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A , Model space to establish how stimulus information reaches the amygdala. In M1, stimuli activate the amygdala (Amyg) directly; in M2, the stimulus is first processed by the auditory cortex (Aud) and then reaches the amygdala; in M3, both the amygdala and the auditory cortex receive the stimulus independently. B , Model exceedance probabilities for the models shown in A . M2, in which the auditory cortex drives the amygdala, is the best model (exceedance probability ϭ 0.97).
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This study addresses the neuronal representation of aversive sounds that are perceived as unpleasant. Functional magnetic resonance imaging in humans demonstrated responses in the amygdala and auditory cortex to aversive sounds. We show that the amygdala encodes both the acoustic features of a stimulus and its valence (perceived unpleasantness). Dy...
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Context 1
... tested alternative models based on direct inputs to the amygdala and inputs via the auditory cortex ( Fig. 5 A ). In the models, no subcortical structure (e.g., thalamus) of the auditory system was included because no reliable activity was detected in these structures. This could be because of their small size or motion of brainstem (Poncelet et al., 1992). In the first model (M1, direct), the stimulus is received directly by the amygdala, which then drives the auditory cortex. In the second model (M2, via auditory cortex), the stimulus is first processed in the auditory cortex which then drives the amygdala. In the third model (M3, both), the amygdala and the auditory cortex are driven by the stimulus independently. The connectivity of all the three models is same, the only difference being the location of driving inputs. These models were estimated for 13 subjects and compared using Bayesian model comparison with random effects. The model exceedance probabilities of the three models are shown in Figure 5 B . These results show that the model in which the stimulus first reaches the features. A , Negative auditory cortex, which then drives the amygdala (model M2), is the best model (exceedance probability ϭ 0.97). The Bayes factor for best model (M2) compared with the next-best model (M1) is ϳ 35, which implies a strong evidence (see Materials and Methods, Connectivity analysis: dynamic causal modeling, above) for model M2. To answer this question, we created a set of four models (Fig. 6). In the first model (M1, none), a null model, the acoustic features The structure of models in this comparison is the same as in Figure 6, but here the modulation is by valence rather than acoustic features. The exceedance probabilities of the models (random- effects analysis, 13 subjects) are shown in Figure 7 B . The model in which the backward connections from the amygdala to the auditory cortex are modulated by perceived unpleasantness has the highest probability (0.83). Analysis of the parameters of the best model showed that all modulatory influences are statistically significant. The Bayes factor for the best model (M2) compared with the next-best model (M1) is 6.91, which implies positive evidence for model M2. A number of previous studies have implicated the amygdala in the perception of aversive sounds. In this paper, using conventional GLM analysis and effective connectivity analysis using DCM, we answer three questions that are important in building a detailed model of how the aversive percepts are formed: (1) What does the amygdala encode? (2) How does the stimulus reach the amygdala? (3) How does the amygdala interact with the auditory cortex? One model of amygdala function suggests that it encodes the value of stimuli both external and internal to an organism (Morrison and Salzman, 2010). Results of most of the previous studies that have implicated the amygdala in processing of emotional information are confounded by lack of control of low-level sensory features. In this work, we distinguished areas of the amygdala that process acoustic features from those that process valence by explicitly modeling the sensory features of stimuli and valence and using them as explanatory variables in fMRI analysis. Our results demonstrate that acoustic features of stimuli are en- coded in the amygdala. This is consistent with the few studies in the literature that have examined the encoding of sensory features in the amygdala. A study in rodents (Bordi and LeDoux, 1992) has shown that neurons in the amygdala are tuned to high frequencies ( Ͼ 10 kHz) relevant to negative affect (e.g., distress calls). Similarly Du et al. (2009) measured frequency following response to a chatter sound in rats. For nonauditory stimuli, Kadohisa et al. (2005) showed a detailed representation of food stimulus features, such as viscosity, fat texture, and temperature, exists in the amygdala. Although much is known about the roles played by different nuclei of the amygdala in animals (LeDoux, 2000), many details are not available in humans. Thanks to the availability of amygdala maps (Eickhoff et al., 2005), recent studies in humans (Ball et al., 2007) have started to tease apart the contributions of different nuclei in the amygdala. In our data, the distribution of responses in different nuclei shows that both basolateral and superficial nuclei of the amygdala encode the acoustic features nec- essary for attributing valence. This is in agreement with the animal models of amygdala function, in which the basolateral nucleus acts as a gateway for sensory information to the amygdala. Less is known about the role of superficial nucleus in humans. One study (Ball et al., 2007), however, showed that this nucleus responds to auditory input. Our results show that the amygdala encodes not only the low- level acoustic features that determine valence but also the valence itself. This is in agreement a number of neuroimaging studies in normal subjects and psychopathology that implicate the amygdala in the subjective experience of negative affect. In the ...
