Abstract Sensory systems must represent a vast range of stimulus dimensions and energy whilst subject to metabolic constraints. Efficient-coding theory predicts that neural adaptation re-allocates a relatively limited range of neural activity toward the most informative stimulus values, but it is unclear how subtle peripheral lesions shift this operating point in central circuits. Hearing is a stringent test because sound level varies enormously across environments, yet clinical assessment still relies heavily on tone-detection thresholds that can miss listening deficits in noise. We analyzed extracellular recordings from single neurons in the gerbil auditory midbrain across 14 animals in four experimental groups exposed to unfolding distributions of sound intensities drawn either uniformly from a wide range (24-96 decibels) of sound pressure levels or from contexts in which 80% of levels were restricted to a 12-decibel high-probability range. For each context we summarized each neuron’s rate-intensity input-output function by an effective threshold and gain, and we interpreted the resulting threshold-gain distributions with an information-cost model that trades bits of stimulus information against a penalty on mean spiking. Noise exposure consistent with loss of synapses between inner-ear cells and auditory nerve fibers altered gain modulation across acoustic contexts, with noise-exposed animals showing compressed gain adjustments relative to controls; within the information-cost framework, the clearest hidden-hearing-loss effect was a quiet-context utility advantage distributed across low- and intermediate-threshold neurons, whereas moderate-to-loud contexts showed weaker or absent group differences. Temporary conductive attenuation caused by ear-canal plugging shifted effective thresholds to higher sound levels, with incomplete recovery after plug removal; the corresponding optimization-prior trajectories were consistent with incomplete rapid renormalization but were weaker than the hidden-hearing-loss effect. These results support an efficient-coding interpretation of altered central auditory representations after subtle lesions and provide a quantitative, context-based framework for comparing mechanisms of hearing difficulty beyond threshold-only tests and Fisher information alone. Author Summary Everyday hearing is an ecological challenge for the auditory system: we must follow speech while background sounds fluctuate and overlap. Standard tests emphasize tone-detection thresholds, but many listeners struggle in noise even when thresholds appear normal. We asked whether subtle peripheral changes shift how the auditory brain trades information for neural effort. We analyzed recordings from single neurons in the gerbil auditory midbrain during sound environments with different loudness statistics, including ones dominated by a narrow intensity range. Using information-theoretic measures, we quantified how much spikes distinguished sound-level categories and related this to the amount of spiking produced. Noise exposure consistent with inner-ear synaptic loss altered gain modulation across acoustic contexts and most strongly improved model-based coding utility in quieter settings, but reduced adaptation and efficiency as sound environments became louder. Temporary ear-canal plugging raised effective response thresholds substantially above both control and synaptopathy groups, with only partial recovery immediately after plug removal. By mapping both manipulations onto a common information-versus-cost scale, we highlight context-dependent metrics that may prove more informative than threshold audiograms for subtle hearing problems.

Background: In Kreuz et al., J Neurosci Methods 381, 109703 (2022) two methods were proposed that perform latency correction, i.e., optimize the spike time alignment of sparse neuronal spike trains with well defined global spiking events. The first one based on direct shifts is fast but uses only partial latency information, while the other one makes use of the full information but relies on the computationally costly simulated annealing. Both methods reach their limits and can become unreliable when successive global events are not sufficiently separated or even overlap. New Method: Here we propose an iterative scheme that combines the advantages of the two original methods by using in each step as much of the latency information as possible and by employing a very fast extrapolation direct shift method instead of the much slower simulated annealing. Results: We illustrate the effectiveness and the improved performance, measured in terms of the relative shift error, of the new iterative scheme not only on simulated data with known ground truths but also on single-unit recordings from two medial superior olive neurons of a gerbil. Comparison with Existing Method(s): The iterative scheme outperforms the existing approaches on both the simulated and the experimental data. Due to its low computational demands, and in contrast to simulated annealing, it can also be applied to very large datasets. Conclusions: The new method generalizes and improves on the original method both in terms of accuracy and speed. Importantly, it is the only method that allows to disentangle global events with overlap.

