Intensity-invariant coding in the auditory system

Reliability and stability of tactile perception in the whisker somatosensory system

Rodents rely on their whiskers as vital sensory tools for tactile perception, enabling them to distinguish textures and shapes. Ensuring the reliability and constancy of tactile perception under varying stimulus conditions remains a fascinating and fundamental inquiry. This study explores the impact of stimulus configurations, including whisker movement velocity and object spatial proximity, on texture discrimination and stability in rats. To address this issue, we employed three distinct approaches for our investigation. Stimulus configurations notably affected tactile inputs, altering whisker vibration's kinetic and kinematic aspects with consistent effects across various textures. Through a texture discrimination task, rats exhibited consistent discrimination performance irrespective of changes in stimulus configuration. However, alterations in stimulus configuration significantly affected the rats' ability to maintain stability in texture perception. Additionally, we investigated the influence of stimulus configurations on cortical neuronal responses by manipulating them experimentally. Notably, cortical neurons demonstrated substantial and intricate changes in firing rates without compromising the ability to discriminate between textures. Nevertheless, these changes resulted in a reduction in texture neuronal response stability. Stimulating multiple whiskers led to improved neuronal texture discrimination and maintained coding stability. These findings emphasize the importance of considering numerous factors and their interactions when studying the impact of stimulus configuration on neuronal responses and behavior.

Segregation and integration of sensory features by flexible temporal characteristics of independent neural representations

Segregation and integration are two fundamental yet competing computations in cognition. For example, in serial speech processing, stable perception necessitates the sequential establishment of perceptual representations to remove irrelevant features for achieving invariance. Whereas multiple features need to combine to create a coherent percept. How to simultaneously achieve seemingly contradicted computations of segregation and integration in a serial process is unclear. To investigate their neural mechanisms, we used loudness and lexical tones as a research model and employed a novel multilevel oddball paradigm with Electroencephalogram (EEG) recordings to explore the dynamics of mismatch negativity (MMN) responses to their deviants. When two types of deviants were presented separately, distinct topographies of MMNs to loudness and tones were observed at different latencies (loudness earlier), supporting the sequential dynamics of independent representations for two features. When they changed simultaneously, the latency of responses to tones became shorter and aligned with that to loudness, while the topographies remained independent, yielding the combined MMN as a linear additive of single MMNs of loudness and tones. These results suggest that neural dynamics can be temporally synchronized to distinct sensory features and balance the computational demands of segregation and integration, grounding for invariance and feature binding in serial processing.

Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss

Listening in noisy or complex sound environments is difficult for individuals with normal hearing and can be a debilitating impairment for those with hearing loss. Extracting meaningful information from a complex acoustic environment requires the ability to accurately encode specific sound features under highly variable listening conditions and segregate distinct sound streams from multiple overlapping sources. The auditory system employs a variety of mechanisms to achieve this auditory scene analysis. First, neurons across levels of the auditory system exhibit compensatory adaptations to their gain and dynamic range in response to prevailing sound stimulus statistics in the environment. These adaptations allow for robust representations of sound features that are to a large degree invariant to the level of background noise. Second, listeners can selectively attend to a desired sound target in an environment with multiple sound sources. This selective auditory attention is another form of sensory gain control, enhancing the representation of an attended sound source while suppressing responses to unattended sounds. This review will examine both “bottom-up” gain alterations in response to changes in environmental sound statistics as well as “top-down” mechanisms that allow for selective extraction of specific sound features in a complex auditory scene. Finally, we will discuss how hearing loss interacts with these gain control mechanisms, and the adaptive and/or maladaptive perceptual consequences of this plasticity.

