Jennifer F. Linden

Background: Peripheral hearing loss is associated with auditory hallucinations and increased risk for psychotic disorder, particularly in genetically vulnerable individuals. Moreover, both hearing loss and schizophrenia disrupt auditory temporal acuity, a sensitive measure of auditory brain function. Here, we used mouse models of hearing loss and schizophrenia to reveal how neural mechanisms of auditory cortical temporal acuity depend on sensory and genetic risk factors for psychosis. Methods: We quantified auditory cortical temporal acuity in mice with or without hearing loss and with or without the 22q11.2 deletion — one of the strongest known genetic risk factors for schizophrenia. Cortical single-unit and population activity were recorded in awake mice (N = 23) during presentations of loudness-adjusted gap-in-noise stimuli with varying durations of silent gap, which are commonly used to assess auditory temporal acuity in humans. Results: Both hearing loss and the 22q11.2 deletion disrupted auditory cortical temporal acuity, but through distinct mechanisms. Hearing loss broadly degraded temporal acuity at the level of single-unit responses and neural population activity. In contrast, the 22q11.2 deletion selectively impaired gap duration thresholds in regular-spiking (putative excitatory) but not fast-spiking (putative inhibitory) neurons. Mice with comorbid hearing loss and 22q11.2 deletion exhibited both abnormalities in auditory cortical temporal acuity. Conclusions: Sensory and genetic risk factors for psychosis can disrupt auditory cortical temporal acuity via distinct mechanisms that remain partially dissociable even under comorbid conditions. These findings underscore the importance of accounting for hearing loss comorbidity when interpreting auditory cortical dysfunction in psychotic disorder.

Human brain activity tracks slow fluctuations in continuous sound, including speech, and modelling this relationship provides insight into how the brain encodes naturalistic auditory input. Traditional approaches to modelling brain tracking of speech with linear regression often use the amplitude envelope of the stimulus as a regressor, implicitly assuming that neural responses scale linearly with sound intensity. However, previous studies of central auditory processing in both animals and humans have shown that adaptive mechanisms adjust auditory responses based on recent sound history. Here, we find that a simple nonlinear transformation of the stimulus envelope, derived from studies of mouse auditory thalamic responses to temporally varying sounds, significantly improves modelling of human cortical speech tracking. This Adaptive Gain transformation essentially normalizes sound level by its recent context. Improvements in modelling of cortical speech tracking using the Adaptive Gain stimulus representation are robust across experiments, participant groups, and languages, indicating that short-timescale adaptation to recent sound level is a general feature of auditory processing. We further show that the optimal adaptation time constant for human cortical responses to continuous speech is approximately 50–100 ms, longer than previously observed for auditory thalamic responses to temporally varying sounds in mice. In summary, the Adaptive Gain transformation is a mathematically simple alternative to the standard envelope representation that captures dynamic adaptation in auditory processing and reliably improves prediction of human cortical EEG signals during listening to continuous speech. Significance Statement: Standard models of human brain responses to continuous speech use the raw stimulus envelope as a predictor of EEG responses, implicitly assuming stationary sensitivity to sound intensity. Here, we use instead a simple nonlinear transformation of the stimulus envelope, Adaptive Gain, which normalizes sound level based on recent sound history over tens to hundreds of milliseconds. Originally derived from studies of auditory processing in mice, the Adaptive Gain transformation robustly improves prediction of EEG responses to continuous speech in humans across experiments, participant groups, and languages. Results demonstrate that dynamic adaptation to sound level is a core feature of auditory processing, and can be approximated in a mathematically simple form to improve prediction of cortical speech tracking.

Accurate detection of sound onsets and offsets is thought to be vital for speech perception. Sound‐onset event‐related potentials (ERPs) have been well characterised in EEG studies, but the characteristics of sound‐offset ERPs have often been obscured by temporal confounds in experimental design. Here two EEG studies were conducted in human listeners performing an interval duration discrimination task with (i) noise intervals in silence or (ii) silent intervals (gaps) in noise. Stimuli used in the active task were also presented under passive listening conditions with no participant response required. This design enabled us to investigate whether features of the offset ERP could be masked by task demands, and whether the usual temporal precedence of the onset event in a sound influences the relative magnitude and shape of onset and offset ERPs. The morphology of the offset ERP was distinct from that of the onset ERP in both noise and silent interval duration discrimination tasks, even though the functional roles of onsets and offsets as start‐interval versus end‐interval cues were reversed between the two tasks. This observation corroborates evidence from animal studies that there are fundamental differences in the brain mechanisms of onset and offset perception. In all experimental conditions, the amplitude of the offset ERP was one‐third to one‐half that of the onset ERP. Differences between active and passive listening conditions were largely explained by enhancement of ERPs for whichever transient marked the end of the discriminated intervals. However, this context dependence emerged in earlier ERP waves for offset than onset responses. image Key points: Onset and offset event‐related potentials (ERPs) differ in morphology and amplitude. For both onset and offset ERPs, P200 and later waves depend on behavioural context. For offset ERPs only, P100/N100 waves also depend on behavioural context. Engagement in duration discrimination alters ERPs primarily at the end of intervals.