Abstract Navigating complex sensory environments is critical to survival, and brain mechanisms have evolved to cope with the wide range of surroundings we encounter. To determine how listeners learn the statistical properties of acoustic spaces, we assessed their ability to perceive speech in a range of noisy and reverberant rooms. Listeners were also exposed to repetitive transcranial stimulation (rTMS) to disrupt the dorsolateral prefrontal cortex (dlPFC) activity, a region believed to play a role in statistical learning. Our data suggest listeners rapidly adapt to statistical characteristics of an environment to improve speech understanding. This ability is impaired when rTMS is applied bilaterally to the dlPFC. The data demonstrate that speech understanding in noise is best when exposed to a room with reverberant characteristics common to human-built environments, with performance declining for higher and lower reverberation times, including fully anechoic (non-reverberant) environments. Our findings provide evidence for a reverberation “sweet spot” and the presence of brain mechanisms that might have evolved to cope with the acoustic characteristics of listening environments encountered every day.

Measures of autistic traits are only useful - for pre-diagnostic screening, exploring individual differences, and gaining personal insight - if they efficiently and accurately assess autism as currently conceptualised while maintaining psychometric validity across different demographic groups. We recruited 1322 autistic and 1279 non-autistic adults who varied in autism status (non-autistic, diagnosed autistic, self-identifying autistic) and gender (cisgender men, cisgender women, gender diverse) to assess the psychometric properties of the Comprehensive Autistic Trait Inventory, a recently developed measure of autistic traits that examines six trait domains using 42 self-report statements. Factor fit for the six subscales was appropriate, as was total-scale and subscale reliability. Importantly, measurement invariance was demonstrated based on both autism status and gender, indicating that Comprehensive Autistic Trait Inventory scores of these group members can be directly compared. Autistic traits were highly similar between diagnosed and self-identifying autistic adults, while gender-diverse participants showed more autistic traits than their cisgender counterparts. A total-scale discrimination threshold of 147.5 calculated was suggested which corresponded to sensitivity and specificity of 77.20 and 87.41, respectively. Our analysis indicates that the Comprehensive Autistic Trait Inventory is a practical measure of autistic traits in non-autistic and autistic participants that is useful for researchers and clinicians and for affirming self-identity.Lay abstractThe Comprehensive Autistic Trait Inventory (CATI) is a free questionnaire designed to measure autistic traits in both autistic and non-autistic adults. The CATI includes 42 items focusing on six areas: Social Interactions, Communication, Social Camouflage, Self-Regulating Behaviours, Cognitive Flexibility, and Sensory Sensitivity. Here, we set out to determine whether the CATI can accurately measure autistic traits in both autistic (both diagnosed and self-identifying) and non-autistic people, as well as people of different genders. We also wanted to explore the extent to which trait scores differed between these groups of individuals. Our study recruited over 2600 participants, including 1322 autistic and 1279 non-autistic adults. Our findings suggest that the CATI works the way it was designed to. It is a reliable and accurate tool for measuring autistic traits, can distinguish between autistic and non-autistic people, and appears appropriate for people of different genders. Notably, we found that people who self-identify as autistic have similar trait scores to those with a clinical diagnosis of autism and that gender-diverse people scored higher on autistic traits compared to cisgender people. Our data suggest that the CATI is a useful tool for measuring autistic traits in autistic and non-autistic people and for understanding the way that autistic people vary from one another. It should be helpful for researchers and clinicians, and support a public understanding of autism.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular neuroimaging technique that measures cortical hemodynamic activity in a non-invasive and portable fashion. Although the fNIRS community has been successful in disseminating open-source processing tools and a standard file format (SNIRF), reproducible research and sharing of fNIRS data amongst researchers has been hindered by a lack of standards and clarity over how study data should be organized and stored. This problem is not new in neuroimaging, and it became evident years ago with the proliferation of publicly available neuroimaging datasets. To solve this critical issue, the neuroimaging community created the Brain Imaging Data Structure (BIDS) that specifies standards for how datasets should be organized to facilitate sharing and reproducibility of science. Currently, BIDS supports dozens of neuroimaging modalities including MRI, EEG, MEG, PET, and many others. In this paper, we present the extension of BIDS for NIRS data alongside tools that may assist researchers in organizing existing and new data with the goal of promoting public disseminations of fNIRS datasets.