Context 2
... tested alternative models based on direct inputs to the amygdala and inputs via the auditory cortex ( Fig. 5 A ). In the models, no subcortical structure (e.g., thalamus) of the auditory system was included because no reliable activity was detected in these structures. This could be because of their small size or motion of brainstem (Poncelet et al., 1992). In the first model (M1, direct), the stimulus is received directly by the amygdala, which then drives the auditory cortex. In the second model (M2, via auditory cortex), the stimulus is first processed in the auditory cortex which then drives the amygdala. In the third model (M3, both), the amygdala and the auditory cortex are driven by the stimulus independently. The connectivity of all the three models is same, the only difference being the location of driving inputs. These models were estimated for 13 subjects and compared using Bayesian model comparison with random effects. The model exceedance probabilities of the three models are shown in Figure 5 B . These results show that the model in which the stimulus first reaches the features. A , Negative auditory cortex, which then drives the amygdala (model M2), is the best model (exceedance probability ϭ 0.97). The Bayes factor for best model (M2) compared with the next-best model (M1) is ϳ 35, which implies a strong evidence (see Materials and Methods, Connectivity analysis: dynamic causal modeling, above) for model M2. To answer this question, we created a set of four models (Fig. 6). In the first model (M1, none), a null model, the acoustic features The structure of models in this comparison is the same as in Figure 6, but here the modulation is by valence rather than acoustic features. The exceedance probabilities of the models (random- effects analysis, 13 subjects) are shown in Figure 7 B . The model in which the backward connections from the amygdala to the auditory cortex are modulated by perceived unpleasantness has the highest probability (0.83). Analysis of the parameters of the best model showed that all modulatory influences are statistically significant. The Bayes factor for the best model (M2) compared with the next-best model (M1) is 6.91, which implies positive evidence for model M2. A number of previous studies have implicated the amygdala in the perception of aversive sounds. In this paper, using conventional GLM analysis and effective connectivity analysis using DCM, we answer three questions that are important in building a detailed model of how the aversive percepts are formed: (1) What does the amygdala encode? (2) How does the stimulus reach the amygdala? (3) How does the amygdala interact with the auditory cortex? One model of amygdala function suggests that it encodes the value of stimuli both external and internal to an organism (Morrison and Salzman, 2010). Results of most of the previous studies that have implicated the amygdala in processing of emotional information are confounded by lack of control of low-level sensory features. In this work, we distinguished areas of the amygdala that process acoustic features from those that process valence by explicitly modeling the sensory features of stimuli and valence and using them as explanatory variables in fMRI analysis. Our results demonstrate that acoustic features of stimuli are en- coded in the amygdala. This is consistent with the few studies in the literature that have examined the encoding of sensory features in the amygdala. A study in rodents (Bordi and LeDoux, 1992) has shown that neurons in the amygdala are tuned to high frequencies ( Ͼ 10 kHz) relevant to negative affect (e.g., distress calls). Similarly Du et al. (2009) measured frequency following response to a chatter sound in rats. For nonauditory stimuli, Kadohisa et al. (2005) showed a detailed representation of food stimulus features, such as viscosity, fat texture, and temperature, exists in the amygdala. Although much is known about the roles played by different nuclei of the amygdala in animals (LeDoux, 2000), many details are not available in humans. Thanks to the availability of amygdala maps (Eickhoff et al., 2005), recent studies in humans (Ball et al., 2007) have started to tease apart the contributions of different nuclei in the amygdala. In our data, the