Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery.

MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 - far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

Hydroxyzine Use and Mortality in Patients Hospitalized for COVID-19: A Multicenter Observational Study.

(1) Background: Based on its antiviral activity, anti-inflammatory properties, and functional inhibition effects on the acid sphingomyelinase/ceramide system (FIASMA), we sought to examine the potential usefulness of the H1 antihistamine hydroxyzine in patients hospitalized for COVID-19. (2) Methods: In a multicenter observational study, we included 15,103 adults hospitalized for COVID-19, of which 164 (1.1%) received hydroxyzine within the first 48 h of hospitalization, administered orally at a median daily dose of 25.0 mg (SD = 29.5). We compared mortality rates between patients who received hydroxyzine at hospital admission and those who did not, using a multivariable logistic regression model adjusting for patients' characteristics, medical conditions, and use of other medications. (3) Results: This analysis showed a significant association between hydroxyzine use and reduced mortality (AOR, 0.51; 95%CI, 0.29-0.88, p = 0.016). This association was similar in multiple sensitivity analyses. (4) Conclusions: In this retrospective observational multicenter study, the use of the FIASMA hydroxyzine was associated with reduced mortality in patients hospitalized for COVID-19. Double-blind placebo-controlled randomized clinical trials of hydroxyzine for COVID-19 are needed to confirm these results, as are studies to examine the potential usefulness of this medication for outpatients and as post-exposure prophylaxis for individuals at high risk for severe COVID-19.

External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals.

PURPOSE: The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19. METHODS: We used "COVID-19 Evidence Alerts" (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. RESULTS: We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome. CONCLUSION: Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients.

Observational Study of Chlorpromazine in Hospitalized Patients with COVID-19.

INTRODUCTION: Chlorpromazine has been suggested as being potentially useful in patients with coronavirus disease 2019 (COVID-19) on the grounds of its potential antiviral and anti-inflammatory effects. OBJECTIVE: The aim of this study was to examine the association between chlorpromazine use and mortality among adult patients hospitalized for COVID-19. METHODS: We conducted an observational, multicenter, retrospective study at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of first prescription of chlorpromazine during hospitalization for COVID-19. The primary endpoint was death. Among patients who had not been hospitalized in intensive care units (ICUs), we compared this endpoint between those who received chlorpromazine and those who did not, in time-to-event analyses adjusted for patient characteristics, clinical markers of disease severity, and other psychotropic medications. The primary analysis used a Cox regression model with inverse probability weighting. Multiple sensitivity analyses were performed. RESULTS: Of the 14,340 adult inpatients hospitalized outside ICUs for COVID-19, 55 patients (0.4%) received chlorpromazine. Over a mean follow-up of 14.3 days (standard deviation [SD] 18.2), death occurred in 13 patients (23.6%) who received chlorpromazine and 1289 patients (9.0%) who did not. In the primary analysis, there was no significant association between chlorpromazine use and mortality (hazard ratio [HR] 2.01, 95% confidence interval [CI] 0.75-5.40; p = 0.163). Sensitivity analyses included a Cox regression in a 1:5 ratio matched analytic sample that showed a similar result (HR 1.67, 95% CI 0.91-3.06; p = 0.100) and a multivariable Cox regression that indicated a significant positive association (HR 3.10, 95% CI 1.31-7.34; p = 0.010). CONCLUSION: Our results suggest that chlorpromazine prescribed at a mean daily dose of 70.8 mg (SD 65.3) was not associated with reduced mortality.

Dexamethasone use and mortality in hospitalized patients with coronavirus disease 2019: A multicentre retrospective observational study.

AIMS: To examine the association between dexamethasone use and mortality among patients hospitalized for COVID-19. METHODS: We examined the association between dexamethasone use and mortality at AP-HP Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was time to death. We compared this endpoint between patients who received dexamethasone and those who did not in time-to-event analyses adjusted for patient characteristics (such as age, sex and comorbidity) and clinical and biological markers of clinical severity of COVID-19, and stratified by the need for respiratory support, i.e. mechanical ventilation or oxygen. The primary analysis was a multivariable Cox regression model. RESULTS: Of 12 217 adult patients hospitalized with a positive COVID-19 reverse transcriptase-polymerase chain reaction test, 171 (1.4%) received dexamethasone orally or by intravenous perfusion during the visit. Among patients who required respiratory support, the end-point occurred in 10/63 (15.9%) patients who received dexamethasone and 298/1129 (26.4%) patients who did not. In this group, there was a significant association between dexamethasone use and reduced mortality in the primary analysis (hazard ratio, 0.46; 95% confidence interval 0.22-0.96, P = .039). Among patients who did not require respiratory support, there was no significant association between dexamethasone use and the endpoint. CONCLUSIONS: In this multicentre observational study, dexamethasone use administered either orally or by intravenous injection at a cumulative dose between 60 mg and 150 mg was associated with reduced mortality among patients with COVID-19 requiring respiratory support.

