVID-19 in Emergency Department settings have been conducted in high- or middle-income settings. We assessed eight triage scores' accuracy for death or organ support in patients with suspected COVID-19 in Sudan. Methods: We conducted an observational cohort study using Covid-19 registry data from eight emergency unit isolation centres in Khartoum State, Sudan. We assessed performance of eight triage scores including: PRIEST, LMIC-PRIEST, NEWS2, TEWS, the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS in suspected COVID-19. A composite primary outcome included death, ventilation or ICU admission. Results: In total 874 (33.84 %, 95 % CI:32.04 % to 35.69 %) of 2,583 patients died, required intubation/non-invasive ventilation or HDU/ICU admission . All risk-stratification scores assessed had worse estimated discrimination in this setting, compared to studies conducted in higher-income settings: C-statistic range for primary outcome: 0.56-0.64. At previously recommended thresholds NEWS2, PRIEST and LMIC-PRIEST had high estimated sensitivities (≥0.95) for the primary outcome. However, the high baseline risk meant that low-risk patients identified at these thresholds still had a between 8 % and 17 % risk of death, ventilation or ICU admission. Conclusion: None of the triage scores assessed demonstrated sufficient accuracy to be used clinically. This is likely due to differences in the health care system and population (23 % of patients died) compared to higher-income settings in which the scores were developed. Risk-stratification scores developed in this setting are needed to provide the necessary accuracy to aid triage of patients with suspected COVID-19.

The Benefits of Cochlear Implantation for Adults: A Systematic Umbrella Review.

OBJECTIVES: The uptake of cochlear implants among adults who could benefit (based on pure-tone audiometry) in developed countries is estimated to be less than 10%. Concerns about potential surgical complications, fear of losing residual hearing, and limited awareness about the benefits of this intervention contribute to the low adoption rate. To enhance quality of life and improve the uptake of cochlear implants, it is essential to have a clear understanding of their benefits. DESIGN: This umbrella review aims to summarize the major benefits of cochlear implant usage in adults, by synthesizing findings from published review articles. A comprehensive search of databases including MEDLINE, EMBASE, PsycINFO, and Google Scholar, was conducted. The search was limited to English-language review articles published between 1990 and 2022, focusing on cochlear implant outcomes in at least 5 adults (aged ≥18 years). Two independent reviewers screened titles, abstracts, and full-text articles, and conducted a quality assessment using the Joanna Briggs Checklist for Systematic Reviews and Research Syntheses. RESULTS: Forty-two articles were included in this review. There were 15 systematic reviews with meta-analysis, 25 systematic reviews without meta-analysis, and 2 systematic scoping reviews. All 42 articles underwent quality assessment using the Joanna Briggs Institute Checklist for Systematic Reviews and Research Syntheses, of which 40% (n = 17) satisfied 9 out of 11 quality criteria. This umbrella review shows that cochlear implants are associated with improvements in speech perception and recognition as well as improved quality of life and cognition. These benefits are observed in a significant proportion of adults undergoing the procedure, highlighting its effectiveness as a viable intervention for individuals with severe to profound hearing loss. CONCLUSIONS: The potential benefits of cochlear implantation appear to outweigh the risks and complications associated with the procedure. It is recommended that adults with severe to profound hearing loss in particular, engage in informed discussions with healthcare professionals to consider cochlear implantation as a viable treatment option.

Training and testing of a gradient boosted machine learning model to predict adverse outcome in patients presenting to emergency departments with suspected covid-19 infection in a middle-income setting.

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.

Evidence That Ultrafast Nonquantal Transmission Underlies Synchronized Vestibular Action Potential Generation.