distribution of responses in different nuclei shows that both basolateral and superficial nuclei of the amygdala encode the acoustic features nec- essary for attributing valence. This is in agreement with the animal models of amygdala function, in which the basolateral nucleus acts as a gateway for sensory information to the amygdala. Less is known about the role of superficial nucleus in humans. One study (Ball et al., 2007), however, showed that this nucleus responds to auditory input. Our results show that the amygdala encodes not only the low- level acoustic features that determine valence but also the valence itself. This is in agreement a number of neuroimaging studies in normal subjects and psychopathology that implicate the amygdala in the subjective experience of negative affect. In the auditory domain, although a number of studies show activity in the amygdala in response to unpleasant sounds (Phillips et al., 1998; Morris et al., 1999; Mirz et al., 2000; Sander and Scheich, 2001; Zald and Pardo, 2002), these studies have not specifically examined the relation between its activity and the subjective experience of emotions. However, one study (Blood and Zatorre, 2001) using music as emotional stimuli showed activity of the right amygdala was negatively correlated with increasing chills, experienced by subjects when they listened to certain pieces of music. In studies using nonauditory stimuli, Zald and Pardo (1997) showed that responses in the left amygdala correlated positively with the subjective ratings of aversiveness of odor. Ketter et al. (1996) observed greater regional cerebral blood flow in the left amygdala in response to procaine-induced fear, which correlated with the ...
Citations
... Some aspects of the experimental system and approaches we have described could be relevant for research on the mechanisms of emotionally salient vocal communication. Sounds with positive or negative qualities (valences) are perceived and processed differently than neutral sounds in brain regions like the amygdala, and multimodal cues such as facial expressions can influence the process of emotional judgment (Rankin et al., 2009;Skipper et al., 2009;Kumar et al., 2012;Gerdes et al., 2014;Husain et al., 2014;Buono et al., 2021). Age, hearing loss, and social isolation/loneliness can alter the evaluation of the emotional value of sounds, generally dampening the distinctions between neutral and pleasant or unpleasant sounds (Picou, 2016;Picou and Buono, 2018;Zinchenko et al., 2018;Christensen et al., 2019). ...
The context surrounding vocal communication can have a strong influence on how vocal signals are perceived. The serotonergic system is well-positioned for modulating the perception of communication signals according to context, because serotonergic neurons are responsive to social context, influence social behavior, and innervate auditory regions. Animals like lab mice can be excellent models for exploring how serotonin affects the primary neural systems involved in vocal perception, including within central auditory regions like the inferior colliculus (IC). Within the IC, serotonergic activity reflects not only the presence of a conspecific, but also the valence of a given social interaction. To assess whether serotonin can influence the perception of vocal signals in male mice, we manipulated serotonin systemically with an injection of its precursor 5-HTP, and locally in the IC with an infusion of fenfluramine, a serotonin reuptake blocker. Mice then participated in a behavioral assay in which males suppress their ultrasonic vocalizations (USVs) in response to the playback of female broadband vocalizations (BBVs), used in defensive aggression by females when interacting with males. Both 5-HTP and fenfluramine increased the suppression of USVs during BBV playback relative to controls. 5-HTP additionally decreased the baseline production of a specific type of USV and male investigation, but neither drug treatment strongly affected male digging or grooming. These findings show that serotonin modifies behavioral responses to vocal signals in mice, in part by acting in auditory brain regions, and suggest that mouse vocal behavior can serve as a useful model for exploring the mechanisms of context in human communication.
