Cochlear Implant Users can Effectively Combine Place and Timing Cues for Pitch Perception

OBJECTIVES

The study objective was to characterize cochlear implant (CI) pitch perception for pure, complex, and modulated tones for frequencies and fundamental frequencies in the ecologically essential range between 110 and 440 Hz. Stimulus manipulations were used to examine CI users' reliance on stimulation place and rate cues for pitch discrimination.

DESIGN

The study was a within-subjects design with 21 CI users completing pitch discrimination measures using pure, complex, and modulated tones. Stimulus manipulations were used to test whether CI users have better pitch discrimination for low-pass compared with high-pass filtered harmonic complexes, and to test whether they have better pitch discrimination when provided a covarying place cue when listening to amplitude-modulated tones.

RESULTS

Averaged across conditions, participants had better pitch discrimination for pure tones compared with either complex or amplitude-modulated tones. Participants had better pitch discrimination for low-pass compared with high-pass harmonic complexes and better pitch discrimination for amplitude-modulated tones when provided a covarying place cue.

CONCLUSIONS

CI users integrate place and rate cues across the ecologically essential pitch range between 110 and 440 Hz. We interpret the observed better pitch discrimination for low-pass compared with high-pass filtered harmonics complexes, and for amplitude-modulated tones when provided a covarying place cue, as evidence for the importance of providing place-of-excitation cues for fundamental frequencies below 440 Hz. Discussion considers how such encoding could be implemented with existing devices.

"What" and "when" predictions modulate auditory processing in a mutually congruent manner

Introduction

Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if "beat" timing predictions selectively interact with "what" predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates.

Methods

To address these questions, our study manipulated "what" and "when" predictions at different levels - (local) interval-defining and (global) beat-defining - within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task.

Results

Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of "what" predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of "what" and "when" predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network.

Discussion

Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing.

naplib-python: Neural Acoustic Data Processing and Analysis Tools in Python

Recently, the computational neuroscience community has pushed for more transparent and reproducible methods across the field. In the interest of unifying the domain of auditory neuroscience, naplib-python provides an intuitive and general data structure for handling all neural recordings and stimuli, as well as extensive preprocessing, feature extraction, and analysis tools which operate on that data structure. The package removes many of the complications associated with this domain, such as varying trial durations and multi-modal stimuli, and provides a general-purpose analysis framework that interfaces easily with existing toolboxes used in the field.

Questions and controversies surrounding the perception and neural coding of pitch

Pitch is a fundamental aspect of auditory perception that plays an important role in our ability to understand speech, appreciate music, and attend to one sound while ignoring others. The questions surrounding how pitch is represented in the auditory system, and how our percept relates to the underlying acoustic waveform, have been a topic of inquiry and debate for well over a century. New findings and technological innovations have led to challenges of some long-standing assumptions and have raised new questions. This article reviews some recent developments in the study of pitch coding and perception and focuses on the topic of how pitch information is extracted from peripheral representations based on frequency-to-place mapping (tonotopy), stimulus-driven auditory-nerve spike timing (phase locking), or a combination of both. Although a definitive resolution has proved elusive, the answers to these questions have potentially important implications for mitigating the effects of hearing loss via devices such as cochlear implants.

