Deficits in congenital amusia: pitch, music, speech, and beyond.

Congenital amusia is a neurodevelopmental disorder characterized by deficits of music perception and production, which are related to altered pitch processing. The present study used a wide variety of tasks to test potential patterns of processing impairment in individuals with congenital amusia (N=18) in comparison to matched controls (N=19), notably classical pitch processing tests (i.e., pitch change detection, pitch direction of change identification, and pitch short-term memory tasks) together with tasks assessing other aspects of pitch-related auditory cognition, such as emotion recognition in speech, sound segregation in tone sequences, and speech-in-noise perception. Additional behavioral measures were also collected, including text reading/copying tests, visual control tasks, and a subjective assessment of hearing abilities. As expected, amusics' performance was impaired for the three pitch-specific tasks compared to controls. This deficit of pitch perception had a self-perceived impact on amusics' quality of hearing. Moreover, participants with amusia were impaired in emotion recognition in vowels compared to controls, but no group difference was observed for emotion recognition in sentences, replicating previous data. Despite pitch processing deficits, participants with amusia did not differ from controls in sound segregation and speech-in-noise perception. Text reading and visual control tests did not reveal any impairments in participants with amusia compared to controls. However, the copying test revealed more numerous eye-movements and a smaller memory span. These results allow us to refine the pattern of pitch processing and memory deficits in congenital amusia, thus contributing further to understand pitch-related auditory cognition. Together with previous reports suggesting a comorbidity between congenital amusia and dyslexia, the findings call for further investigation of language-related abilities in this disorder even in the absence of neurodevelopmental language disorder diagnosis.

Identification of the needs of individuals affected by COVID-19.

BACKGROUND: The optimal management of COVID-19 symptoms and their sequelae remains an important area of clinical research. Policy makers have little scientific data regarding the effects on the daily life of affected individuals and the identification of their needs. Such data are needed to inform effective care policy. METHODS: We studied 639 people with COVID-19 resident in France via an online questionnaire. They reported their symptoms, effects on daily life, and resulting needs, with particular focus on olfaction. RESULTS: The results indicate that a majority of participants viewed their symptoms as disabling, with symptoms affecting their physical and mental health, social and professional lives. 60% of the individuals reported having unmet medical, psychological and socio-professional support needs. Finally, affected individuals were concerned about the risk and invasiveness of possible treatments as shown by a preference for non-invasive intervention over surgery to cure anosmia. CONCLUSIONS: It is important that policy makers take these needs into consideration in order to assist affected individuals to regain a normal quality of life.

The impact of COVID-19 has been substantial, both on individuals' health and on society. Information is needed to understand the biological mechanisms underlying the illness and to provide appropriate support for people affected. This study uses data from an online questionnaire of adults diagnosed with COVID-19 to characterize symptoms, understand their impact on peoples' everyday lives, and determine the support that people need. Our over-arching analysis of symptoms experienced reveals that heart- and skin-related symptoms are linked to chronic illness, and symptoms related to the sense of smell may have a different underlying disease mechanism. Most respondents had a mild initial illness, but their symptoms were long-lasting and had a severe impact. Our findings show that sufferers need different kinds of support in order to regain a normal quality of life.

Enhancing reading accuracy through visual search training using symbols.

Children with reading disorders present with inaccurate and/or delayed printed word identification. Regarding visual-attentional processing, printed words are letter strings, and each letter is a symbol made of separable features. Simultaneous processing of separable features has been evidenced to be specifically impaired in visual search tasks using symbols in poor readers as well as in a patient with superior parietal lobules (SPL) lesion. Additionally, activation in the SPL has been shown to be abnormally low in dyslexic readers displaying a reduced span of letter strings processing. This deficit has been assumed to impair visual-attentional sampling of printed words. An experiment conducted with 21 dyslexic children tested the hypothesis that a training program based on visual symbol search may stimulate the SPL, leading to a potential benefit transferred to reading performance. We designed the VisioCogLetters serious game and introduced it at random for one month (10 min every day) between four monthly reading sessions. No training was provided between the other (control) reading sessions. Reading accuracy increased without any speed-accuracy trade-off specifically in the session after training. Moreover, the percentage of improvement correlated with the individual time spent at home on training. These results show that improved visual search skills on symbols can translate into enhanced reading performance, and pave a new avenue for future rehabilitation tools.

Recovery From COVID-19-Related Olfactory Disorders and Quality of Life: Insights From an Observational Online Study.