Neural Synchrony Gives Rise to Amplitude- and Duration-Invariant Encoding Consistent With Perception of Natural Communication Stimuli

When confronted with a highly variable environment, it remains poorly understood how neural populations encode and classify natural stimuli to give rise to appropriate and consistent behavioral responses. Here we investigated population coding of natural communication signals with different attributes (i.e., amplitude and duration) in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. Our results show that, while single peripheral neurons encode the detailed timecourse of different stimulus waveforms, measures of population synchrony are effectively unchanged because of coordinated increases and decreases in activity. A phenomenological mathematical model reproduced this invariance and shows that this can be explained by considering homogeneous populations whose responses are solely determined by single neuron firing properties. Moreover, recordings from downstream central neurons reveal that synchronous afferent activity is actually decoded and thus most likely transmitted to higher brain areas. Finally, we demonstrate that the associated behavioral responses at the organism level are invariant. Our results provide a mechanism by which amplitude- and duration-invariant coding of behaviorally relevant sensory input emerges across successive brain areas thereby presumably giving rise to invariant behavioral responses. Such mechanisms are likely to be found in other systems that share anatomical and functional features with the electrosensory system (e.g., auditory, visual, vestibular).

Robust and Intensity-Dependent Synaptic Inhibition Underlies the Generation of Non-monotonic Neurons in the Mouse Inferior Colliculus

Intensity and frequency are the two main properties of sound. The non-monotonic neurons in the auditory system are thought to represent sound intensity. The central nucleus of the inferior colliculus (ICC), as an important information integration nucleus of the auditory system, is also involved in the processing of intensity encoding. Although previous researchers have hinted at the importance of inhibitory effects on the formation of non-monotonic neurons, the specific underlying synaptic mechanisms in the ICC are still unclear. Therefore, we applied the in vivo whole-cell voltage-clamp technique to record the excitatory and inhibitory postsynaptic currents (EPSCs and IPSCs) in the ICC neurons, and compared the effects of excitation and inhibition on the membrane potential outputs. We found that non-monotonic neuron responses could not only be inherited from the lower nucleus but also be created in the ICC. By integrating with a relatively weak IPSC, approximately 35% of the monotonic excitatory inputs remained in the ICC. In the remaining cases, monotonic excitatory inputs were reshaped into non-monotonic outputs by the dominating inhibition at high intensity, which also enhanced the non-monotonic nature of the non-monotonic excitatory inputs.

Hidden hearing loss selectively impairs neural adaptation to loud sound environments

Exposure to even a single episode of loud noise can damage synapses between cochlear hair cells and auditory nerve fibres, causing hidden hearing loss (HHL) that is not detected by audiometry. Here we investigate the effects of noise-induced HHL on functional hearing by measuring the ability of neurons in the auditory midbrain of mice to adapt to sound environments containing quiet and loud periods. Neurons from noise-exposed mice show less capacity for adaptation to loud environments, convey less information about sound intensity in those environments, and adaptation to the longer-term statistical structure of fluctuating sound environments is impaired. Adaptation comprises a cascade of both threshold and gain adaptation. Although noise exposure only impairs threshold adaptation directly, the preserved function of gain adaptation surprisingly aggravates coding deficits for loud environments. These deficits might help to understand why many individuals with seemingly normal hearing struggle to follow a conversation in background noise.

Hidden hearing loss (HHL) arises through subtle damage to the synapses of hair cells in the inner ear before audiograms reveal hearing threshold shifts. Here, the authors report that HHL in a mouse model disrupts the neural encoding of loud sound environments in the central auditory system.

Decoding sound level in the marmoset primary auditory cortex

Neurons that respond favorably to a particular sound level have been observed throughout the central auditory system, becoming steadily more common at higher processing areas. One theory about the role of these level-tuned or nonmonotonic neurons is the level-invariant encoding of sounds. To investigate this theory, we simulated various subpopulations of neurons by drawing from real primary auditory cortex (A1) neuron responses and surveyed their performance in forming different sound level representations. Pure nonmonotonic subpopulations did not provide the best level-invariant decoding; instead, mixtures of monotonic and nonmonotonic neurons provided the most accurate decoding. For level-fidelity decoding, the inclusion of nonmonotonic neurons slightly improved or did not change decoding accuracy until they constituted a high proportion. These results indicate that nonmonotonic neurons fill an encoding role complementary to, rather than alternate to, monotonic neurons.NEW & NOTEWORTHY Neurons with nonmonotonic rate-level functions are unique to the central auditory system. These level-tuned neurons have been proposed to account for invariant sound perception across sound levels. Through systematic simulations based on real neuron responses, this study shows that neuron populations perform sound encoding optimally when containing both monotonic and nonmonotonic neurons. The results indicate that instead of working independently, nonmonotonic neurons complement the function of monotonic neurons in different sound-encoding contexts.

