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.

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.

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 « stay on task », 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.

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.

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.

Neurophysiological correlates of face gender processing in humans

Event-related potentials (ERPs) were recorded while subjects were involved in three gender-processing tasks based on human faces and on human hands. In one condition all stimuli were only of one gender, preventing any gender discrimination. In a second condition, faces (or hands) of men and women were intermixed but the gender was irrelevant for the subject's task; hence gender discrimination was assumed to be incidental. In the third condition, the task required explicit gender discrimination; gender processing was therefore assumed to be intentional. Gender processing had no effect on the occipito-temporal negative potential at approximately 170 ms after stimulation (N170 component of the ERP), suggesting that the neural mechanisms involved in the structural encoding of faces are different from those involved in the extraction of gender-related facial features. In contrast, incidental and intentional processing of face (but not hand) gender affected the ERPs between 145 and 185 ms from stimulus onset at more anterior scalp locations. This effect was interpreted as evidence for the direct visual processing of faces as described in Bruce and Young's model [Bruce, V. & Young, A. (1986) Br. J. Psychol., 77, 305-327]. Additional gender discrimination effects were observed for both faces and hands at mid-parietal sites around 45-85 ms latency, in the incidental task only. This difference was tentatively assumed to reflect an early mechanism of coarse visual categorization. Finally, intentional (but not incidental) gender processing affected the ERPs during a later epoch starting from approximately 200 ms and ending at approximately 250 ms for faces, and approximately 350 ms for hands. This later effect might be related to attention-based gender categorization or to a more general categorization activity.