Observational study of haloperidol in hospitalized patients with COVID-19.

BACKGROUND: Haloperidol, a widely used antipsychotic, has been suggested as potentially useful for patients with COVID-19 on the grounds of its in-vitro antiviral effects against SARS-CoV-2, possibly through sigma-1 receptor antagonist effect. METHODS: We examined the associations of haloperidol use with intubation or death and time to discharge home among adult patients hospitalized for COVID-19 at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of hospital admission. The primary endpoint was a composite of intubation or death and the secondary endpoint was discharge home among survivors in time-to-event analyses. In the primary analyses, we compared these two outcomes between patients receiving and not receiving haloperidol using univariate Cox regression models in matched analytic samples based on patient characteristics and other psychotropic medications. Sensitivity analyses included propensity score analyses with inverse probability weighting and multivariable Cox regression models. RESULTS: Of 15,121 adult inpatients with a positive COVID-19 PT-PCR test, 39 patients (0.03%) received haloperidol within the first 48 hours of admission. Over a mean follow-up of 13.8 days (SD = 17.9), 2,024 patients (13.4%) had a primary end-point event and 10,179 patients (77.6%) were discharged home at the time of study end on May 1st. The primary endpoint occurred in 9 patients (23.1%) who received haloperidol and 2,015 patients (13.4%) who did not. The secondary endpoint of discharge home occurred in 16 patients (61.5%) who received haloperidol and 9,907 patients (85.8%) who did not. There were no significant associations between haloperidol use and the primary (HR, 0.80; 95% CI, 0.39 to 1.62, p = 0.531) and secondary (HR, 1.30; 95% CI, 0.74 to 2.28, p = 0.355) endpoints. Results were similar in multiple sensitivity analyses. CONCLUSION: Findings from this multicenter observational study suggest that haloperidol use prescribed at a mean dose of 4.5 mg per day (SD = 5.2) for a mean duration of 8.4 days (SD = 7.2) may not be associated with risk of intubation or death, or with time to discharge home, among adult patients hospitalized for COVID-19.

Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study.

A prior meta-analysis showed that antidepressant use in major depressive disorder was associated with reduced plasma levels of several pro-inflammatory mediators, which have been associated with severe COVID-19. Recent studies also suggest that several antidepressants may inhibit acid sphingomyelinase activity, which may prevent the infection of epithelial cells with SARS-CoV-2, and that the SSRI fluoxetine may exert in-vitro antiviral effects on SARS-CoV-2. We examined the potential usefulness of antidepressant use in patients hospitalized for COVID-19 in an observational multicenter retrospective cohort study conducted at AP-HP Greater Paris University hospitals. Of 7230 adults hospitalized for COVID-19, 345 patients (4.8%) received an antidepressant within 48 h of hospital admission. The primary endpoint was a composite of intubation or death. We compared this endpoint between patients who received antidepressants and those who did not in time-to-event analyses adjusted for patient characteristics, clinical and biological markers of disease severity, and other psychotropic medications. The primary analysis was a multivariable Cox model with inverse probability weighting. This analysis showed a significant association between antidepressant use and reduced risk of intubation or death (HR, 0.56; 95% CI, 0.43-0.73, p < 0.001). This association remained significant in multiple sensitivity analyses. Exploratory analyses suggest that this association was also significant for SSRI and non-SSRI antidepressants, and for fluoxetine, paroxetine, escitalopram, venlafaxine, and mirtazapine (all p < 0.05). These results suggest that antidepressant use could be associated with lower risk of death or intubation in patients hospitalized for COVID-19. Double-blind controlled randomized clinical trials of antidepressant medications for COVID-19 are needed.