... For instance, the amygdala, a central structure in emotional information processing, is reciprocally connected to the medial geniculate nucleus of the thalamus and the auditory cortex (LeDoux et al., 1984;Viinikainen et al., 2012). This connection allows the transfer of acoustic features of stimuli through forward connections from auditory cortex regions to the amygdala, and the emotional valence of auditory stimuli through backward projections from the amygdala to the auditory cortex (Kumar et al., 2012). The auditory cortex also plays an equivalent and complementary role to the amygdala, decoding complex emotional sounds and sound features that evolve over time (Frühholz et al., 2014). ...
A growing number of health-related sciences, including audiology, have increasingly recognized the importance of affective phenomena. However, in audiology, affective phenomena are mostly studied as a consequence of hearing status. This review first addresses anatomical and functional bidirectional connections between auditory and affective systems that support a reciprocal affect-hearing relationship. We then postulate, by focusing on four practical examples (hearing public campaigns, hearing intervention uptake, thorough hearing evaluation, and tinnitus), that some important challenges in audiology are likely affect-related and that potential solutions could be developed by inspiration from affective science advances. We continue by introducing useful resources from affective science that could help audiology professionals learn about the wide range of affective constructs and integrate them into hearing research and clinical practice in structured and applicable ways. Six important considerations for good quality affective audiology research are summarized. We conclude that it is worthwhile and feasible to explore the explanatory power of emotions, feelings, motivations, attitudes, moods, and other affective processes in depth when trying to understand and predict how people with hearing difficulties perceive, react, and adapt to their environment.
... Such aversive sounds elicit a distinctive pattern of heart rate responses compared to other unpleasant, pleasant and neutral sounds (Schweiger Gallo et al., 2017). On the neural level, they activate the amygdala, reflecting the sound's association with negative emotions, and are (in comparison to neutral sounds) accompanied by higher activity in auditory cortex regions (Kumar et al., 2012). In the current study, we use electroencephalography (EEG) to compare brain responses and their habituation characteristics between such aversive sounds and sounds with neutral valence that are embedded as infrequent "deviant" sounds in an oddball paradigm while participants do not pay attention to the sound input. ...
... If we consider MMN to reflect pre-attentive, automatic stimulus processing, its dependence on or predictability by the arousal associated with a deviant might suggest contributions from brain structures associated with processing emotional or arousing stimuli. For instance, Kumar et al. (2012) showed that aversive sounds activated the amygdala, and that amygdala activity, in turn, modulated activity in the auditory cortex in order to facilitate sensory processing and evaluation of these stimuli. This is also in line with previously reported amygdala activation for emotional compared to neutral voices (Schirmer et al., 2008). ...
There are sounds that most people perceive as highly unpleasant, for instance, the sound of rubbing pieces of polystyrene together. Previous research showed larger physiological and neural responses for such aversive compared to neutral sounds. Hitherto, it remains unclear whether habituation, i.e., diminished responses to repeated stimulus presentation, which is typically reported for neutral sounds, occurs to the same extent for aversive stimuli. We measured the mismatch negativity (MMN) in response to rare occurrences of aversive or neutral deviant sounds within an auditory oddball sequence in 24 healthy participants, while they performed a demanding visual distractor task. Deviants occurred as single events (i.e., between two standards) or as double deviants (i.e., repeating the identical deviant sound in two consecutive trials). All deviants elicited a clear MMN, and amplitudes were larger for aversive than for neutral deviants (irrespective of their position within a deviant pair). This supports the claim of preattentive emotion evaluation during early auditory processing. In contrast to our expectations, MMN amplitudes did not show habituation, but increased in response to deviant repetition—similarly for aversive and neutral deviants. A more fine‐grained analysis of individual MMN amplitudes in relation to individual arousal and valence ratings of each sound item revealed that stimulus‐specific MMN amplitudes were best predicted by the interaction of deviant position and perceived arousal, but not by valence. Deviants with perceived higher arousal elicited larger MMN amplitudes only at the first deviant position, indicating that the MMN reflects preattentive processing of the emotional content of sounds.