Amniotes evolved a unique postsynaptic terminal in the inner ear vestibular organs called the calyx that receives both quantal and nonquantal (NQ) synaptic inputs from Type I sensory hair cells. The nonquantal synaptic current includes an ultrafast component that has been hypothesized to underlie the exceptionally high synchronization index (vector strength) of vestibular afferent neurons in response to sound and vibration. Here, we present three lines of evidence supporting the hypothesis that nonquantal transmission is responsible for synchronized vestibular action potentials of short latency in the guinea pig utricle of either sex. First, synchronized vestibular nerve responses are unchanged after administration of the AMPA receptor antagonist CNQX, while auditory nerve responses are completely abolished. Second, stimulus evoked vestibular nerve compound action potentials (vCAP) are shown to occur without measurable synaptic delay and three times shorter than the latency of auditory nerve compound action potentials (cCAP), relative to the generation of extracellular receptor potentials. Third, paired-pulse stimuli designed to deplete the readily releasable pool (RRP) of synaptic vesicles in hair cells reveal forward masking in guinea pig auditory cCAPs, but a complete lack of forward masking in vestibular vCAPs. Results support the conclusion that the fast component of nonquantal transmission at calyceal synapses is indefatigable and responsible for ultrafast responses of vestibular organs evoked by transient stimuli.SIGNIFICANCE STATEMENT The mammalian vestibular system drives some of the fastest reflex pathways in the nervous system, ensuring stable gaze and postural control for locomotion on land. To achieve this, terrestrial amniotes evolved a large, unique calyx afferent terminal which completely envelopes one or more presynaptic vestibular hair cells, which transmits mechanosensory signals mediated by quantal and nonquantal (NQ) synaptic transmission. We present several lines of evidence in the guinea pig which reveals the most sensitive vestibular afferents are remarkably fast, much faster than their auditory nerve counterparts. Here, we present neurophysiological and pharmacological evidence that demonstrates this vestibular speed advantage arises from ultrafast NQ electrical synaptic transmission from Type I hair cells to their calyx partners.

Corrigendum: Listening loops and the adapting auditory brain.

[This corrects the article DOI: 10.3389/fnins.2023.1081295.].

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting.

BACKGROUND: Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic. METHODS: Routinely collected data for public hospitals in the Western Cape, South Africa from the 27th August 2020 to 11th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort. RESULTS: We analysed 305,564 derivation, 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment. CONCLUSION: The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings.

External validation of triage tools for adults with suspected COVID-19 in a middle-income setting: an observational cohort study.

BACKGROUND: Tools proposed to triage ED acuity in suspected COVID-19 were derived and validated in higher income settings during early waves of the pandemic. We estimated the accuracy of seven risk-stratification tools recommended to predict severe illness in the Western Cape, South Africa. METHODS: An observational cohort study using routinely collected data from EDs across the Western Cape, from 27 August 2020 to 11 March 2022, was conducted to assess the performance of the PRIEST (Pandemic Respiratory Infection Emergency System Triage) tool, NEWS2 (National Early Warning Score, version 2), TEWS (Triage Early Warning Score), the WHO algorithm, CRB-65, Quick COVID-19 Severity Index and PMEWS (Pandemic Medical Early Warning Score) in suspected COVID-19. The primary outcome was intubation or non-invasive ventilation, death or intensive care unit admission at 30 days. RESULTS: Of the 446 084 patients, 15 397 (3.45%, 95% CI 34% to 35.1%) experienced the primary outcome. Clinical decision-making for inpatient admission achieved a sensitivity of 0.77 (95% CI 0.76 to 0.78), specificity of 0.88 (95% CI 0.87 to 0.88) and the negative predictive value (NPV) of 0.99 (95% CI 0.99 to 0.99). NEWS2, PMEWS and PRIEST scores achieved good estimated discrimination (C-statistic 0.79 to 0.82) and identified patients at risk of adverse outcomes at recommended cut-offs with moderate sensitivity (>0.8) and specificity ranging from 0.41 to 0.64. Use of the tools at recommended thresholds would have more than doubled admissions, with only a 0.01% reduction in false negative triage. CONCLUSION: No risk score outperformed existing clinical decision-making in determining the need for inpatient admission based on prediction of the primary outcome in this setting. Use of the PRIEST score at a threshold of one point higher than the previously recommended best approximated existing clinical accuracy.

Cortical haemodynamic responses predict individual ability to recognise vocal emotions with uninformative pitch cues but do not distinguish different emotions.

We investigated the cortical representation of emotional prosody in normal-hearing listeners using functional near-infrared spectroscopy (fNIRS) and behavioural assessments. Consistent with previous reports, listeners relied most heavily on F0 cues when recognizing emotion cues; performance was relatively poor-and highly variable between listeners-when only intensity and speech-rate cues were available. Using fNIRS to image cortical activity to speech utterances containing natural and reduced prosodic cues, we found right superior temporal gyrus (STG) to be most sensitive to emotional prosody, but no emotion-specific cortical activations, suggesting that while fNIRS might be suited to investigating cortical mechanisms supporting speech processing it is less suited to investigating cortical haemodynamic responses to individual vocal emotions. Manipulating emotional speech to render F0 cues less informative, we found the amplitude of the haemodynamic response in right STG to be significantly correlated with listeners' abilities to recognise vocal emotions with uninformative F0 cues. Specifically, listeners more able to assign emotions to speech with degraded F0 cues showed lower haemodynamic responses to these degraded signals. This suggests a potential objective measure of behavioural sensitivity to vocal emotions that might benefit neurodiverse populations less sensitive to emotional prosody or hearing-impaired listeners, many of whom rely on listening technologies such as hearing aids and cochlear implants-neither of which restore, and often further degrade, the F0 cues essential to parsing emotional prosody conveyed in speech.