Selective Neuronal Activation by Cochlear Implant Stimulation in Auditory Cortex of Awake Primate

Despite the success of cochlear implants (CIs) in human populations, most users perform poorly in noisy environments and music and tonal language perception. How CI devices engage the brain at the single neuron level has remained largely unknown, in particular in the primate brain. By comparing neuronal responses with acoustic and CI stimulation in marmoset monkeys unilaterally implanted with a CI electrode array, we discovered that CI stimulation was surprisingly ineffective at activating many neurons in auditory cortex, particularly in the hemisphere ipsilateral to the CI. Further analyses revealed that the CI-nonresponsive neurons were narrowly tuned to frequency and sound level when probed with acoustic stimuli; such neurons likely play a role in perceptual behaviors requiring fine frequency and level discrimination, tasks that CI users find especially challenging. These findings suggest potential deficits in central auditory processing of CI stimulation and provide important insights into factors responsible for poor CI user performance in a wide range of perceptual tasks.

SIGNIFICANCE STATEMENT

The cochlear implant (CI) is the most successful neural prosthetic device to date and has restored hearing in hundreds of thousands of deaf individuals worldwide. However, despite its huge successes, CI users still face many perceptual limitations, and the brain mechanisms involved in hearing through CI devices remain poorly understood. By directly comparing single-neuron responses to acoustic and CI stimulation in auditory cortex of awake marmoset monkeys, we discovered that neurons unresponsive to CI stimulation were sharply tuned to frequency and sound level. Our results point out a major deficit in central auditory processing of CI stimulation and provide important insights into mechanisms underlying the poor CI user performance in a wide range of perceptual tasks.

Compensating Level-Dependent Frequency Representation in Auditory Cortex by Synaptic Integration of Corticocortical Input

Robust perception of auditory objects over a large range of sound intensities is a fundamental feature of the auditory system. However, firing characteristics of single neurons across the entire auditory system, like the frequency tuning, can change significantly with stimulus intensity. Physiological correlates of level-constancy of auditory representations hence should be manifested on the level of larger neuronal assemblies or population patterns. In this study we have investigated how information of frequency and sound level is integrated on the circuit-level in the primary auditory cortex (AI) of the Mongolian gerbil. We used a combination of pharmacological silencing of corticocortically relayed activity and laminar current source density (CSD) analysis. Our data demonstrate that with increasing stimulus intensities progressively lower frequencies lead to the maximal impulse response within cortical input layers at a given cortical site inherited from thalamocortical synaptic inputs. We further identified a temporally precise intercolumnar synaptic convergence of early thalamocortical and horizontal corticocortical inputs. Later tone-evoked activity in upper layers showed a preservation of broad tonotopic tuning across sound levels without shifts towards lower frequencies. Synaptic integration within corticocortical circuits may hence contribute to a level-robust representation of auditory information on a neuronal population level in the auditory cortex.

Neural correlations enable invariant coding and perception of natural stimuli in weakly electric fish

Neural representations of behaviorally relevant stimulus features displaying invariance with respect to different contexts are essential for perception. However, the mechanisms mediating their emergence and subsequent refinement remain poorly understood in general. Here, we demonstrate that correlated neural activity allows for the emergence of an invariant representation of natural communication stimuli that is further refined across successive stages of processing in the weakly electric fish Apteronotus leptorhynchus. Importantly, different patterns of input resulting from the same natural communication stimulus occurring in different contexts all gave rise to similar behavioral responses. Our results thus reveal how a generic neural circuit performs an elegant computation that mediates the emergence and refinement of an invariant neural representation of natural stimuli that most likely constitutes a neural correlate of perception.