... Certain sounds are unanimously recognised as being unpleasant, regardless of the context of their occurrence. The sound produced by chalk on a slate blackboard, distorted harmonies, modulations of the amplitude in the rugosity range (between 20 Hz and 300 Hz), or pure sounds at high frequencies are perceived as unpleasant [10,48,49]. There are also clinical conditions in auditions that are specifically defined by a drop in the threshold of tolerance to sounds. ...
Tinnitus is an auditory sensation without external acoustic stimulation or significance, which may be lived as an unpleasant experience and impact the subject’s quality of life. Tinnitus loudness, which is generally low, bears no relation to distress. Factors other than psychoacoustic (such as psychological factors) are therefore implicated in the way tinnitus is experienced. The aim of this article is to attempt to understand how tinnitus can, like chronic pain, generate a ‘crisis’ in the process of existence, which may go as far as the collapse of the subject. The main idea put forward in the present article is that tinnitus may be compared to the phenomenon of pain from the point of view of the way it is experienced. Although the analogy between tinnitus and pain has often been made in the literature, it has been limited to a parallel concerning putative physiopathological mechanisms and has never really been explored in depth from the phenomenological point of view. Tinnitus is comparable to pain inasmuch as it is felt, not perceived: it springs up (without intention or exploration), abolishes the distance between the subject and the sensation (there is only a subject and no object), and has nothing to say about the world. Like pain, tinnitus is formless and abnormal and can alter the normal order of the world with maximum intensity. Finally, tinnitus and pain enclose the subject within the limits of the body, which then becomes in excess. Tinnitus may be a source of suffering, which affects not only the body but a person’s very existence and, in particular, its deployment in time. Plans are thus abolished, so time is no longer ‘secreted’, it is enclosed in an eternal present. If the crisis triggered by tinnitus is not resolved, the subject may buckle and collapse (depression) when their resources for resisting are depleted. The path may be long and winding from the moment when tinnitus emerges to when it assaults existence and its eventual integration into a new existential norm where tinnitus is no longer a source of disturbance.
... The structure of sound, including its level, has a bearing on how annoying it is perceived by the listener [6]. For example, the scraping of fingernails on a whiteboard is perceived to be significantly different from the sound of a babbling stream. ...
This paper presents a flexible, low-cost, and ultra-low-energy acoustic sensor network solution. This enables both highly distributed and real-time audio measurements to be employed in a wide variety of scenarios. These types of distributed measurements can be useful in environmental monitoring, healthcare settings, smart cities, and security surveillance systems. However, they are typically inflexible in their applications and require specialized deployment. The proposed solution is nonintrusive and uses long-range radio to enable low-power distributed audio monitoring. The system can recognize and be configured for different types of audio measurement and analysis tasks (such as sound pressure level, annoyance, direction of arrival, and roughness), and is shown to be as accurate
r=0.998
as a high-cost measurement system. Remote control, time of day, and grid capture mechanisms can also be realized. The proposed solution provides the ability for an over-the-air configuration, leading to minimal physical access required once deployed. In addition, higher data rates are possible, offering real-time monitoring metrics.
... The spectral and temporal modulation features of speech spectrogram are also highly related to speech intelligibility, noise and reverberation effects [31]. In [32], authors report that the spectro-temporal representation of audio (non-speech) signals with positive\negative valence is different from that of neutral sounds. They also report that spectral frequency and temporal modulation frequency can represent the valence information of sounds. ...
This work explores the use of constant-Q transform based modulation spectral features (CQT-MSF) for speech emotion recognition (SER). The human perception and analysis of sound comprise of two important cognitive parts: early auditory analysis and cortex-based processing. The early auditory analysis considers spectrogram-based representation whereas cortex-based analysis includes extraction of temporal modulations from the spectrogram. This temporal modulation representation of spectrogram is called modulation spectral feature (MSF). As the constant-Q transform (CQT) provides higher resolution at emotion salient low-frequency regions of speech, we find that CQT-based spectrogram, together with its temporal modulations, provides a representation enriched with emotion-specific information. We argue that CQT-MSF when used with a 2-dimensional convolutional network can provide a time-shift invariant and deformation insensitive representation for SER. Our results show that CQT-MSF outperforms standard mel-scale based spectrogram and its modulation features on two popular SER databases, Berlin EmoDB and RAVDESS. We also show that our proposed feature outperforms the shift and deformation invariant scattering transform coefficients, hence, showing the importance of joint hand-crafted and self-learned feature extraction instead of reliance on complete hand-crafted features. Finally, we perform Grad-CAM analysis to visually inspect the contribution of constant-Q modulation features over SER.