Although olfactory disorders (OD) are among the most significant symptoms of COVID-19, recovery time from COVID-19-related OD and their consequences on the quality of life remain poorly documented. We investigated the characteristics and behavioral consequences of COVID-19-related OD using a large-scale study involving 3111 French respondents (78% women) to an online questionnaire over a period of 9 months covering different epidemic waves (from 8 April 2020 to 13 January 2021). In the patients who subjectively recovered from COVID-19-related OD (N = 609), recovery occurred on average after 16 days and most of the time within 1 month ("normal" recovery range); 49 subjectively recovered in 1-2.5 months, and several cases took up to 6.5 months. Among the patients with ongoing OD (N = 2502), 974 were outside the "normal" recovery range (persistent OD) and reported OD for 1-10 months. Developing a persistent OD was more likely with increasing age and in women and was more often associated with parosmia and phantosmia. The deleterious impact of COVID-19-related OD on the quality of life was significantly aggravated by OD duration and was more pronounced in women. Because persistent OD is not infrequent after COVID-19, has deleterious consequences on the quality of life, and receives few solutions from the health practitioners, it would be beneficial to implement screening and treatment programs to minimize the long-term behavioral consequences of COVID-19-related OD.

Rapid Assessment of Non-Verbal Auditory Perception in Normal-Hearing Participants and Cochlear Implant Users.

In the case of hearing loss, cochlear implants (CI) allow for the restoration of hearing. Despite the advantages of CIs for speech perception, CI users still complain about their poor perception of their auditory environment. Aiming to assess non-verbal auditory perception in CI users, we developed five listening tests. These tests measure pitch change detection, pitch direction identification, pitch short-term memory, auditory stream segregation, and emotional prosody recognition, along with perceived intensity ratings. In order to test the potential benefit of visual cues for pitch processing, the three pitch tests included half of the trials with visual indications to perform the task. We tested 10 normal-hearing (NH) participants with material being presented as original and vocoded sounds, and 10 post-lingually deaf CI users. With the vocoded sounds, the NH participants had reduced scores for the detection of small pitch differences, and reduced emotion recognition and streaming abilities compared to the original sounds. Similarly, the CI users had deficits for small differences in the pitch change detection task and emotion recognition, as well as a decreased streaming capacity. Overall, this assessment allows for the rapid detection of specific patterns of non-verbal auditory perception deficits. The current findings also open new perspectives about how to enhance pitch perception capacities using visual cues.

BLAST: A short computerized test to measure the ability to stay on task. Normative behavioral data and detailed cortical dynamics.

This article provides an exhaustive description of a new short computerized test to assess on a second-to-second basis the ability of individuals to «â€¯stay on task ¼, that is, to apply selectively and repeatedly task-relevant cognitive processes. The task (Bron/Lyon Attention Stability Test, or BLAST) lasts around 1 min, and measures repeatedly the time to find a target letter in a two-by-two letter array, with an update of all letters every new trial across thirty trials. Several innovative psychometric measures of attention stability are proposed based on the instantaneous fluctuations of reaction times throughout the task, and normative data stratified over a wide range of age are provided by a large (>6000) dataset of participants aged 8 to 70. We also detail the large-scale brain dynamics supporting the task from an in-depth study of 32 participants with direct electrophysiological cortical recordings (intracranial EEG) to prove that BLAST involves critically large-scale executive attention networks, with a marked activation of the dorsal attention network and a deactivation of the default-mode network. Accordingly, we show that BLAST performance correlates with scores established by ADHD-questionnaires.

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O'Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew's coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.

Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis.

A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

BACKGROUND: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. NEW METHOD: Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. RESULTS: The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. COMPARISON WITH EXISTING METHODS: The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. CONCLUSION: The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection.

Scalp current density mapping in the analysis of mismatch negativity paradigms.

MMN oddball paradigms are frequently used to assess auditory (dys)functions in clinical populations, or the influence of various factors (such as drugs and alcohol) on auditory processing. A widely used procedure is to compare the MMN responses between two groups of subjects (e.g. patients vs controls), or between experimental conditions in the same group. To correctly interpret these comparisons, it is important to take into account the multiple brain generators that produce the MMN response. To disentangle the different components of the MMN, we describe the advantages of scalp current density (SCD)-or surface Laplacian-computation for ERP analysis. We provide a short conceptual and mathematical description of SCDs, describe their properties, and illustrate with examples from published studies how they can benefit MMN analysis. We conclude with practical tips on how to correctly use and interpret SCDs in this context.

Impaired pitch perception and memory in congenital amusia: the deficit starts in the auditory cortex.