DOI:http://dx.doi.org/10.7554/eLife.12993.001

We can effortlessly recognize an object – a car, for example – in many different contexts such as when seen from behind, under different lighting levels or even from different viewpoints. This phenomenon is known as perceptual invariance: objects are correctly recognized, despite variations in exactly what is seen (or otherwise sensed). However, it is still not clear how the brain processes perceptual information to recognize the same object under a wide variety of contexts.

Some fish, such as the brown ghost knifefish, produce a weak electric signal that they can alter to communicate with other members of their species. A communication call may be produced in a variety of contexts that alter which aspects of the signal nearby fish detect. Despite this, fish tend to respond to a given communication call in the same way regardless of its context; this suggests that these fish also have perceptual invariance.

The communication calls of weakly electric fish can be easily mimicked in a laboratory and produce reliable behavioral responses, which makes these fish a good model for understanding how perceptual invariance might be coded in the brain. Therefore, Metzen et al. recorded the activity of the receptor neurons that first respond to communication calls in weakly electric fish. The results revealed that a given communication signal made the firing patterns of all receptor neurons in the fish’s brain more similar to each other, regardless of the signal’s context. This occurs despite the changes in context causing single receptor neurons to respond in different ways. At each stage of the process by which information is transmitted from the receptor neurons to neurons deeper in the brain, the similarity in the neurons’ firing patterns is refined, thereby giving rise to perceptual invariance.

While perceptual invariance to a given object in different contexts is desirable, it is also important to be able to distinguish between different objects. This implies that neurons should respond similarly to stimuli associated with the same object and differently to stimuli associated with different objects. Further studies are now needed to confirm whether this is the case.

DOI:http://dx.doi.org/10.7554/eLife.12993.002

Resource allocation models of auditory working memory

Auditory working memory (WM) is the cognitive faculty that allows us to actively hold and manipulate sounds in mind over short periods of time. We develop here a particular perspective on WM for non-verbal, auditory objects as well as for time based on the consideration of possible parallels to visual WM. In vision, there has been a vigorous debate on whether WM capacity is limited to a fixed number of items or whether it represents a limited resource that can be allocated flexibly across items. Resource allocation models predict that the precision with which an item is represented decreases as a function of total number of items maintained in WM because a limited resource is shared among stored objects. We consider here auditory work on sequentially presented objects of different pitch as well as time intervals from the perspective of dynamic resource allocation. We consider whether the working memory resource might be determined by perceptual features such as pitch or timbre, or bound objects comprising multiple features, and we speculate on brain substrates for these behavioural models. This article is part of a Special Issue entitled SI: Auditory working memory.

Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli

Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

A model for non-monotonic intensity coding

Peripheral neurons of most sensory systems increase their response with increasing stimulus intensity. Behavioural responses, however, can be specific to some intermediate intensity level whose particular value might be innate or associatively learned. Learning such a preference requires an adjustable trans- formation from a monotonic stimulus representation at the sensory periphery to a non-monotonic representation for the motor command. How do neural systems accomplish this task? We tackle this general question focusing on odour-intensity learning in the fruit fly, whose first- and second-order olfactory neurons show monotonic stimulus-response curves. Nevertheless, flies form associative memories specific to particular trained odour intensities. Thus, downstream of the first two olfactory processing layers, odour intensity must be re-coded to enable intensity-specific associative learning. We present a minimal, feed-forward, three-layer circuit, which implements the required transformation by combining excitation, inhibition, and, as a decisive third element, homeostatic plasticity. Key features of this circuit motif are consistent with the known architecture and physiology of the fly olfactory system, whereas alternative mechanisms are either not composed of simple, scalable building blocks or not compatible with physiological observations. The simplicity of the circuit and the robustness of its function under parameter changes make this computational motif an attractive candidate for tuneable non-monotonic intensity coding.