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.

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.

Interaural time differences are often considered a weak cue for stream segregation. We investigated this claim with headphone-presented pure tones differing in a related form of interaural configuration—interaural phase differences (ΔIPD)—or/and in frequency (ΔF). In experiment 1, sequences comprised 5 × ABA– repetitions (A and B = 80-ms tones, “–” = 160-ms silence), and listeners reported whether integration or segregation was heard. Envelope shape was varied but remained constant across all tones within a trial. Envelopes were either quasi-trapezoidal or had a fast attack and slow release (FA-SR) or vice versa (SA-FR). The FA-SR envelope caused more segregation than SA-FR in a task where only ΔIPD cues were present, but not in a corresponding ΔF-only task. In experiment 2, interstimulus interval (ISI) was varied (0–60 ms) between FA-SR tones. ΔF-based segregation decreased with increasing ISI, whereas ΔIPD-based segregation increased. This suggests that binaural temporal integration may limit segregation at short ISIs. In another task, ΔF and ΔIPD cues were presented alone or in combination. Here, ΔIPD-based segregation was greatly reduced, suggesting ΔIPD-based segregation is highly sensitive to experimental context. Experiments 1–2 demonstrate that ΔIPD can promote segregation in optimized stimuli/tasks. Experiment 3 employed a task requiring integration for good performance. Listeners detected a delay on the final four B tones of an 8 × ABA– sequence. Although performance worsened with increasing ΔF, increasing ΔIPD had only a marginal impact. This suggests that, even in stimuli optimized for ΔIPD-based segregation, listeners remained mostly able to disregard ΔIPD when segregation was detrimental to performance.

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs.

Interpreting the world around us requires integrating sensory information across modalities to derive meaning and shape our experiences and interactions with and within everyday environments. Recent interest in sensory-focused approaches to supporting autistic people has fixed on auditory processing—the sense of hearing and the act of listening—and its crucial role in language, communications, and social domains, as well as non-social autism- specific attributes, to understand better how sensory processing might differ in autistic people. In this narrative review, we synthesize published research into auditory processing in autistic people and the relationship between auditory processing and autistic attributes in a contextually novel way. The purpose is to understand the relationship between these domains more fully, drawing on evidence gleaned from experiential perspectives through neurological investigations. We also examine the relationship between auditory processing and diagnosable auditory conditions, such as hyperacusis, misophonia, phonophobia, and intolerance to loud sounds, as well as its relation to sleep, anxiety, and sensory overload. Our review reveals that autistic people often exhibit differences in their auditory profile early in life and that this intrinsically impacts their neurological development and shapes their broader autistic profile. Through a better understanding of the potential impact of auditory experiences on daily life, our review aims to inform future research on how to support the auditory profile of autistic people through accessible environments and improved strategies, technologies, and strategies for supporting autistic people in everyday life.

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.

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.

What is noise? When does a sound form part of the acoustic background and when might it come to our attention as part of the foreground? Our brain seems to filter out irrelevant sounds in a seemingly effortless process, but how this is achieved remains opaque and, to date, unparalleled by any algorithm. In this review, we discuss how noise can be both background and foreground, depending on what a listener/brain is trying to achieve. We do so by addressing questions concerning the brain’s potential bias to interpret certain sounds as part of the background, the extent to which the interpretation of sounds depends on the context in which they are heard, as well as their ethological relevance, task-dependence, and a listener’s overall mental state. We explore these questions with specific regard to the implicit, or statistical, learning of sounds and the role of feedback loops between cortical and subcortical auditory structures.