... While sounds inform us about events, it is also common for sounds to trigger emotional or physiological responses (Keil et al., 2007). Some sounds, such as a favorite piece of music, can evoke joy or pleasant chills (Blood and Zatorre, 2001; Barratt and Davis, 2015), while other sounds, such as crying, can evoke discomfort (Zald and Pardo, 2002;Anders et al., 2008;Grewe et al., 2011;Kumar et al., 2012;Ro et al., 2013). However, for a subset of people, certain common sounds elicit irritation, rage, or even panic (Edelstein et al., 2013;Rouw and Erfanian, 2018). ...
This study examines the role of source identification in the emotional response to everyday sounds. Although it is widely acknowledged that sound identification modulates the unpleasantness of sounds, this assumption is based on sparse evidence on a select few sounds. We gathered more robust evidence by having listeners judge the causal properties of sounds, such as actions, materials, and causal agents. Participants also identified and rated the pleasantness of the sounds. We included sounds from a variety of emotional categories, such as Neutral, Misophonic, Unpleasant, and Pleasant. The Misophonic category consists of everyday sounds that are uniquely distressing to a subset of listeners who suffer from Misophonia. Sounds from different emotional categories were paired together based on similar causal properties. This enabled us to test the prediction that a sound’s pleasantness should increase or decrease if it is misheard as being in a more or less pleasant emotional category, respectively. Furthermore, we were able to induce more misidentifications by imposing spectral degradation in the form of envelope vocoding. Several instances of misidentification were obtained, all of which showed pleasantness changes that agreed with our predictions.
... Aversive sound was administered using MRI-compatible headphones. We used the sound of a knife scraping on a bottle (sound file retrieved from YouTube), which is a reliable aversive auditory stimulus 77,78 . Four stimulus intensity levels were delivered at 2,000 Hz for 6 s each (level 1: level 4 minus 8 dB; level 2: level 4 minus 4 dB; level 3: level 4 minus 1 dB; level 4: original YouTube sound file). ...
The brain contains both generalized and stimulus-type-specific representations of aversive events, but models of how these are integrated and related to subjective experience are lacking. We combined functional magnetic resonance imaging with predictive modeling to identify representations of generalized (common) and stimulus-type-specific negative affect across mechanical pain, thermal pain, aversive sounds and aversive images of four intensity levels each. This allowed us to examine how generalized and stimulus-specific representations jointly contribute to aversive experience. Stimulus-type-specific negative affect was largely encoded in early sensory pathways, whereas generalized negative affect was encoded in a distributed set of midline, forebrain, insular and somatosensory regions. All models specifically predicted negative affect rather than general salience or arousal and accurately predicted negative affect in independent samples, demonstrating robustness and generalizability. Common and stimulus-type-specific models were jointly important for predicting subjective experience. Together, these findings offer an integrated account of how negative affect is constructed in the brain and provide predictive neuromarkers for future studies. Using multiple types of negative affect stimuli, functional magnetic resonance imaging and predictive modeling, Čeko et al. show that the brain integrates generalized and stimulus-type-specific representations of aversive events to jointly predict subjective experience.
... Sounds can reduce stress (Davis and Thaut, 1989), support learning and memory formation (Hallam et al., 2002), improve mood (Chanda and Levitin, 2013), and increase motivation (Salimpoor et al., 2015). Sounds can also do the opposite and create aversive experiences (Schreiber and Kahneman, 2000;Zald and Pardo, 2002;Kumar et al., 2012). One of the most significant effects of sounds is to impact focus. ...