Odorant concentration differentiator for intermittent olfactory signals

Animals need to discriminate differences in spatiotemporally distributed sensory signals in terms of quality as well as quantity for generating adaptive behavior. Olfactory signals characterized by odor identity and concentration are intermittently distributed in the environment. From these intervals of stimulation, animals process odorant concentration to localize partners or food sources. Although concentration-response characteristics in olfactory neurons have traditionally been investigated using single stimulus pulses, their behavior under intermittent stimulus regimens remains largely elusive. Using the silkmoth (Bombyx mori) pheromone processing system, a simple and behaviorally well-defined model for olfaction, we investigated the neuronal representation of odorant concentration upon intermittent stimulation in the naturally occurring range. To the first stimulus in a series, the responses of antennal lobe (AL) projection neurons (PNs) showed a concentration dependence as previously shown in many olfactory systems. However, PN response amplitudes dynamically changed upon exposure to intermittent stimuli of the same odorant concentration and settled to a constant, largely concentration-independent level. As a result, PN responses emphasized odorant concentration changes rather than encoding absolute concentration in pulse trains of stimuli. Olfactory receptor neurons did not contribute to this response transformation which was due to long-lasting inhibition affecting PNs in the AL. Simulations confirmed that inhibition also provides advantages when stimuli have naturalistic properties. The primary olfactory center thus functions as an odorant concentration differentiator to efficiently detect concentration changes, thereby improving odorant source orientation over a wide concentration range.

A review of hyperacusis and future directions: part II. Measurement, mechanisms, and treatment

PURPOSE

Hyperacusis can be extremely debilitating, and at present, there is no cure. In this detailed review of the field, we consolidate present knowledge in the hope of facilitating future research.

METHOD

We review and reference the literature on hyperacusis and related areas. This is the 2nd of a 2-part review.

RESULTS

Hyperacusis encompasses a wide range of reactions to sounds, which can be grouped into the categories of excessive loudness, annoyance, fear, and pain. Reasonable approaches to assessing the different forms of hyperacusis are emerging, including brain-imaging studies. Researchers are only beginning to understand the many mechanisms at play, and valid animal models are still evolving. There are many counseling and sound-therapy approaches that some patients find helpful, but well-controlled studies are needed to measure their long-term efficacy and to test new approaches.

CONCLUSIONS

Hyperacusis can make life difficult in this increasingly noisy world, forcing sufferers to dramatically alter their work and social habits. We believe this is an opportune time to explore approaches to better understand and treat hyperacusis.

Selective adaptation to "oddball" sounds by the human auditory system

Adaptation to both common and rare sounds has been independently reported in neurophysiological studies using probabilistic stimulus paradigms in small mammals. However, the apparent sensitivity of the mammalian auditory system to the statistics of incoming sound has not yet been generalized to task-related human auditory perception. Here, we show that human listeners selectively adapt to novel sounds within scenes unfolding over minutes. Listeners' performance in an auditory discrimination task remains steady for the most common elements within the scene but, after the first minute, performance improves for distinct and rare (oddball) sound elements, at the expense of rare sounds that are relatively less distinct. Our data provide the first evidence of enhanced coding of oddball sounds in a human auditory discrimination task and suggest the existence of an adaptive mechanism that tracks the long-term statistics of sounds and deploys coding resources accordingly.

