Weak coupling of neurons enables very high-frequency and ultra-fast oscillations through the interplay of synchronized phase shifts.

Recently, in the past decade, high-frequency oscillations (HFOs), very high-frequency oscillations (VHFOs), and ultra-fast oscillations (UFOs) were reported in epileptic patients with drug-resistant epilepsy. However, to this day, the physiological origin of these events has yet to be understood. Our study establishes a mathematical framework based on bifurcation theory for investigating the occurrence of VHFOs and UFOs in depth EEG signals of patients with focal epilepsy, focusing on the potential role of reduced connection strength between neurons in an epileptic focus. We demonstrate that synchronization of a weakly coupled network can generate very and ultra high-frequency signals detectable by nearby microelectrodes. In particular, we show that a bistability region enables the persistence of phase-shift synchronized clusters of neurons. This phenomenon is observed for different hippocampal neuron models, including Morris-Lecar, Destexhe-Paré, and an interneuron model. The mechanism seems to be robust for small coupling, and it also persists with random noise affecting the external current. Our findings suggest that weakened neuronal connections could contribute to the production of oscillations with frequencies above 1000 Hz, which could advance our understanding of epilepsy pathology and potentially improve treatment strategies. However, further exploration of various coupling types and complex network models is needed.

We have built a mathematical framework to examine how a reduced neuronal coupling within an epileptic focus could lead to very high-frequency (VHFOs) and ultra-fast oscillations (UFOs) in depth EEG signals. By analyzing weakly coupled neurons, we found a bistability synchronization region where in-phase and anti-phase synchrony persist. These dynamics can be detected as very high-frequency EEG signals. The principle of weak coupling aligns with the disturbances in neuronal connections often observed in epilepsy; moreover, VHFOs are important markers of epileptogenicity. Our findings point to the potential significance of weakened neuronal connections in producing VHFOs and UFOs related to focal epilepsy. This could enhance our understanding of brain disorders. We emphasize the need for further investigations of weakly coupled neurons.

[The research of the contralateral ear hearing thresholds and the hearing prognosis in unilateral Meniere's disease].

Objective:This study analyzed the pure tone audiometry results of the affected ear and the contralateral ear of unilateral Meniere's disease to investigate the correlation of the hearing threshold of the contralateral ear and the hearing prognosis of unilateral Meniere's disease. Methods:In this study, the follow-up data of 135 patients with unilateral Meniere's disease in Beijing Tongren Hospital were used to analyze the pure tone audiometry results of the affected and contralateral ears at the first visit and 1 year later. Results:①At the first visit, there was no statistically significant difference between the mean hearing thresholds of the affected ear in the normal hearing group and the high-frequency hearing loss group of the contralateral ear（P>0.05）. ②The range of improvement of hearing thresholds in the affected ear was greater in the contralateral ear normal hearing group than in the contralateral ear high-frequency hearing loss group. In the normal hearing group of the contralateral ear, the hearing thresholds of the affected ear at 0.25 kHz（P<0.01）, 0.50 kHz（P<0.01）, 1.00 kHz（P<0.01）, and 2.00 kHz（P<0.05） were significantly improved; and in the high-frequency hearing loss group of the contralateral ear, the hearing thresholds at 0.25 kHz（P<0.01） hearing thresholds improved significantly, and there was no significant difference between the rest of the frequencies before and after treatment（P>0.05）. A consistent pattern was observed in both higher and lower age groups. ③After 1 year of follow-up, the low and mid-frequency hearing of the affected ear improved. 0.25 kHz（P<0.01）, 0.50 kHz（P<0.01）, 1.00 kHz（P<0.01） hearing thresholds improved significantly; 8.00 kHz hearing thresholds decreased slightly（P<0.05）. Conclusion:After standardized treatment, the results of 1-year follow-up suggested that the low-frequency hearing of MD patients could be improved, but the high-frequency hearing was slightly decreased. The hearing prognosis of the affected ear with normal hearing threshold of the contralateral ear may be better.