Congenital amusia is a lifelong disorder of music perception and production. The present study investigated the cerebral bases of impaired pitch perception and memory in congenital amusia using behavioural measures, magnetoencephalography and voxel-based morphometry. Congenital amusics and matched control subjects performed two melodic tasks (a melodic contour task and an easier transposition task); they had to indicate whether sequences of six tones (presented in pairs) were the same or different. Behavioural data indicated that in comparison with control participants, amusics' short-term memory was impaired for the melodic contour task, but not for the transposition task. The major finding was that pitch processing and short-term memory deficits can be traced down to amusics' early brain responses during encoding of the melodic information. Temporal and frontal generators of the N100m evoked by each note of the melody were abnormally recruited in the amusic brain. Dynamic causal modelling of the N100m further revealed decreased intrinsic connectivity in both auditory cortices, increased lateral connectivity between auditory cortices as well as a decreased right fronto-temporal backward connectivity in amusics relative to control subjects. Abnormal functioning of this fronto-temporal network was also shown during the retention interval and the retrieval of melodic information. In particular, induced gamma oscillations in right frontal areas were decreased in amusics during the retention interval. Using voxel-based morphometry, we confirmed morphological brain anomalies in terms of white and grey matter concentration in the right inferior frontal gyrus and the right superior temporal gyrus in the amusic brain. The convergence between functional and structural brain differences strengthens the hypothesis of abnormalities in the fronto-temporal pathway of the amusic brain. Our data provide first evidence of altered functioning of the auditory cortices during pitch perception and memory in congenital amusia. They further support the hypothesis that in neurodevelopmental disorders impacting high-level functions (here musical abilities), abnormalities in cerebral processing can be observed in early brain responses.

ELAN: a software package for analysis and visualization of MEG, EEG, and LFP signals.

The recent surge in computational power has led to extensive methodological developments and advanced signal processing techniques that play a pivotal role in neuroscience. In particular, the field of brain signal analysis has witnessed a strong trend towards multidimensional analysis of large data sets, for example, single-trial time-frequency analysis of high spatiotemporal resolution recordings. Here, we describe the freely available ELAN software package which provides a wide range of signal analysis tools for electrophysiological data including scalp electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG, and local field potentials (LFPs). The ELAN toolbox is based on 25 years of methodological developments at the Brain Dynamics and Cognition Laboratory in Lyon and was used in many papers including the very first studies of time-frequency analysis of EEG data exploring evoked and induced oscillatory activities in humans. This paper provides an overview of the concepts and functionalities of ELAN, highlights its specificities, and describes its complementarity and interoperability with other toolboxes.

Effects of selective attention on the electrophysiological representation of concurrent sounds in the human auditory cortex.

In noisy environments, we use auditory selective attention to actively ignore distracting sounds and select relevant information, as during a cocktail party to follow one particular conversation. The present electrophysiological study aims at deciphering the spatiotemporal organization of the effect of selective attention on the representation of concurrent sounds in the human auditory cortex. Sound onset asynchrony was manipulated to induce the segregation of two concurrent auditory streams. Each stream consisted of amplitude modulated tones at different carrier and modulation frequencies. Electrophysiological recordings were performed in epileptic patients with pharmacologically resistant partial epilepsy, implanted with depth electrodes in the temporal cortex. Patients were presented with the stimuli while they either performed an auditory distracting task or actively selected one of the two concurrent streams. Selective attention was found to affect steady-state responses in the primary auditory cortex, and transient and sustained evoked responses in secondary auditory areas. The results provide new insights on the neural mechanisms of auditory selective attention: stream selection during sound rivalry would be facilitated not only by enhancing the neural representation of relevant sounds, but also by reducing the representation of irrelevant information in the auditory cortex. Finally, they suggest a specialization of the left hemisphere in the attentional selection of fine-grained acoustic information.

Neural substrate of concurrent sound perception: direct electrophysiological recordings from human auditory cortex.

In everyday life, consciously or not, we are constantly disentangling the multiple auditory sources contributing to our acoustical environment. To better understand the neural mechanisms involved in concurrent sound processing, we manipulated sound onset asynchrony to induce the segregation or grouping of two concurrent sounds. Each sound consisted of amplitude-modulated tones at different carrier and modulation frequencies, allowing a cortical tagging of each sound. Electrophysiological recordings were carried out in epileptic patients with pharmacologically resistant partial epilepsy, implanted with depth electrodes in the temporal cortex. Patients were presented with the stimuli while they performed an auditory distracting task. We found that transient and steady-state evoked responses, and induced gamma oscillatory activities were enhanced in the case of onset synchrony. These effects were mainly located in the Heschl's gyrus for steady-state responses whereas they were found in the lateral superior temporal gyrus for evoked transient responses and induced gamma oscillations. They can be related to distinct neural mechanisms such as frequency selectivity and habituation. These results in the auditory cortex provide an anatomically refined description of the neurophysiological components which might be involved in the perception of concurrent sounds.