Perceptual anchoring, a process akin to statistical learning, occurs rapidly and without conscious awareness and is integral to our ability to successfully navigate a noisy world. Here, we investigated anchoring abilities in typical hearing and reading participants by implementing an anchoring paradigm (Agus et al., 2014) using rapid pure-tone sequences (Barascud etal., 2016). We then attempted to disrupt anchoring by applying rapid transcranial magnetic stimulation (rTMS) to frontal cortical regions—areas implicated in the processing of and integration of tone sequences (Abla and Okanoya, 2008). Pure-tone sequences consisted of 50 ms tone-pips that were arranged according to two segments, random (RAND) and regular (REG). RAND segments were generated as tones of random frequencies, and REG segments were generated as in RAND but were iterated to create a repeating pattern. Sequences were presented across three conditions: REPfixed (identical sequences repeated across trials), REPnovel (identical sequences not repeated across trials), and NonREPnovel (nonidentical sequences not repeated across trials). We observed a significantly higher sensitivity to REPfixed relative to REPnovel across all participants (i.e., an anchoring effect). The disruption of frontal regions using rTMS did not significantly impact overall performance but did alter how participants completed the task over time.

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.

In natural listening environments, humans hear multiple concurrent sounds arriving from different locations. In order to attend to a single source, concurrent sounds must be separated into individual sound objects, a process that relies on binaural hearing. However, the fluctuating binaural cues that arise when sounds from multiple sources merge at each ear generate fluctuating binaural cues that may be unreliable as to source location. This reliability can be quantified in terms of the interaural coherence (IAC). To overcome unreliable binaural cues, it is suggested that the brain monitors IAC and extracts spatial information from sound energy during epochs where IAC is high. To test this hypothesis, we designed a stimulus allowing us to modulate IAC over time and frequency. Our data indicate that IAC weighting plays a relatively minor role in source separation. Rather, binaural cues only contribute to source localization during rising sound energy. Binaural cues in later epochs of a modulated sound waveform are completely ignored even when IAC is high and spans a wide frequency range. The data do not support weighting based on IAC but, rather, suggest a fast, “non-sluggish” processing of binaural cues to extract information only during the rising energy envelope.

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.

Amniotes evolved a unique calyceal postsynaptic terminal in the vestibular organs of the inner ear that underpins quantal and non-quantal transmission at the synapse of sensory hair cells and vestibular afferent neurons. The non-quantal component is of particular interest as it includes an ultrafast synaptic current thought to underlie the exquisite synchronization of action potentials in vestibular afferent fibres to dynamic stimuli such as sound and vibration. Here we demonstrate evidence that non-quantal transmission is responsible for short latency vestibular evoked potentials (vCAPs) in the guinea pig utricle. We first show that, unlike auditory nerve responses which are completely abolished, vCAPs are insensitive to local administration of the AMPA receptor agonist CNQX. Moreover, latency comparisons between presynaptic hair cell and postsynaptic neural responses reveal that the vCAP occurs without measurable synaptic delay. Finally, using a paired-pulse stimulus designed to deplete the readily releasable pool of synaptic vesicles in hair cells, we reveal that forward masking is lacking in vestibular responses, compared to the equivalent cochlear responses. Our data support the hypothesis that the fast component of non-quantal transmission at calyceal synapses is indefatigable and responsible for ultrafast responses of vestibular organs evoked by transient stimulation. Significance 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 pre-synaptic vestibular hair cells, which transmits mechanosensory signals mediated by quantal and nonquantal (NQ) synaptic transmission. We present several lines of data in the guinea pig that reveal the pre-synaptic transmission of the most sensitive vestibular afferents are faster than their auditory nerve counterparts. Here, we present neurophysiological and pharmacological evidence that this vestibular speed advantage arises from ultrafast NQ electrical synaptic transmission from Type I hair cells to their calyx partners.