Echo-intensity characterization at implant sites and novel diagnostic ultrasonographic markers for peri-implantitis.

AIM: To apply high-frequency ultrasound (HFUS) echo intensity for characterizing peri-implant tissues at healthy and diseased sites and to investigate the possible ultrasonographic markers of health versus disease. MATERIALS AND METHODS: Sixty patients presenting 60 implants diagnosed as healthy (N = 30) and peri-implantitis (N = 30) were assessed with HFUS. HFUS scans were imported into a software where first-order greyscale outcomes [i.e., mean echo intensity (EI)] and second-order greyscale outcomes were assessed. Other ultrasonographic outcomes of interest involved the vertical extension of the hypoechoic supracrestal area (HSA), soft-tissue area (STA) and buccal bone dehiscence (BBD), among others. RESULTS: HFUS EI mean values obtained from peri-implant soft tissue at healthy and diseased sites were 122.9 ± 19.7 and 107.9 ± 24.7 grey levels (GL); p = .02, respectively. All the diseased sites showed the appearance of an HSA that was not present in healthy implants (area under the curve = 1). The proportion of HSA/STA was 37.9% ± 14.8%. Regression analysis showed that EI of the peri-implant soft tissue was significantly different between healthy and peri-implantitis sites (odds ratio 0.97 [95% confidence interval: 0.94-0.99], p = .019). CONCLUSIONS: HFUS EI characterization of peri-implant tissues shows a significant difference between healthy and diseased sites. HFUS EI and the presence/absence of an HSA may be valid diagnostic ultrasonographic markers to discriminate peri-implant health status.

All for one: Collaboration between dermatologist, radiation oncologist and radiologist in the clinical management of "difficult to treat" non melanoma skin cancer.

This series introduces the clinical management of difficult-to-treat non-melanoma skin cancers (NMSCs) through a multidisciplinary approach, emphasizing the integration of dermoscopy and Ultra high-frequency ultrasound (UHFUS) for accurate diagnosis and treatment planning, particularly in cases referred for radiotherapy (RT). Dermoscopy aids in diagnosing both pigmented and non-pigmented skin lesions, guiding treatment margins and reducing the benign-to-malignant biopsy ratio. UHFUS provides valuable insights into tumor size, depth, and vascularity, complementing clinical evaluations and assisting in RT planning. Three challenging cases are presented, highlighting the pivotal role of dermoscopy and UHFUS in decision-making and treatment optimization. Collaboration between dermatologists, radiation oncologists, and radiologists enhances diagnostic accuracy, tailoring treatment plans to individual patient needs and preferences, ultimately improving patient outcomes and experience. The integration of these imaging techniques holds promise for optimizing non-surgical treatments like RT and monitoring treatment progress, offering a personalized approach to NMSC management.

Transcatheter patent ductus arteriosus closure in premature infants requiring high-frequency ventilation.

Transcatheter closure has become a common treatment method for patent ductus arteriosus in premature infants at many centres; however, many remain uncertain about the ability to perform the procedure in the catheterisation laboratory for infants requiring high-frequency ventilation. This study presents our centre's experience following the implementation of neonatal ventilatory guidelines, which resulted in 100% procedural success without any procedural or respiratory adverse events.

A profile on the WISE cortical strip for intraoperative neurophysiological monitoring.

INTRODUCTION: During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality. AREAS COVERED: We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data. EXPERT OPINION: The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.

Evaluation of High-Frequency Measurement Errors from Turned Surface Topography Data Using Machine Learning Methods.

