Childhood sound disturbance and sleep problems in Alpine valleys with high levels of traffic exposures and greenspace.

Sound disturbance and sleep problems are regarded as the most common adverse effects of environmental noise but evidence of the role of air pollution and greenspace is scant. This is especially true for children who find themselves in a sensitive developmental period and experience their environment differently than adults. This study examined the joint effects of traffic exposures and residential greenspace on child sound disturbance and sleep problems via perceptions of neighborhood quality. We used cross-sectional data for 1251 schoolchildren (8-12 years) in the Tyrol region of Austria/Italy. Questionnaires provided information on sociodemographic and housing factors, perceived neighborhood quality, sound disturbance in different situations, and sleep problems. Modelled acoustic indicators included day-evening-night sound levels and the highest percentile level, and night-time sound level and a bespoke sleep disturbance index. Nitrogen dioxide served as a proxy for traffic-related air pollution. The normalized difference vegetation index was calculated as a measure of residential greenspace, and presence of a domestic garden was self-reported. Results showed that higher level of traffic-related exposures was positively associated with sound disturbance and sleep problems, while living in a greener area, especially in a house with a garden, was associated with lower sound disturbance and less sleep problems even in the presence of traffic. Traffic exposures contributed to more unfavorable, and greenspace to more positive perceptions in terms of traffic-related stressors, opportunities for outdoor recreation, and general satisfaction with the neighborhood. This indirect path seemed more important for greenspace than for traffic exposures. In conclusion, it seems advantageous to combine traffic-related mitigation with improving access to greenspace in interventions for supporting the acoustic comfort of children during day and nighttime. Even highly nature-dominated environments could still benefit from proximal green infrastructure, especially from domestic gardens.

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject's electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictions using saliency maps. We were able to successfully perform the aforementioned classification task for both the IS and CS scenarios, reaching classification accuracies up to 76%. In accordance with the literature, the model primarily used the parietal-occipital electrodes between 200 ms and 300 ms after the stimulus to make its prediction. The findings from this research contribute to the development of more effective P300-based brain-computer interfaces. Furthermore, they validate the EEG data collected in our experiment.

Learnable axonal delay in spiking neural networks improves spoken word recognition.

Spiking neural networks (SNNs), which are composed of biologically plausible spiking neurons, and combined with bio-physically realistic auditory periphery models, offer a means to explore and understand human auditory processing-especially in tasks where precise timing is essential. However, because of the inherent temporal complexity in spike sequences, the performance of SNNs has remained less competitive compared to artificial neural networks (ANNs). To tackle this challenge, a fundamental research topic is the configuration of spike-timing and the exploration of more intricate architectures. In this work, we demonstrate a learnable axonal delay combined with local skip-connections yields state-of-the-art performance on challenging benchmarks for spoken word recognition. Additionally, we introduce an auxiliary loss term to further enhance accuracy and stability. Experiments on the neuromorphic speech benchmark datasets, NTIDIDIGITS and SHD, show improvements in performance when incorporating our delay module in comparison to vanilla feedforward SNNs. Specifically, with the integration of our delay module, the performance on NTIDIDIGITS and SHD improves by 14% and 18%, respectively. When paired with local skip-connections and the auxiliary loss, our approach surpasses both recurrent and convolutional neural networks, yet uses 10 × fewer parameters for NTIDIDIGITS and 7 × fewer for SHD.

AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyancea).

Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most previous research focused on psycho-acoustic quantities or automatic sound recognition. Few attempts were made to include appraisal (e.g., in circumplex frameworks). This paper proposes an artificial intelligence (AI)-based dual-branch convolutional neural network with cross-attention-based fusion (DCNN-CaF) to analyze automatic soundscape characterization, including sound recognition and appraisal. Using the DeLTA dataset containing human-annotated sound source labels and perceived annoyance, the DCNN-CaF is proposed to perform sound source classification (SSC) and human-perceived annoyance rating prediction (ARP). Experimental findings indicate that (1) the proposed DCNN-CaF using loudness and Mel features outperforms the DCNN-CaF using only one of them. (2) The proposed DCNN-CaF with cross-attention fusion outperforms other typical AI-based models and soundscape-related traditional machine learning methods on the SSC and ARP tasks. (3) Correlation analysis reveals that the relationship between sound sources and annoyance is similar for humans and the proposed AI-based DCNN-CaF model. (4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on soundscape augmentation.

Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope.

PURPOSE: The advent of new medical devices allows patients with asthma to self-monitor at home, providing a more complete picture of their disease than occasional in-person clinic visits. This raises a pertinent question: which devices and parameters perform best in exacerbation detection? METHODS: A total of 149 patients with asthma (90 children, 59 adults) participated in a 6-month observational study. Participants (or parents) regularly (daily for the first 2 weeks and weekly for the next 5.5 months, with increased frequency during exacerbations) performed self-examinations using 3 devices: an artificial intelligence (AI)-aided home stethoscope (providing wheezes, rhonchi, and coarse and fine crackles intensity; respiratory and heart rate; and inspiration-to-expiration ratio), a peripheral capillary oxygen saturation (SpO2) meter, and a peak expiratory flow (PEF) meter and filled out a health state survey. The resulting 6,029 examinations were evaluated by physicians for the presence of exacerbations. For each registered parameter, a machine learning model was trained, and the area under the receiver operating characteristic curve (AUC) was calculated to assess its utility in exacerbation detection. RESULTS: The best single-parameter discriminators of exacerbations were wheezes intensity for young children (AUC 84% [95% CI, 82%-85%]), rhonchi intensity for older children (AUC 81% [95% CI, 79%-84%]), and survey answers for adults (AUC 92% [95% CI, 89%-95%]). The greatest efficacy (in terms of AUC) was observed for a combination of several parameters. CONCLUSIONS: The AI-aided home stethoscope provides reliable information on asthma exacerbations. The parameters provided are effective for children, especially those younger than 5 years of age. The introduction of this tool to the health care system might enhance asthma exacerbation detection substantially and make remote monitoring of patients easier.

Adopting a child perspective for exposome research on mental health and cognitive development - Conceptualisation and opportunities.

Mental disorders among children and adolescents pose a significant global challenge. The exposome framework covering the totality of internal, social and physical exposures over a lifetime provides opportunities to better understand the causes of and processes related to mental health, and cognitive functioning. The paper presents a conceptual framework on exposome, mental health, and cognitive development in children and adolescents, with potential mediating pathways, providing a possibility for interventions along the life course. The paper underscores the significance of adopting a child perspective to the exposome, acknowledging children's specific vulnerability, including differential exposures, susceptibility of effects and capacity to respond; their susceptibility during development and growth, highlighting neurodevelopmental processes from conception to young adulthood that are highly sensitive to external exposures. Further, critical periods when exposures may have significant effects on a child's development and future health are addressed. The paper stresses that children's behaviour, physiology, activity pattern and place for activities make them differently vulnerable to environmental pollutants, and calls for child-specific assessment methods, currently lacking within today's health frameworks. The importance of understanding the interplay between structure and agency is emphasized, where agency is guided by social structures and practices and vice-versa. An intersectional approach that acknowledges the interplay of social and physical exposures as well as a global and rural perspective on exposome is further pointed out. To advance the exposome field, interdisciplinary efforts that involve multiple scientific disciplines are crucial. By adopting a child perspective and incorporating an exposome approach, we can gain a comprehensive understanding of how exposures impact children's mental health and cognitive development leading to better outcomes.

Time course of EEG complexity reflects attentional engagement during listening to speech in noise.

Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions.