Sound‐onset and sound‐offset encoding are critical aspects of auditory temporal processing with important roles in speech‐in‐noise perception. We investigated brain correlates of active‐offset encoding in older adults and their relationship to real‐world listening. A total of 34 adults aged 60–81 years underwent pure‐tone audiometry and assessments of speech‐in‐noise ability at both word and sentence levels. EEG data were recorded while participants performed active duration discrimination of noise segments and during passive listening. Onset and offset responses were extracted from EEG data, and amplitude differences were analysed. We found robust onset and offset responses at a single‐subject level. Onset responses were significantly reduced in the active condition compared to passive listening, whereas offset responses were significantly larger. This pattern suggests that offset encoding is more susceptible to attentional modulation, whereas onset responses may be less affected. We found that active sound‐offset amplitudes were significantly correlated with sentence‐in‐noise performance, whereas no correlation was observed for word‐in‐noise performance. We suggest a role for offset analysis in parsing of the speech stream needed for speech segregation. The active EEG measure we use allows single‐subject inference and is a potential clinical measure of auditory cognition relevant to speech‐in‐noise listening. image Key points: Sound‐offset responses can be robustly identified at the single‐subject level using EEG. Sound‐offset responses were larger in the active task than in passive listening, suggesting task dependency and indicating that attention may be involved in sound‐offset encoding. Active sound‐offset responses correlate with sentence‐level speech‐in‐noise performance.

Accurate detection of sound onsets and offsets is vital for speech perception. Sound-onset event-related potentials (ERPs) have been well-characterised in electroencephalography (EEG) studies, but the characteristics of sound-offset ERPs have often been obscured by temporal confounds in experimental design. Here, two EEG studies were conducted in human listeners performing an interval duration discrimination task with (i) noise intervals in silence or (ii) silent intervals (gaps) in noise. Stimuli used in the active task were also presented under passive listening conditions. This design enabled us to investigate whether features of the offset ERP could be masked by task demands, and whether the usual temporal precedence of the onset cue in a sound influences the relative magnitude and shape of onset and offset ERPs. The morphology of the offset ERP was distinct from that of the onset ERP in both noise and silent interval duration discrimination tasks, even though the roles of onset and offset cues as initial versus final markers of interval duration were reversed. This observation corroborates evidence from animal studies that there are fundamental differences in brain mechanisms of onset and offset perception. In all experimental conditions, the amplitude of the offset ERP was one-third to one-half that of the onset ERP. Differences between active and passive listening conditions were largely explained by enhancement of ERPs for whichever cue (onset or offset) marked the end of the intervals compared in the duration discrimination task. However, this context dependence emerged in earlier ERP waves for offset than onset responses. Key points summary: Onset and offset event-related potentials (ERPs) differ in morphology and amplitude For both onset and offset ERPs, P200 and later waves depend on behavioural context For offset ERPs only, P100/N100 waves also depend on behavioural context Engagement in duration discrimination alters ERPs primarily for end-interval cues

The 22q11.2 deletion is a risk factor for multiple psychiatric disorders including schizophrenia and also increases vulnerability to middle-ear problems that can cause hearing impairment. Up to 60% of deletion carriers experience hearing impairment and ~30% develop schizophrenia in adulthood. It is not known if these risks interact. Here we used the Df1/+ mouse model of the 22q11.2 deletion to investigate how hearing impairment might interact with increased genetic vulnerability to psychiatric disease to affect brain function. We measured brain function using cortical auditory evoked potentials (AEPs), which are commonly measured non-invasively in humans. After identifying one of the simplest and best-validated methods for AEP measurement in mice from the diversity of previous approaches, we measured peripheral hearing sensitivity and cortical AEPs in Df1/+ mice and their WT littermates. We exploited large inter-individual variation in hearing ability among Df1/+ mice to distinguish effects of genetic background from effects of hearing impairment. Central auditory gain and adaptation were quantified by comparing brainstem activity and cortical AEPs and by analyzing the growth of cortical AEPs with increasing sound level or inter-tone interval duration. We found that level-dependent AEP growth was abnormally large in Df1/+ mice regardless of hearing impairment, but other AEP measures of central auditory gain and adaptation depended on both genotype and hearing phenotype. Our results demonstrate the relevance of comorbid hearing loss to auditory brain dysfunction in 22q11.2DS and also identify potential biomarkers for psychiatric disease that are robust to hearing impairment.