The goal of this study was to investigate the effect of audio listened to through headphones on subjectively reported human focus levels, and to identify through objective measures the properties that contribute most to increasing and decreasing focus in people within their regular, everyday environment. Participants ( N = 62, 18–65 years) performed various tasks on a tablet computer while listening to either no audio (silence), popular audio playlists designed to increase focus (pre-recorded music arranged in a particular sequence of songs), or engineered soundscapes that were personalized to individual listeners (digital audio composed in real-time based on input parameters such as heart rate, time of day, location, etc.). Audio stimuli were delivered to participants through headphones while their brain signals were simultaneously recorded by a portable electroencephalography headband. Participants completed four 1-h long sessions at home during which different audio played continuously in the background. Using brain-computer interface technology for brain decoding and based on an individual’s self-report of their focus, we obtained individual focus levels over time and used this data to analyze the effects of various properties of the sounds contained in the audio content. We found that while participants were working, personalized soundscapes increased their focus significantly above silence ( p = 0.008), while music playlists did not have a significant effect. For the young adult demographic (18–36 years), all audio tested was significantly better than silence at producing focus ( p = 0.001–0.009). Personalized soundscapes increased focus the most relative to silence, but playlists of pre-recorded songs also increased focus significantly during specific time intervals. Ultimately we found it is possible to accurately predict human focus levels a priori based on physical properties of audio content. We then applied this finding to compare between music genres and revealed that classical music, engineered soundscapes, and natural sounds were the best genres for increasing focus, while pop and hip-hop were the worst. These insights can enable human and artificial intelligence composers to produce increases or decreases in listener focus with high temporal (millisecond) precision. Future research will include real-time adaptation of audio for other functional objectives beyond affecting focus, such as affecting listener enjoyment, drowsiness, stress and memory.
... The auditory cortex plays a key role in affective processing of sound. [7][8][9] The superior temporal cortex responds to sounds that elicit an automatic emotional response. 10,11 The neural network engaged in affective processing of sound extends to other brain areas as well as the auditory cortex. ...
... [12][13][14] The insula shows activity in response to all types of affective sounds, including human verbalizations, [15][16][17] and the amygdala is responsive to the sounds with negative valence. 9,18 Interindividual differences exist in affective and neurobiological sensitivity to sleep-related sounds. Some people appraise the sound of a ticking clock to be sufficiently bothersome to prevent sleep. ...
Purpose
Sounds play important roles in promoting and disrupting sleep. How our brain processes sleep-related sounds and individual differences in processing sleep-related sounds must be determined to understand the role of sound in sleep. We investigated neural responses to sleep-related sounds and their associations with cognitive appraisals of sleep.
Participants and Methods
Forty-four healthy adults heard sleep-related and neutral sounds during functional magnetic resonance imaging using a 3T scanner. They also completed the Dysfunctional Beliefs and Attitudes about Sleep (DBAS) questionnaire, which was used to assess cognitive appraisals of sleep. We conducted a voxel-wise whole-brain analysis to compare brain activation in response to sleep-related and neutral sounds. We also examined the association between the DBAS score and brain activity in response to sleep-related sounds (vs neutral sounds) using region of interest (ROI) and whole-brain correlation analyses. The ROIs included the anterior cingulate cortex (ACC), anterior insula (AI), and amygdala.
Results
The whole-brain analysis revealed increased activation in the temporal regions and decreased activation in the ACC in response to sleep-related sounds compared to neutral sounds. The ROI and whole-brain correlation analyses showed that higher DBAS scores, indicating a negative appraisal of sleep, were significantly correlated with increased activation of the ACC, right medial prefrontal cortex, and brainstem in response to sleep-related sounds.
Conclusion
These results indicate that the temporal cortex and ACC, which are implicated in affective sound processing, may play important roles in the processing of sleep-related sounds. The positive association between the neural responses to sleep-related sounds and DBAS scores suggest that negative and dysfunctional appraisals of sleep may be an important factor in individual differences in the processing of sleep-related sounds.