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot. We used data collected with physiological sensors during the playing of a risk-taking game (the Balloon Analogue Risk Task, or BART). The results of a mixed-effects model were used as a baseline to predict risk-taking propensity from physiological measures, and these results were further improved through the use of two machine learning techniques-support vector regression (SVR) and multi-input convolutional multihead attention (MCMA)-to achieve low-latency risk-taking behaviour prediction during human-robot tactile interaction. The performance of the models was evaluated based on mean absolute error (MAE), root mean squared error (RMSE), and R squared score (R2), which obtained the optimal result with MCMA yielding an MAE of 3.17, an RMSE of 4.38, and an R2 of 0.93 compared with the baseline of 10.97 MAE, 14.73 RMSE, and 0.30 R2. The results of this study offer new insights into the interplay between physiological data and the intensity of risk-taking behaviour in predicting human risk-taking behaviour during human-robot tactile interactions. This work illustrates that physiological activation and the intensity of tactile interaction play a prominent role in risk processing during human-robot tactile interaction and demonstrates that it is feasible to use human physiological data and behavioural data to predict risk-taking behaviour in human-robot tactile interaction.

Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique-an artificial neural network in this work-is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2-3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.

Protective effect of restorative possibilities on cognitive function and mental health in children and adolescents: A scoping review including the role of physical activity.

Background The exposome approach can be a powerful tool for understanding the intertwining of social, physical, and internal influences that shape mental health and cognitive development throughout childhood. To distil conceptual models for subsequent analyses, the EU-funded project Early Environmental quality and Life-course mental health effects (Equal-Life) has conducted literature reviews on potential mediators linking the exposome to these outcomes. We report on a scoping review and a conceptual model of the role of restorative possibilities and physical activity. Methods Peer-reviewed studies published since the year 2000 in English, on the association between the exposome and mental health/cognition in children/adolescents, and quantitatively investigating restoration/restorative quality as a mediating variable were considered. Database searches were last updated in December 2022. We used an unstructured expert-driven approach to fill in gaps in the reviewed literature. Results Five records of three distinct studies were identified, indicating a scarcity of empirical evidence in this newly developing research area. Not only were these studies few in numbers, but also cross-sectional, lending only tentative support to the idea that perceived restorative quality of adolescent's living environment might mediate the association between greenspace and mental health. Physical activity emerged as a mediator leading to better psychological outcomes in restorative environments. We provide a critical discussion of potential caveats when investigating the restoration mechanism in children and propose a hierarchical model including restoration, physical activity, and relational dynamics between children and their environment, including social context, as well as restorative environments other than nature. Conclusions It is justified to further explore the role of restoration and physical activity as mediators in the association between early-life exposome and mental health/cognitive development. It is important to consider the child perspective and specific methodological caveats. Given the evolving conceptual definitions/operationalizations, Equal-Life will attempt to fill in a critical gap in the literature.

The Influence of Everyday Acoustic Environments on the Challenging Behavior in Dementia: A Participatory Observation Study in Nursing Homes.

Challenging behavior (CB) is a group of behaviors, reactions and symptoms due to dementia, which can be challenging for the caregivers. The study aims to research the influence of acoustics on CB in people with dementia (PwD). An ethnographic method was used to study the daily life of PwD in their nursing homes with a specific focus on how people react to everyday environmental sounds. Thirty-five residents were included in the sample based on purposeful, homogeneous group characteristics and sampling. Empirical data were collected using 24/7 participatory observations. The collected data were analyzed using a phenomenological-hermeneutical method: a naïve understanding, a structural analysis and a comprehensive understanding. The result shows that the onset of CB depends on whether the resident feels safe and is triggered by an excess or lack of stimuli. The excess or shortage of stimuli and whether and when it affects a person is personal. It depends on various factors, the person's state and the time of day, the nature of the stimuli, familiarity, or strangeness is also a determining factor for the onset and progression of CB. The results can form an essential basis for developing soundscapes to make the PwD feel safe and reduce CB.

With No Attention Specifically Directed to It, Rhythmic Sound Does Not Automatically Facilitate Visual Task Performance.

In a century where humans and machines-powered by artificial intelligence or not-increasingly work together, it is of interest to understand human processing of multi-sensory stimuli in relation to attention and working memory. This paper explores whether and when supporting visual information with rhythmic auditory stimuli can optimize multi-sensory information processing. In turn, this can make the interaction between humans or between machines and humans more engaging, rewarding and activating. For this purpose a novel working memory paradigm was developed where participants are presented with a series of five target digits randomly interchanged with five distractor digits. Their goal is to remember the target digits and recall them orally. Depending on the condition support is provided by audio and/or rhythm. It is expected that the sound will lead to a better performance. It is also expected that this effect of sound is different in case of rhythmic and non-rhythmic sound. Last but not least, some variability is expected across participants. To make correct conclusions, the data of the experiment was statistically analyzed in a classic way, but also predictive models were developed in order to predict outcomes based on a range of input variables related to the experiment and the participant. The effect of auditory support could be confirmed, but no difference was observed between rhythmic and non-rhythmic sounds. Overall performance was indeed affected by individual differences, such as visual dominance or perceived task difficulty. Surprisingly a music education did not significantly affect the performance and even tended toward a negative effect. To better understand the underlying processes of attention, also brain activation data, e.g., by means of electroencephalography (EEG), should be recorded. This approach can be subject to a future work.

Early environmental quality and life-course mental health effects: The Equal-Life project.

BACKGROUND: There is increasing evidence that a complex interplay of factors within environments in which children grows up, contributes to children's suboptimal mental health and cognitive development. The concept of the life-course exposome helps to study the impact of the physical and social environment, including social inequities, on cognitive development and mental health over time. METHODS: Equal-Life develops and tests combined exposures and their effects on children's mental health and cognitive development. Data from eight birth-cohorts and three school studies (N = 240.000) linked to exposure data, will provide insights and policy guidance into aspects of physical and social exposures hitherto untapped, at different scale levels and timeframes, while accounting for social inequities. Reasoning from the outcome point of view, relevant stakeholders participate in the formulation and validation of research questions, and in the formulation of environmental hazards. Exposure assessment combines GIS-based environmental indicators with omics approaches and new data sources, forming the early-life exposome. Statistical tools integrate data at different spatial and temporal granularity and combine exploratory machine learning models with hypothesis-driven causal modeling. CONCLUSIONS: Equal-Life contributes to the development and utilization of the exposome concept by (1) integrating the internal, physical and social exposomes, (2) studying a distinct set of life-course effects on a child's development and mental health (3) characterizing the child's environment at different developmental stages and in different activity spaces, (4) looking at supportive environments for child development, rather than merely pollutants, and (5) combining physical, social indicators with novel effect markers and using new data sources describing child activity patterns and environments.

Parkinson's disease affects the neural alpha oscillations associated with speech-in-noise processing.

Parkinson's disease (PD) has increasingly been associated with auditory dysfunction, including alterations regarding the control of auditory information processing. Although these alterations may interfere with the processing of speech in degraded listening conditions, behavioural studies have generally found preserved speech-in-noise recognition in PD. However, behavioural speech audiometry does not capture the neurophysiological mechanisms supporting speech-in-noise processing. Therefore, the aim of this study was to investigate the neural oscillatory mechanisms associated with speech-in-noise processing in PD. Twelve persons with PD and 12 age- and gender-matched healthy controls (HCs) were included in this study. Persons with PD were studied in the medication off condition. All subjects underwent an audiometric screening and performed a sentence-in-noise recognition task under simultaneous electroencephalography (EEG) recording. Behavioural speech recognition scores and self-reported ratings of effort, performance, and motivation were collected. Time-frequency analysis of EEG data revealed no significant difference between persons with PD and HCs regarding delta-theta (2-8 Hz) inter-trial phase coherence to noise and sentence onset. In contrast, significantly increased alpha (8-12 Hz) power was found in persons with PD compared with HCs during the sentence-in-noise recognition task. Behaviourally, persons with PD demonstrated significantly decreased speech recognition scores, whereas no significant differences were found regarding effort, performance, and motivation ratings. These results suggest that persons with PD allocate more cognitive resources to support speech-in-noise processing. The interpretation of this finding is discussed in the context of a top-down mediated compensation mechanism for inefficient filtering and degradation of auditory input in PD.

Evaluation of multi-feature auditory deviance detection in Parkinson's disease: a mismatch negativity study.

Behavioral studies on auditory deviance detection in patients with Parkinson's disease (PD) have reported contradictory results. The primary aim of this study was to investigate auditory deviance detection of multiple auditory features in patients with PD by means of objective and reliable electroencephalographic (EEG) measurements. Twelve patients with early-stage PD and twelve age- and gender-matched healthy controls (HCs) were included in this study. Patients with PD participated without their regular dopaminergic medication. All subjects underwent an audiometric screening and performed a passive multi-feature mismatch negativity (MMN) paradigm. Repeated-measures analysis of variance (ANOVA) demonstrated no significant differences between patients with PD and HCs regarding MMN mean amplitude and latency for frequency, duration and gap deviants. Nevertheless, a trend towards increased MMN mean amplitude and latency was found in response to intensity deviants in patients with PD compared to HCs. Increased intensity MMN amplitude may indicate that more neural resources are allocated to the processing of intensity deviances in patients with PD compared to HCs. The interpretation of this intensity-specific MMN alteration is further discussed in the context of a compensatory mechanism for auditory intensity processing and involuntary attention switching in PD.

The Effect of Parkinson's Disease on Otoacoustic Emissions and Efferent Suppression of Transient Evoked Otoacoustic Emissions.

Purpose Several studies have demonstrated increased auditory thresholds in patients with Parkinson's disease (PD) based on subjective tonal audiometry. However, the pathophysiological mechanisms underlying auditory dysfunction in PD remain elusive. The primary aim of this study was to investigate cochlear and olivocochlear function in PD using objective measurements and to assess the effect of dopaminergic medication on auditory function. Method Eighteen patients with PD and 18 gender- and age-matched healthy controls (HCs) were included. Patients with PD participated in medication on and off conditions. Linear mixed models were used to determine the effect of PD on tonal audiometry, transient evoked and distortion product otoacoustic emissions (OAEs), and efferent suppression (ES). Results Tonal audiometry revealed normal auditory thresholds in patients with PD for their age across all frequencies. OAE signal amplitudes demonstrated a significant interaction effect between group (PD vs. HC) and frequency, indicating decreased OAEs at low frequencies and increased OAEs at high frequencies in patients with PD. No significant differences were found between patients with PD and HCs regarding ES. In addition, no significant effect of medication status was found on auditory measurements in patients with PD. Conclusions Altered OAEs support the hypothesis of cochlear alterations in PD. No evidence was found for the involvement of the medial olivocochlear system. Altogether, OAEs may provide an objective early indicator of auditory alterations in PD and should complement subjective tonal audiometry when assessing and monitoring auditory function in PD.

Neurophysiological investigation of auditory intensity dependence in patients with Parkinson's disease.

There is accumulating evidence for auditory dysfunctions in patients with Parkinson's disease (PD). Moreover, a possible relationship has been suggested between altered auditory intensity processing and the hypophonic speech characteristics in PD. Nonetheless, further insight into the neurophysiological correlates of auditory intensity processing in patients with PD is needed primarily. In the present study, high-density EEG recordings were used to investigate intensity dependence of auditory evoked potentials (IDAEPs) in 14 patients with PD and 14 age- and gender-matched healthy control participants (HCs). Patients with PD were evaluated in both the on- and off-medication states. HCs were also evaluated twice. Significantly increased IDAEP of the N1/P2 was demonstrated in patients with PD evaluated in the on-medication state compared to HCs. Distinctive results were found for the N1 and P2 component. Regarding the N1 component, no differences in latency or amplitude were shown between patients with PD and HCs regardless of the medication state. In contrast, increased P2 amplitude was demonstrated in patients with PD evaluated in the on-medication state compared to the off-medication state and HCs. In addition to a dopaminergic deficiency, deficits in serotonergic neurotransmission in PD were shown based on increased IDAEP. Due to specific alterations of the N1-P2 complex, the current results suggest deficiencies in early-attentive inhibitory processing of auditory input in PD. This interpretation is consistent with the involvement of the basal ganglia and the role of dopaminergic and serotonergic neurotransmission in auditory gating.

EEG Correlates of Learning From Speech Presented in Environmental Noise.

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms.

Future Perspectives on the Relevance of Auditory Markers in Prodromal Parkinson's Disease.

Research on auditory processing in Parkinson's disease (PD) has recently made substantial progress. At present, evidence has been found for altered auditory processing in the clinical stage of PD. The auditory alterations in PD have been demonstrated with low-cost and non-invasive assessments that are already used in routine clinical practice. Since auditory alterations have been reported early in disease progression, it would be highly relevant to investigate whether auditory markers could be provided in the prodromal stage of PD. In addition, auditory alterations in early stage PD might be modulated by dopaminergic medication. Therefore, the aim of this review is (1) to summarize the literature on auditory processing in PD with a specific focus on the early disease stages, (2) to give future perspectives on which audiological and electrophysiological measurements could be useful in the prodromal stage of PD and (3) to assess the effect of dopaminergic medication on potential auditory markers in the prodromal stage of PD.

Cyclists' noise exposure in a Brazilian medium-sized city. / Exposição de ciclistas ao ruído em uma cidade média brasileira.

This study aimed to assess cyclists' exposure to noise in a medium-sized Brazilian city. Mobile sensors were used to conduct noise measurements in streets with and without dedicated cycling infrastructures. The method can be summarized in the following procedures: i) characterization of the study area; ii) data collection and validation; iii) calculation of exposure indicators; and iv) comparison and representation of the results on maps. Two strategies were adopted for the analysis, namely, spatial data aggregation and temporal data aggregation. Thus, measurements were initially organized in 1,200 nodes distributed along the paths. The results indicate that bicycle riders in São Carlos may be exposed, in some routes, to a high proportion of high noise-level segments. In the two routes selected for this study, the cyclist was exposed to noise levels above the adopted threshold (> 75dBA) in 33.2% and 18.9% of the nodes. Also, the possibility of simultaneously working with two related indicators has broadened the classification criteria of the route segments regarding noise exposure.

O objetivo deste estudo foi avaliar a exposição dos ciclistas ao ruído em uma cidade média brasileira. Um sensor móvel foi utilizado para as medições de ruído em ruas com e sem infraestruturas cicloviárias segregadas. O método proposto contempla as seguintes etapas: i) caracterização da área de estudo; ii) coleta e validação de dados; iii) cálculo dos indicadores de exposição; e iv) comparação e representação de resultados em mapas. Dois tipos de análises foram realizadas, por agregação espacial e por agregação temporal. Para isto, as medições foram inicialmente organizadas em 1.200 nós distribuídos nos percursos considerados. Os resultados apontam que os ciclistas que percorrem alguns trechos na cidade de São Carlos podem estar expostos a uma proporção considerável de ambientes com altos níveis de ruído. Nos dois trechos selecionados para este estudo, o ciclista esteve exposto à níveis acima do valor adotado (> 75dBA) em 33,2% e 18,9% dos nós. Além disso, observou-se que a possibilidade de trabalhar concomitantemente dois indicadores relacionados amplia os critérios de classificação dos trechos quanto à exposição do ruído.