Manufacturing processes in industry applications are often controlled by the evaluation of surface topography. Topography, in its overall performance, includes form, waviness, and roughness. Methods of measurement of surface roughness can be roughly divided into tactile and contactless techniques. The latter ones are much faster but sensitive to external disturbances from the environment. One type of external source error, while the measurement of surface topography occurs, is a high-frequency noise. This noise originates from the vibration of the measuring system. In this study, the methods for reducing high-frequency errors from the results of contactless roughness measurements of turned surfaces were supported by machine learning methods. This research delves into optimizing filtration methods for surface topography measurements through the application of machine learning models, focusing on enhancing the accuracy of surface roughness assessments. By examining turned surfaces under specific machining conditions and employing a variety of digital filters, the study identifies the Gaussian regression filter and spline filter as the most effective methods at a 22.5 µm cut-off. Utilizing neural networks, support vector machines, and decision trees, the research demonstrates the superior performance of SVMs, achieving remarkable accuracy and sensitivity in predicting optimal filtration methods.

Impact of coronavirus pandemic on stock index: A polynomial regression with time delay.

Motivation: Under contemporary market conditions in China, the stock index has been volatile and highly reflect trends in the coronavirus pandemic, but rare scientific research has been conducted to model the possible nonlinear relations between the two indicators. Added, on the advent that covid-related news in one time period impacts the stock market in another period, time delay can be an equally good predictor of the stock index but rarely investigated. Objectives: To contribute to filling the gaps identified in existing research, this study models relationship between the stock market index and coronavirus pandemic by leveraging volatility in the stock market and covid data through time delay and best degree in a polynomial environment. The resultant optimal time delay and best degree model is used to derive a high-accuracy prediction of stock market index. Novelty: In line with the possible relations, the novelty of this study is that it proposes, validates and implements polynomial regression with time delay to model nonlinear relationship between the stock index and covid. Methods: This study utilizes high-frequency data from January 2020 to the first week of July 2022 to model the nonlinear relationship between the stock index, new covid cases and time delay under polynomial regression environment. Findings: The empirical results show that time delay and new covid cases, when modelled in a polynomial environment with optimal degree and delay, do present better representation of the nonlinear relationship such predictors have with stock index for China. Relative to results from the polynomial regression without delay, the empirical evidence from the model with delay show that an optimal time delay of 17 weeks makes it possible to predict the stock index at high accuracy and record improvements of 16-fold or higher. The representative delay model is used to project for up to 17 weeks for future trends in the stock index. Implication: The implication of the findings herein is that the prowess of the time delay polynomial regression is heavily dependent on instability in covid-related time trends and that researchers and decision-makers should consider modeling to cover for the unsteadiness in coronavirus cases to achieve better results.

Simulated resections and RNS placement can optimize post-operative seizure outcomes when guided by fast ripple networks.

Resecting cortical tissue generating high-frequency oscillations (HFOs) has been investigated as a more efficacious alternative to resecting the clinically defined seizure onset zone (SOZ). In this study, we asked if seizure freedom would be achieved using virtual resections of fast ripple (FR) networks. We compared these virtual resections to the individual patient's actual resection and clinical outcome. We conclude that the SOZ is the minimum territory of cortex that must be resected to achieve seizure freedom. By utilizing support vector machines (SVMs) with an accuracy of 0.78 for labeling seizure freedom using factors from FR networks we could predict whether resection of the SOZ corresponded with a seizure free outcome. Furthermore, this approach could identify regions that generate FR autonomously and at high rates outside the SOZ. In the patients who experienced seizures after resection of the SOZ, virtual resections that included the SOZ and other FR generating regions rendered the patient virtually seizure free. We examined responsive neurostimulator system (RNS) patients and virtually targeted the RNS stimulation contacts proximal to sites generating FR. We used the simulations to investigate if the likelihood of a RNS super responder (>90% seizure reduction) outcome would be increased.

Evaluation of Subjective Tinnitus Severity and Distortion Product Otoacoustic Emissions and Extended High-Frequency Audiometry.