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data.

Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correlation and Mutual Information). To bridge the technical gap, this study proposes a Siamese-based Symmetric Positive Definite (SPD) Matrix Representation framework (SiameseSPD-MR) to derive the functional connectivity of brain imaging data (BID) such as Electroencephalography (EEG), thus the alternative application-independent measure (in the form of SPD matrix) can be automatically learnt: (1) SiameseSPD-MR first exploits graph convolution to extract the representative features of BID with the adjacency matrix computed considering the anatomical structure; (2) Adaptive Gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by SPD matrix transformation to address the intrinsic functional characteristics; and (3) Two-branch (Siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. Experimental results on two EEG datasets (autism spectrum disorder, emotion) indicate that (1) SiameseSPD-MR can capture more significant differences in functional connectivity between neural states than the state-of-the-art counterparts do, and these findings properly highlight the typical EEG characteristics of ASD subjects, and (2) the obtained functional connectivity representations conforming to the proposed measure can act as meaningful markers for brain network analysis and ASD discrimination.

Listening loops and the adapting auditory brain.

Analysing complex auditory scenes depends in part on learning the long-term statistical structure of sounds comprising those scenes. One way in which the listening brain achieves this is by analysing the statistical structure of acoustic environments over multiple time courses and separating background from foreground sounds. A critical component of this statistical learning in the auditory brain is the interplay between feedforward and feedback pathways-"listening loops"-connecting the inner ear to higher cortical regions and back. These loops are likely important in setting and adjusting the different cadences over which learned listening occurs through adaptive processes that tailor neural responses to sound environments that unfold over seconds, days, development, and the life-course. Here, we posit that exploring listening loops at different scales of investigation-from in vivo recording to human assessment-their role in detecting different timescales of regularity, and the consequences this has for background detection, will reveal the fundamental processes that transform hearing into the essential task of listening.

Disentangled and Side-Aware Unsupervised Domain Adaptation for Cross-Dataset Subjective Tinnitus Diagnosis.

EEG-based tinnitus classification is a valuable tool for tinnitus diagnosis, research, and treatments. Most current works are limited to a single dataset where data patterns are similar. But EEG signals are highly non-stationary, resulting in model's poor generalization to new users, sessions or datasets. Thus, designing a model that can generalize to new datasets is beneficial and indispensable. To mitigate distribution discrepancy across datasets, we propose to achieve Disentangled and Side-aware Unsupervised Domain Adaptation (DSUDA) for cross-dataset tinnitus diagnosis. A disentangled auto-encoder is developed to decouple class-irrelevant information from the EEG signals to improve the classifying ability. The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification. It also aligns signals of left and right ears to overcome inherent EEG pattern difference. We compare DSUDA with state-of-the-art methods, and our model achieves significant improvements over competitors regarding comprehensive evaluation criteria. The results demonstrate our model can successfully generalize to a new dataset and effectively diagnose tinnitus.

Cross-modal functional connectivity supports speech understanding in cochlear implant users.

Sensory deprivation can lead to cross-modal cortical changes, whereby sensory brain regions deprived of input may be recruited to perform atypical function. Enhanced cross-modal responses to visual stimuli observed in auditory cortex of postlingually deaf cochlear implant (CI) users are hypothesized to reflect increased activation of cortical language regions, but it is unclear if this cross-modal activity is "adaptive" or "mal-adaptive" for speech understanding. To determine if increased activation of language regions is correlated with better speech understanding in CI users, we assessed task-related activation and functional connectivity of auditory and visual cortices to auditory and visual speech and non-speech stimuli in CI users (n = 14) and normal-hearing listeners (n = 17) and used functional near-infrared spectroscopy to measure hemodynamic responses. We used visually presented speech and non-speech to investigate neural processes related to linguistic content and observed that CI users show beneficial cross-modal effects. Specifically, an increase in connectivity between the left auditory and visual cortices-presumed primary sites of cortical language processing-was positively correlated with CI users' abilities to understand speech in background noise. Cross-modal activity in auditory cortex of postlingually deaf CI users may reflect adaptive activity of a distributed, multimodal speech network, recruited to enhance speech understanding.