The perceptual salience of a sound depends on the acoustic context in which it appears, and can vary on a timescale of milliseconds. At the level of single neurons in the auditory cortex, spectrotemporal tuning for particular sounds is shaped by a similarly fast and systematic nonlinear sensitivity to acoustic context. Does this neuronal context sensitivity “drift” over time in awake animals, or is it a stable feature of sound representation in the auditory cortex? We used chronically implanted tetrode arrays in awake mice to measure the electrophysiological responses of auditory cortical neurons to spectrotemporally complex, rapidly varying sounds across many days. For each neuron in each recording session, we applied the nonlinear-linear “context model” to estimate both a principal (spectrotemporal) receptive field and a “contextual gain field” describing the neuron’s nonlinear sensitivity to acoustic context. We then quantified the stability of these fields within and across days, using spike waveforms to match neurons recorded in multiple sessions. Contextual gain fields of auditory cortical neurons in awake mice were remarkably stable across many days of recording, and comparable in stability to principal receptive fields. Interestingly, there were small but significant effects of changes in locomotion or pupil size on the ability of the context model to fit temporal fluctuations in the neuronal response. We conclude that both spectrotemporal tuning and nonlinear sensitivity to acoustic context are stable features of neuronal sound representation in the awake auditory cortex, which can be modulated by behavioral state.

The 22q11.2 deletion is a risk factor for multiple psychiatric disorders including schizophrenia and also increases vulnerability to middle-ear problems that can cause hearing impairment. Up to 60% of deletion carriers experience hearing impairment and ∼30% develop schizophrenia in adulthood. It is not known if these risks interact. Here we used the Df1/+ mouse model of the 22q11.2 deletion to investigate how hearing impairment might interact with increased genetic vulnerability to psychiatric disease to affect brain function. We measured brain function using cortical auditory evoked potentials (AEPs), which are commonly measured non-invasively in humans. After identifying one of the simplest and best-validated methods for AEP measurement in mice from the diversity of previous approaches, we measured peripheral hearing sensitivity and cortical AEPs in Df1/+ mice and their WT littermates. We exploited large inter-individual variation in hearing ability among Df1/+ mice to distinguish effects of genetic background from effects of hearing impairment. Central auditory gain and adaptation were quantified by comparing brainstem activity and cortical AEPs and by analyzing the growth of cortical AEPs with increasing sound level or inter-tone interval duration. We found distinctive measures of central auditory gain or adaptation that were abnormal in Df1/+ mice regardless of hearing impairment, and other measures that were abnormal only in Df1/+ mice with or without hearing impairment. Our data identify potential biomarkers for auditory brain dysfunction in psychiatric disease and illustrate that central auditory abnormalities in 22q11.2DS are a function of both genotype and hearing phenotype.

The perceptual salience of a sound depends on the acoustic context in which it appears, and can vary on a timescale of milliseconds. At the level of single neurons in the auditory cortex, spectrotemporal tuning for particular sounds is shaped by a similarly fast and systematic nonlinear sensitivity to acoustic context. Does this neuronal context sensitivity “drift” over time in awake animals, or is it a stable feature of sound representation in the auditory cortex? We used chronically implanted tetrode arrays in awake mice to measure the electrophysiological responses of auditory cortical neurons to spectrotemporally complex, rapidly varying sounds across many days. For each neuron in each recording session, we applied the nonlinear-linear “context model” to estimate both a principal (spectrotemporal) receptive field and a “contextual gain field” describing the neuron’s nonlinear sensitivity to acoustic context. We then quantified the stability of these fields within and across days, using spike waveforms to match neurons recorded in multiple sessions. Contextual gain fields of auditory cortical neurons in awake mice were remarkably stable across many days of recording, and comparable in stability to principal receptive fields. Interestingly, there were small but significant effects of changes in locomotion or pupil size on the ability of the context model to fit temporal fluctuations in the neuronal response. We conclude that both spectrotemporal tuning and nonlinear sensitivity to acoustic context are stable features of neuronal sound representation in the awake auditory cortex, which can be modulated by behavioral state.