Prior Acoustic Trauma Alters Type II Afferent Activity in the Mouse Cochlea

Auditory stimuli travel from the cochlea to the brainstem through type I and type II cochlear afferents. While type I afferents convey information about the frequency, intensity, and timing of sounds, the role of type II afferents remains unresolved. Limited recordings of type II afferents from cochlear apex of prehearing rats reveal they are activated by widespread outer hair cell stimulation, ATP, and by the rupture of nearby outer hair cells. Altogether, these lines of evidence suggest that type II afferents sense loud, potentially damaging levels of sound. To explore this hypothesis further, calcium imaging was used to determine the impact of acoustic trauma on the activity of type II cochlear afferents of young adult mice of both sexes. Two known marker genes (Th, Drd2) and one new marker gene (Tac1), expressed in type II afferents and some other cochlear cell types, drove GCaMP6f expression to reveal calcium transients in response to focal damage in the organ of Corti in all turns of the cochlea. Mature type II afferents responded to acute photoablation damage less often but at greater length compared with prehearing neurons. In addition, days after acoustic trauma, acute photoablation triggered a novel response pattern in type II afferents and surrounding epithelial cells, delayed bursts of activity occurring minutes after the initial response subsided. Overall, calcium imaging can report type II afferent responses to damage even in mature and noise-exposed animals and reveals previously unknown tissue hyperactivity subsequent to acoustic trauma.

Objective Estimation of Sensory Thresholds Based on Neurophysiological Parameters

Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold is estimated based on such measures, the standard approach until now is the subjective setting-either by eye or by statistical means-of the threshold to the value where at least a "clear" signal is detectable. These measures are highly subjective, strongly depend on the noise, and fluctuate due to the low signal-to-noise ratio near the threshold. Here we show a novel method to reliably estimate physiological thresholds based on neurophysiological parameters. Using surrogate data we demonstrate that fitting the responses to different stimulus intensities with a hard sigmoid function, in combination with subsampling, provides a robust threshold value as well as an accurate uncertainty estimate. This method has no systematic dependence on the noise and does not even require samples in the full dynamic range of the sensory system. We prove that this method is universally applicable to all types of sensory systems, ranging from somatosensory stimulus processing in the cortex to auditory processing in the brain stem.

NAPLIB: AN OPEN SOURCE TOOLBOX FOR REAL-TIME AND OFFLINE NEURAL ACOUSTIC PROCESSING

In this paper, we introduce the Neural Acoustic Processing Library (NAPLib), a toolbox containing novel processing methods for real-time and offline analysis of neural activity in response to speech. Our method divides the speech signal and resultant neural activity into segmental units (e.g., phonemes), allowing for fast and efficient computations that can be implemented in real-time. NAPLib contains a suite of tools that characterize various properties of the neural representation of speech, which can be used for functionality such as characterizing electrode tuning properties, brain mapping and brain computer interfaces. The library is general and applicable to both invasive and non-invasive recordings, including electroencephalography (EEG), electrocorticography (ECoG) and magnetoecnephalography (MEG). In this work, we describe the structure of NAPLib, as well as demonstrating its use in both EEG and ECoG. We believe NAPLib provides a valuable tool to both clinicians and researchers who are interested in the representation of speech in the brain.

The steady-state response of the cerebral cortex to the beat of music reflects both the comprehension of music and attention

The brain’s analyses of speech and music share a range of neural resources and mechanisms. Music displays a temporal structure of complexity similar to that of speech, unfolds over comparable timescales, and elicits cognitive demands in tasks involving comprehension and attention. During speech processing, synchronized neural activity of the cerebral cortex in the delta and theta frequency bands tracks the envelope of a speech signal, and this neural activity is modulated by high-level cortical functions such as speech comprehension and attention. It remains unclear, however, whether the cortex also responds to the natural rhythmic structure of music and how the response, if present, is influenced by higher cognitive processes. Here we employ electroencephalography to show that the cortex responds to the beat of music and that this steady-state response reflects musical comprehension and attention. We show that the cortical response to the beat is weaker when subjects listen to a familiar tune than when they listen to an unfamiliar, non-sensical musical piece. Furthermore, we show that in a task of intermodal attention there is a larger neural response at the beat frequency when subjects attend to a musical stimulus than when they ignore the auditory signal and instead focus on a visual one. Our findings may be applied in clinical assessments of auditory processing and music cognition as well as in the construction of auditory brain-machine interfaces.

Optimized design and analysis of sparse-sampling FMRI experiments

Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase the number of samples and improve statistical power.