Selectivity and sparseness in randomly connected balanced networks

Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the “paradoxical” effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

Midbrain local circuits shape sound intensity codes

Hierarchical processing of sensory information requires interaction at multiple levels along the peripheral to central pathway. Recent evidence suggests that interaction between driving and modulating components can shape both top down and bottom up processing of sensory information. Here we show that a component inherited from extrinsic sources combines with local components to code sound intensity. By applying high concentrations of divalent cations to neurons in the nucleus of the inferior colliculus in the auditory midbrain, we show that as sound intensity increases, the source of synaptic efficacy changes from inherited inputs to local circuits. In neurons with a wide dynamic range response to intensity, inherited inputs increase firing rates at low sound intensities but saturate at mid-to-high intensities. Local circuits activate at high sound intensities and widen dynamic range by continuously increasing their output gain with intensity. Inherited inputs are necessary and sufficient to evoke tuned responses, however local circuits change peak output. Push–pull driving inhibition and excitation create net excitatory drive to intensity-variant neurons and tune neurons to intensity. Our results reveal that dynamic range and tuning re-emerge in the auditory midbrain through local circuits that are themselves variable or tuned.

Robust encoding of stimulus identity and concentration in the accessory olfactory system

Sensory systems represent stimulus identity and intensity, but in the neural periphery these two variables are typically intertwined. Moreover, stable detection may be complicated by environmental uncertainty; stimulus properties can differ over time and circumstance in ways that are not necessarily biologically relevant. We explored these issues in the context of the mouse accessory olfactory system, which specializes in detection of chemical social cues and infers myriad aspects of the identity and physiological state of conspecifics from complex mixtures, such as urine. Using mixtures of sulfated steroids, key constituents of urine, we found that spiking responses of individual vomeronasal sensory neurons encode both individual compounds and mixtures in a manner consistent with a simple model of receptor-ligand interactions. Although typical neurons did not accurately encode concentration over a large dynamic range, from population activity it was possible to reliably estimate the log-concentration of pure compounds over several orders of magnitude. For binary mixtures, simple models failed to accurately segment the individual components, largely because of the prevalence of neurons responsive to both components. By accounting for such overlaps during model tuning, we show that, from neuronal firing, one can accurately estimate log-concentration of both components, even when tested across widely varying concentrations. With this foundation, the difference of logarithms, log A - log B = log A/B, provides a natural mechanism to accurately estimate concentration ratios. Thus, we show that a biophysically plausible circuit model can reconstruct concentration ratios from observed neuronal firing, representing a powerful mechanism to separate stimulus identity from absolute concentration.

Equalization of odor representations by a network of electrically coupled inhibitory interneurons

Robustness of neuronal activity patterns against variations in input intensity is critical for neuronal computations. We found that odor representations in the olfactory bulb were stabilized by interneurons that were densely coupled to the output neurons by electrical and GABAergic synapses. This interneuron network modulated responses of output neurons as a function of stimulus intensity in two ways: it globally boosted responses to weak odors, but attenuated responses to strong odors, and it increased the sensitivity of some output neurons, but decreased the sensitivity of others. These effects are closely related to strategies used in engineering to increase dynamic range. Together, they maintained not only the mean, but also the distribution, of activity across the population of output neurons within narrow limits, which is important for pattern classification. Neuronal circuits in the olfactory bulb therefore stabilize combinatorial sensory representations against variations in stimulus intensity by generic mechanisms.

Tuning of human modulation filters is carrier-frequency dependent

Recent studies employing speech stimuli to investigate 'cocktail-party' listening have focused on entrainment of cortical activity to modulations at syllabic (5 Hz) and phonemic (20 Hz) rates. The data suggest that cortical modulation filters (CMFs) are dependent on the sound-frequency channel in which modulations are conveyed, potentially underpinning a strategy for separating speech from background noise. Here, we characterize modulation filters in human listeners using a novel behavioral method. Within an 'inverted' adaptive forced-choice increment detection task, listening level was varied whilst contrast was held constant for ramped increments with effective modulation rates between 0.5 and 33 Hz. Our data suggest that modulation filters are tonotopically organized (i.e., vary along the primary, frequency-organized, dimension). This suggests that the human auditory system is optimized to track rapid (phonemic) modulations at high sound-frequencies and slow (prosodic/syllabic) modulations at low frequencies.