OBJECTIVE: Tinnitus is a multifactorial phenomenon with quality-of-life detriments for those affected by it. We aim to establish a relationship between subjective tinnitus severity with objective audiometric data in the extended high frequency (EHF) from 9 to 16 khz and with distortion product otoacoustic emissions (DPOAE). We hypothesize that severe subjective tinnitus as measured by the Tinnitus Handicap Inventory (THI) does not correlate with increased hearing thresholds in the EHF range. STUDY DESIGN: Prospective. SETTING: Single Tertiary Care Center. METHODS: Patients identified with tinnitus and normal hearing thresholds within standard frequency range (250-8000 Hz) were consented for participation. Those with underlying otologic disease, trauma, radiotherapy, or ototoxic drug use were excluded. The THI questionnaire was given to eligible patients and audiometric test results were collected. THI scores were categorized by severity groups. An n = 20 to 30 was determined to have an effect size of 0.7 with a significance level of P = .05. RESULTS: THI and audiometric data were collected for 38 patients and categorized into mild (n = 18, 47.4%), moderate (n = 8, 21.1%), slight (n = 7, 18.4%), and severe (n = 5, 13.2%) tinnitus severity groups. Mean THI score was 32.3 ± 19.6 with a statistically significant difference in scores by assigned THI severity group (P < .01). There were no significant differences or linear relationship among hearing thresholds in EHF range or DPOAE stratified by subjective tinnitus group (P = .49, r2 = 0.10) CONCLUSION: Subjective tinnitus severity is not predictive of audiometric outcomes. This finding can be used as a counseling tool to help tinnitus patients manage symptoms, expectations, and overall treatment outcomes.

Assessing the functionality of a water-vending kiosk network with high-frequency instrumentation in Freetown, Sierra Leone.

Access to safe, reliable, and equitable water services in urban settings of low- and middle-income countries remains a critical challenge toward achieving Sustainable Development Goal 6.1, but progress has either slowed or stagnated in recent years. A pilot water kiosk network funded by the United States Millennium Challenge Corporation was implemented by the Sierra Leone Millennium Challenge Coordinating Unit into the intermittent piped water distribution network of Freetown, Sierra Leone, as a private-public partnership to improve water service provision for households without reliable piped water connections and to reduce non-revenue water. This study employs the use of high-frequency instrumentation to monitor, model, and assess the functionality of this water kiosk network over 2,947 kiosk-days. Functionality was defined via functionality levels on a daily basis through monitored stored water levels and modeled water withdrawals. The functionality levels across the kiosk network were found to be 34% operational, 30% offline, and 35% empty. Statistically significant (p<0.001) determinants of functionality were found for several predictors across the defined thresholds. Finally, modeling of water supply, water demand and withdrawal capacity, and water storage was conducted to further explain findings and provide additionally externally relevant support for kiosk operations.

Corrigendum: The utility of high-frequency 18 MHz ultrasonography for preoperative evaluation of acral melanoma thickness in Chinese patients.

[This corrects the article DOI: 10.3389/fonc.2023.1185389.].

Spinal Cord Stimulation Waveforms for the Treatment of Chronic Pain.

PURPOSE OF REVIEW: Since the advent of spinal cord stimulation (SCS), advances in technology have allowed for improvement and treatment of various conditions, especially chronic pain. Additionally, as the system has developed, the ability to provide different stimulation waveforms for patients to treat different conditions has improved. The purpose and objective of the paper is to discuss basics of waveforms and present the most up-to-date literature and research studies on the different types of waveforms that currently exist. During our literature search, we came across over sixty articles that discuss the various waveforms we intend to evaluate. RECENT FINDINGS: There are several publications on several waveforms used in clinical practice, but to our knowledge, this is the only educational document teaching on waveforms which provides essential knowledge. There is a gap of knowledge related to understanding wave forms and how they work.