LMIC-PRIEST: Derivation and validation of a clinical severity score for acutely ill adults with suspected COVID-19 in a middle-income setting.

Background: Uneven vaccination and less resilient health care systems mean hospitals in LMICs are at risk of being overwhelmed during periods of increased COVID-19 infection. Risk-scores proposed for rapid triage of need for admission from the emergency department (ED) have been developed in higher-income settings during initial waves of the pandemic. Methods: Routinely collected data for public hospitals in the Western Cape, South Africa from the 27 th August 2020 to 11 th March 2022 were used to derive a cohort of 446,084 ED patients with suspected COVID-19. The primary outcome was death or ICU admission at 30 days. The cohort was divided into derivation and Omicron variant validation sets. We developed the LMIC-PRIEST score based on the coefficients from multivariable analysis in the derivation cohort and existing triage practices. We externally validated accuracy in the Omicron period and a UK cohort. Results: We analysed 305,564, derivation 140,520 Omicron and 12,610 UK validation cases. Over 100 events per predictor parameter were modelled. Multivariable analyses identified eight predictor variables retained across models. We used these findings and clinical judgement to develop a score based on South African Triage Early Warning Scores and also included age, sex, oxygen saturation, inspired oxygen, diabetes and heart disease. The LMIC-PRIEST score achieved C-statistics: 0.82 (95% CI: 0.82 to 0.83) development cohort; 0.79 (95% CI: 0.78 to 0.80) Omicron cohort; and 0.79 (95% CI: 0.79 to 0.80) UK cohort. Differences in prevalence of outcomes led to imperfect calibration in external validation. However, use of the score at thresholds of three or less would allow identification of very low-risk patients (NPV ≥0.99) who could be rapidly discharged using information collected at initial assessment. Conclusion: The LMIC-PRIEST score shows good discrimination and high sensitivity at lower thresholds and can be used to rapidly identify low-risk patients in LMIC ED settings. What is already known on this subject: Uneven vaccination in low- and middle-income countries (LMICs) coupled with less resilient health care provision mean that emergency health care systems in LMICs may still be at risk of being overwhelmed during periods of increased COVID-19 infection.Risk-stratification scores may help rapidly triage need for hospitalisation. However, those proposed for use in the ED for patients with suspected COVID-19 have been developed and validated in high-income settings. What this study adds: The LMIC-PRIEST score has been robustly developed using a large routine dataset from the Western Cape, South Africa and is directly applicable to existing triage practices in LMICs.External validation across both income settings and COVID-19 variants showed good discrimination and high sensitivity (at lower thresholds) to a composite outcome indicating need for inpatient admission from the ED. How this study might affect research practice or policy: Use of the LMIC-PRIEST score at thresholds of three or less would allow identification of very low-risk patients (negative predictive value ≥0.99) across all settings assessedDuring periods of increased demand, this could allow the rapid identification and discharge of patients from the ED using information collected at initial assessment.

The hunt for hidden hearing loss in humans: From preclinical studies to effective interventions.

Many individuals experience hearing problems that are hidden under a normal audiogram. This not only impacts on individual sufferers, but also on clinicians who can offer little in the way of support. Animal studies using invasive methodologies have developed solid evidence for a range of pathologies underlying this hidden hearing loss (HHL), including cochlear synaptopathy, auditory nerve demyelination, elevated central gain, and neural mal-adaptation. Despite progress in pre-clinical models, evidence supporting the existence of HHL in humans remains inconclusive, and clinicians lack any non-invasive biomarkers sensitive to HHL, as well as a standardized protocol to manage hearing problems in the absence of elevated hearing thresholds. Here, we review animal models of HHL as well as the ongoing research for tools with which to diagnose and manage hearing difficulties associated with HHL. We also discuss new research opportunities facilitated by recent methodological tools that may overcome a series of barriers that have hampered meaningful progress in diagnosing and treating of HHL.

Noise helps cochlear implant listeners to categorize vowels.

Theoretical studies demonstrate that controlled addition of noise can enhance the amount of information transmitted by a cochlear implant (CI). The present study is a proof-of-principle for whether stochastic facilitation can improve the ability of CI users to categorize speech sounds. Analogue vowels were presented to CI users through a single electrode with independent noise on multiple electrodes. Noise improved vowel categorization, particularly in terms of an increase in information conveyed by the first and second formant. Noise, however, did not significantly improve vowel recognition: the miscategorizations were just more consistent, giving the potential to improve with experience.

Deep reinforcement learning guided graph neural networks for brain network analysis.

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks.