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Understanding speech in noise is particularly difficult for individuals occupationally exposed to noise due to a mix of noise-induced auditory lesions and the energetic masking of speech signals. For years, the monitoring of conventional audiometric thresholds has been the usual method to check and preserve auditory function. Recently, suprathreshold deficits, notably, difficulties in understanding speech in noise, has pointed out the need for new monitoring tools. The present study aims to identify the most important variables that predict speech in noise understanding in order to suggest a new method of hearing status monitoring. Physiological (distortion products of otoacoustic emissions, electrocochleography) and behavioral (amplitude and frequency modulation detection thresholds, conventional and extended high-frequency audiometric thresholds) variables were collected in a population of individuals presenting a relatively homogeneous occupational noise exposure. Those variables were used as predictors in a statistical model (random forest) to predict the scores of three different speech-in-noise tests and a self-report of speech-in-noise ability. The extended high-frequency threshold appears to be the best predictor and therefore an interesting candidate for a new way of monitoring noise-exposed professionals.

Hearing loss is a major public health problem. In 2050, it could affect 2.5 billion people. It has therefore become necessary to prevent and diagnose them as early and as widely as possible. However, the costs of clinical equipment dedicated to the functional exploration of hearing remain high and hamper their distribution, while the technologies used are relatively basic. For example, the gold-standard pure-tone audiometry (PTA) essentially consists of emitting pure sounds. In addition, clinical audiometers are generally limited to PTA or few audiological tests, while hearing loss induce multiple functional deficits. Here, we present the Aupiometer, a low-cost audiometer implemented on a modular open-source system based on Raspberry Pi, and which integrates the entire technical framework necessary to carry out audiological measurements. Several hearing tests are already implemented (e.g. PTA, speech audiometry, questionnaires), while the clinical validity of the Aupiometer was verified on a panel of participants (N = 16) for an automated test of standard and extended high-frequency PTA, from 0.125 to 16 kHz, in comparison with a clinical audiometer. For this comparison between the two devices and over this wide frequency range, the difference is evaluated as less than ±10 dB for a 90% confidence interval, of the same order of magnitude as on test-retest differences on a single device. The interest of this device also extends to academic research as it should encourage the prototyping of innovative hearing tests by the community, in order to better understand the diversity of hearing problems in the population.

The human brain networks responsible for selectively listening to a voice amid other talkers remain to be clarified. The present study aimed to investigate relationships between cortical activity and performance in a speech-in-speech task, before (Experiment I) and after training-induced improvements (Experiment II). In Experiment I, 74 participants performed a speech-in-speech task while their cortical activity was measured using a functional near infrared spectroscopy (fNIRS) device. One target talker and one masker talker were simultaneously presented at three different target-to-masker ratios (TMRs): adverse, intermediate and favorable. Behavioral results show that performance may increase monotonically with TMR in some participants and failed to decrease, or even improved, in the adverse-TMR condition for others. On the neural level, an extensive brain network including the frontal (left prefrontal cortex, right dorsolateral prefrontal cortex and bilateral inferior frontal gyri) and temporal (bilateral auditory cortex) regions was more solicited by the intermediate condition than the two others. Additionally, bilateral frontal gyri and left auditory cortex activities were found to be positively correlated with behavioral performance in the adverse-TMR condition. In Experiment II, 27 participants, whose performance was the poorest in the adverse-TMR condition of Experiment I, were trained to improve performance in that condition. Results show significant performance improvements along with decreased activity in bilateral inferior frontal gyri, the right dorsolateral prefrontal cortex, the left inferior parietal cortex and the right auditory cortex in the adverse-TMR condition after training. Arguably, lower neural activity reflects higher efficiency in processing masker inhibition after speech-in-speech training. As speech-in-noise tasks also imply frontal and temporal regions, we suggest that regardless of the type of masking (speech or noise) the complexity of the task will prompt the implication of a similar brain network. Furthermore, the initial significant cognitive recruitment will be reduced following a training leading to an economy of cognitive resources.

The human brain networks responsible for selectively listening to a voice amid other talkers remain to be clarified. The present study aimed to investigate relationships between cortical activity and performance in a speech-in-speech task, before (Experiment I) and after training-induced improvements (Experiment II). In Experiment I, 74 participants performed a speech-in-speech task while their cortical activity was measured using a functional near infrared spectroscopy (fNIRS) device. One target talker and one masker talker were simultaneously presented at three different target-to-masker ratios (TMRs): adverse, intermediate and favorable. Behavioral results show that performance may increase monotonically with TMR in some participants and failed to decrease, or even improved, in the adverse-TMR condition for others. On the neural level, an extensive brain network including the frontal (left prefrontal cortex, right dorsolateral prefrontal cortex and bilateral inferior frontal gyri) and temporal (bilateral auditory cortex) regions was more solicited by the intermediate condition than the two others. Additionally, bilateral frontal gyri and left auditory cortex activities were found to be positively correlated with behavioral performance in the adverse-TMR condition. In Experiment II, 27 participants, whose performance was the poorest in the adverse-TMR condition of Experiment I, were trained to improve performance in that condition. Results show significant performance improvements along with decreased activity in bilateral inferior frontal gyri, the right dorsolateral prefrontal cortex, the left inferior parietal cortex and the right auditory cortex in the adverse-TMR condition after training. Arguably, lower neural activity reflects higher efficiency in processing masker inhibition after speech-in-speech training. As speech-in-noise tasks also imply frontal and temporal regions, we suggest that regardless of the type of masking (speech or noise) the complexity of the task will prompt the implication of a similar brain network. Furthermore, the initial significant cognitive recruitment will be reduced following a training leading to an economy of cognitive resources.

This review intends to provide an overview of the current knowledge on neurologic sequelae of COVID-19 and their possible etiology, and, based on available data, proposes possible improvements in current medical care procedures. We conducted a thorough review of the scientific literature on neurologic manifestations of COVID-19, the neuroinvasive propensity of known coronaviruses (CoV) and their possible effects on brain structural and functional integrity. It appears that around one third of COVID-19 patients admitted to intensive care units (ICU) for respiratory difficulties exhibit neurologic symptoms. This may be due to progressive brain damage and dysfunction triggered by severe hypoxia and hypoxemia, heightened inflammation and SARS-CoV-2 dissemination into brain parenchyma, as suggested by current reports and analyses of previous CoV outbreaks. Viral invasion of the brain may particularly target and alter brainstem and thalamic functions and, consequently, result in sensorimotor dysfunctions and psychiatric disorders. Moreover, data collected from other structurally homologous CoV suggest that SARS-CoV-2 infection may lead to brain cell degeneration and demyelination similar to multiple sclerosis (MS). Hence, current evidence warrants further evaluation and long-term follow-up of possible neurologic sequelae in COVID-19 patients. It may be particularly relevant to evaluate brainstem integrity in recovered patients, as it is suspected that this cerebral area may particularly be dysfunctional following SARS-CoV-2 infection. Because CoV infection can potentially lead to chronic neuroinflammation and progressive demyelination, neuroimaging features and signs of MS may also be evaluated in the long term in recovered COVID-19 patients.