Dual-space high-frequency learning for transformer-based MRI super-resolution.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing works prioritize the design of transformer attention blocks to capture global information. The local high-frequency details, which are pivotal to faithful MRI restoration, are unfortunately neglected. METHODS: In this work, we propose a high-frequency enhanced learning scheme to effectively improve the awareness of high frequency information in current Transformer-based MRI single image super-resolution methods. Specifically, we present two entirely plug-and-play modules designed to equip Transformer-based networks with the ability to recover high-frequency details from dual spaces: 1) in the feature space, we design a high-frequency block (Hi-Fe block) paralleled with Transformer-based attention layers to extract rich high-frequency features; while 2) in the image intensity space, we tailor a high-frequency amplification module (HFA) to further refine the high-frequency details. By fully exploiting the merits of the two modules, our framework can recover abundant and diverse high-frequency information, rendering faithful MRI super-resolved results with fine details. RESULTS: We integrated our modules with six Transformer-based models and conducted experiments across three datasets. The results indicate that our plug-and-play modules can enhance the super-resolution performance of all foundational models to varying degrees, surpassing the capabilities of existing state-of-the-art single image super-resolution networks. CONCLUSION: Comprehensive comparison of super-resolution images and high-frequency maps from various methods, clearly demonstrating that our module possesses the capability to restore high-frequency information, showing huge potential in clinical practice for accelerated MRI reconstruction.

Imagined speech classification exploiting EEG power spectrum features.

Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.

High-frequency optical coherence tomography for endovascular management of cerebral aneurysms.

Endovascular management of intracranial aneurysms has become the mainstay of treatment in recent years; however, retreatment rates remain as high as 1 in 5. High-frequency optical coherence tomography (HF-OCT) is an emerging imaging modality for the assessment, treatment and follow-up of cerebral aneurysms. EMBASE and SCOPUS databases were searched for studies relating to the management of intracranial aneurysm with OCT. A combination of keywords were used including 'cerebral aneurysm', 'intracranial aneurysm', 'high-frequency optical coherence tomography', 'optical coherence tomography', and 'optical frequency domain imaging'. There were 23 papers included in this review. For the assessment of intracranial aneurysm, OCT was able to accurately assess aneurysm morphology as well as detailed analysis of arterial wall layers. During IA treatment, OCT was used to assess and troubleshoot stent placement to optimise successful isolation from the circulation. In the follow-up period, endothelial growth patterns were visualised by OCT imaging. OCT shows promise for the treatment of IAs at all stages of management. Due to the novel development of HF-OCT, there is limited longitudinal data in human studies. Further research in this area is required with a focus specifically on long-term treatment outcomes in humans.

Effect of high-frequency repetitive transcranial magnetic stimulation on cognitive impairment in WD patients based on inverse probability weighting of propensity scores.

Background: Hepatolenticular degeneration [Wilson disease (WD)] is an autosomal recessive metabolic disease characterized by copper metabolism disorder. Cognitive impairment is a key neuropsychiatric symptom of WD. At present, there is no effective treatment for WD-related cognitive impairment. Methods: In this study, high-frequency repetitive transcranial magnetic stimulation (rTMS) was used to treat WD-related cognitive impairment, and inverse probability weighting of propensity scores was used to correct for confounding factors. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Boston Naming Test (BNT), Clock Drawing Test (CDT) and Trail Making Test (TMT) were used to evaluate overall cognition and specific cognitive domains. Results: The MMSE, MoCA and CDT scores after treatment were significantly different from those before treatment (MMSE: before adjustment: OR = 1.404, 95% CI: 1.271-1.537; after adjustment: OR = 1.381, 95% CI: 1.265-1.497, p < 0.001; MoCA: before adjustment: OR = 1.306, 95% CI: 1.122-1.490; after adjustment: OR = 1.286, 95% CI: 1.104; AVLT: OR = 1.161, 95% CI: 1.074-1.248; after adjustment: OR = 1.145, 95% CI: 1.068-1.222, p < 0.05; CDT: OR = 1.524, 95% CI: 1.303-1.745; after adjustment: OR = 1.518, 95% CI: 1.294-1.742, p < 0.001). The BNT and TMT scores after adjustment were not significantly different from those before adjustment (BNT: before adjustment: OR = 1.048, 95% CI: 0.877-1.219; after adjustment: OR = 1.026, 95% CI: 0.863-1.189, p > 0.05; TMT: before adjustment: OR = 0.816, 95% CI: 1.122-1.490; after adjustment: OR = 0.791, 95% CI: 0.406-1.176, p > 0.05). Conclusion: High-frequency rTMS can effectively improve cognitive impairment, especially memory and visuospatial ability, in WD patients. The incidence of side effects is low, and the safety is good.