Perspective of functional magnetic resonance imaging in middle ear research

Functional magnetic resonance imaging (MRI) studies have frequently been applied to study sensory system such as vision, language, and cognition, but have proceeded at a considerably slower speed in investigating middle ear and central auditory processing. This is due to several factors, including the intrinsic anatomy of the middle ear system and inherent acoustic noise during acquisition of MRI data. However, accumulating evidences have demonstrated that clarification of some fundamental neural underpinnings of audition associated with middle ear mechanics can be achieved using functional MRI methods. This mini review attempted to take a narrow snapshot of the currently available functional MRI procedures and gave examples of what may be learned about hearing from their application. It is hoped that with these technical advancements, many new high impact applications in audition would follow. In particular, because the fMRI can be used in humans and in animals, fMRI may represent a unique tool that should promote translational research by enabling parallel analyses of physiological and pathological processes in the human and animal auditory system. This article is part of a special issue entitled "MEMRO 2012".

Functional magnetic resonance imaging of sound pressure level encoding in the rat central auditory system

Intensity is an important physical property of a sound wave and is customarily reported as sound pressure level (SPL). Invasive techniques such as electrical recordings, which typically examine one brain region at a time, have been used to study neuronal encoding of SPL throughout the central auditory system. Non-invasive functional magnetic resonance imaging (fMRI) with large field of view can simultaneously examine multiple auditory structures. We applied fMRI to measure the hemodynamic responses in the rat brain during sound stimulation at seven SPLs over a 72 dB range. This study used a sparse temporal sampling paradigm to reduce the adverse effects of scanner noise. Hemodynamic responses were measured from the central nucleus of the inferior colliculus (CIC), external cortex of the inferior colliculus (ECIC), lateral lemniscus (LL), medial geniculate body (MGB), and auditory cortex (AC). BOLD signal changes generally increase significantly (p<0.001) with SPL and the dependence is monotonic in CIC, ECIC, and LL. The ECIC has higher BOLD signal change than CIC and LL at high SPLs. The difference between BOLD signal changes at high and low SPLs is less in the MGB and AC. This suggests that the SPL dependences of the LL and IC are different from those in the MGB and AC and the SPL dependence of the CIC is different from that of the ECIC. These observations are likely related to earlier observations that neurons with firing rates that increase monotonically with SPL are dominant in the CIC, ECIC, and LL while non-monotonic neurons are dominant in the MGB and AC. Further, the IC's SPL dependence measured in this study is very similar to that measured in our earlier study using the continuous imaging method. Therefore, sparse temporal sampling may not be a prerequisite in auditory fMRI studies of the IC.

Evaluation of techniques used to estimate cortical feature maps

Functional properties of neurons are often distributed nonrandomly within a cortical area and form topographic maps that reveal insights into neuronal organization and interconnection. Some functional maps, such as in visual cortex, are fairly straightforward to discern with a variety of techniques, while other maps, such as in auditory cortex, have resisted easy characterization. In order to determine appropriate protocols for establishing accurate functional maps in auditory cortex, artificial topographic maps were probed under various conditions, and the accuracy of estimates formed from the actual maps was quantified. Under these conditions, low-complexity maps such as sound frequency can be estimated accurately with as few as 25 total samples (e.g., electrode penetrations or imaging pixels) if neural responses are averaged together. More samples are required to achieve the highest estimation accuracy for higher complexity maps, and averaging improves map estimate accuracy even more than increasing sampling density. Undersampling without averaging can result in misleading map estimates, while undersampling with averaging can lead to the false conclusion of no map when one actually exists. Uniform sample spacing only slightly improves map estimation over nonuniform sample spacing typical of serial electrode penetrations. Tessellation plots commonly used to visualize maps estimated using nonuniform sampling are always inferior to linearly interpolated estimates, although differences are slight at higher sampling densities. Within primary auditory cortex, then, multiunit sampling with at least 100 samples would likely result in reasonable feature map estimates for all but the highest complexity maps and the highest variability that might be expected.