Lung behavior during a staircase high-frequency oscillatory ventilation recruitment maneuver.

BACKGROUND: Lung volume optimization maneuvers (LVOM) are necessary to make physiologic use of high-frequency oscillatory ventilation (HFOV), but lung behavior during such maneuvers has not been studied to determine lung volume changes after initiation of HFOV, to quantify recruitment versus derecruitment during the LVOM and to calculate the time to stabilization after a pressure change. METHODS: We performed a secondary analysis of prospectively collected data in subjects < 18 years on HFOV. Uncalibrated respiratory inductance plethysmography (RIP) tracings were used to quantify lung recruitment and derecruitment during the LVOM inflation and deflation. The time constant was calculated according to the Niemann model. RESULTS: RIP data of 51 subjects (median age 3.5 [1.7-13.3] months) with moderate-to-severe pediatric acute respiratory distress syndrome (PARDS) in 85.4% were analyzed. Lung recruitment and derecruitment occurred during the LVOM inflation phase upon start of HFOV and between and within pressure changes. At 90% of maximum inflation pressure, lung derecruitment already started during the deflation phase. Time to stable lung volume (time constant) could only be calculated in 26.2% of all pressure changes during the inflation and in 21.4% during the deflation phase, independent of continuous distending pressure (CDP). Inability to calculate the time constant was due to lack of stabilization of the RIP signal or no change in any direction. CONCLUSIONS: Significant heterogeneity in lung behavior during a staircase incremental-decremental LVOM occurred, underscoring the need for higher initial inflation pressures when transitioning from conventional mechanical ventilation (CMV) and a longer time between pressure changes to allow for equilibration.

Hearing in categories aids speech streaming at the "cocktail party".

Our perceptual system bins elements of the speech signal into categories to make speech perception manageable. Here, we aimed to test whether hearing speech in categories (as opposed to a continuous/gradient fashion) affords yet another benefit to speech recognition: parsing noisy speech at the "cocktail party." We measured speech recognition in a simulated 3D cocktail party environment. We manipulated task difficulty by varying the number of additional maskers presented at other spatial locations in the horizontal soundfield (1-4 talkers) and via forward vs. time-reversed maskers, promoting more and less informational masking (IM), respectively. In separate tasks, we measured isolated phoneme categorization using two-alternative forced choice (2AFC) and visual analog scaling (VAS) tasks designed to promote more/less categorical hearing and thus test putative links between categorization and real-world speech-in-noise skills. We first show that listeners can only monitor up to ~3 talkers despite up to 5 in the soundscape and streaming is not related to extended high-frequency hearing thresholds (though QuickSIN scores are). We then confirm speech streaming accuracy and speed decline with additional competing talkers and amidst forward compared to reverse maskers with added IM. Dividing listeners into "discrete" vs. "continuous" categorizers based on their VAS labeling (i.e., whether responses were binary or continuous judgments), we then show the degree of IM experienced at the cocktail party is predicted by their degree of categoricity in phoneme labeling; more discrete listeners are less susceptible to IM than their gradient responding peers. Our results establish a link between speech categorization skills and cocktail party processing, with a categorical (rather than gradient) listening strategy benefiting degraded speech perception. These findings imply figure-ground deficits common in many disorders might arise through a surprisingly simple mechanism: a failure to properly bin sounds into categories.