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

How old are you? What about your body, and your brain? People are used to answering this question by counting the years since birth. However, biological age could also be measured by looking at the integrity of the DNA in cells or by measuring the levels of proteins in the blood. Whether one goes by chronological age or biological age, each is simply an indicator of general health ­ but people with the same chronological age may have different biological ages, and vice versa. There are different imaging techniques that can be used to study the brain. A method called MRI reveals the brain's structure and the different types of tissue present, like white and grey matter. Functional MRIs (fMRIs for short) measure activity across different brain regions, while electrophysiology records electrical signals sent between neurons. Distinct features measured by all three techniques ­ MRI, fMRI and electrophysiology ­ have been associated with aging. For example, differences between younger and older people have been observed in the proportion of grey to white matter, the communication between certain brain regions, and the intensity of neural activity. MRIs, with their anatomical detail, remain the go-to for predicting the biological age of the brain. Patterns of neuronal activity captured by electrophysiology also provide information about how well the brain is working. However, it remains unclear how electrophysiology could be combined with other brain imaging methods, like MRI and fMRI. Can data from these three techniques be combined to better predict brain age? Engemann et al. designed a computer algorithm stacking electrophysiology data on top of MRI and fMRI imaging to assess the benefit of this three-pronged approach compared to using MRI alone. Brain scans from healthy people between 17 and 90 years old were used to build the computer model. The experiments showed that combining all three methods predicted brain age better. The predictions also correlated with the cognitive fitness of individuals. People whose brains were predicted to be older than their years tended to complain about the quality of their sleep and scored worse on memory and speed-thinking tasks. Crucially, Engemann et al. tested how the algorithm would hold up if some data were missing. This can happen in clinical practice where some tests are required but not others. Positively, prediction was maintained even with incomplete data, meaning this could be a useful clinical tool for characterizing the brain.

Observation and Interview-based Diurnal Sleepiness Inventory for measurement of sleepiness in patients referred for narcolepsy or idiopathic hypersomnia.

STUDY OBJECTIVES: First, to determine whether the 3-item Observation and Interview-based Diurnal Sleepiness Inventory (ODSI) measures the degree of excessive daytime sleepiness in patients with suspected narcolepsy or idiopathic hypersomnia (IH). Second, to assess the correlation between the ODSI and the Epworth Sleepiness Scale (ESS) as well as objective polysomnographic measurements. Third, to test the accuracy of the ODSI to detect narcolepsy or IH (narcolepsy/IH) compared with the ESS. METHODS: A total of 181 patients complaining of excessive daytime sleepiness filled in the ESS and the ODSI and underwent measurements including actigraphy, full-night polysomnography, Multiple Sleep Latency Test, and 24-hour bedrest sleep recording. RESULTS: Narcolepsy or IH was diagnosed in 76 patients. The ODSI found excessive daytime sleepiness in 92.3% of all patients and in 98.7% of those diagnosed with narcolepsy/IH. In the whole population, the ODSI was significantly positively correlated with the ESS (R = .547; 95% confidence interval: .436, .642; P < .001) and weakly with 24-hour total sleep time on bedrest recording (R = .208; 95% confidence interval: .056, .350; P = .047) but not with the Multiple Sleep Latency Test. The ODSI offered a higher negative (92.9%) and positive (44.9%) predictive value to detect narcolepsy/IH than did the ESS (66.7% and 43.3%, respectively). In the IH group, the ODSI's third-item score (daily sleepiness duration) was significantly higher in patients with than without increased 24-hour total sleep time (P = .023). CONCLUSIONS: The ODSI is a brief, simple first-line questionnaire that explores both intensity and duration of daytime sleepiness and offers a high sensitivity to detect narcolepsy and IH.

Is loudness part of a sound recognition process?

Influence of loudness on sound recognition was investigated in an explicit memory experiment based on a conscious recollection-test phase-of previously encoded information-study phase. Three encoding conditions were compared: semantic (sounds were sorted in three different categories), sensory (sounds were rated in loudness), and control (participants were solely asked to listen to the sounds). Results revealed a significant study-to-test change effect: loudness change between the study and the test phases affects recognition. The effect was not specific to the encoding condition (semantic vs sensory) suggesting that loudness is an important hint for everyday sounds recognition.

Prediction of three articulatory categories in vocal sound imitations using models for auditory receptive fields.

Vocal sound imitations provide a new challenge for understanding the coupling between articulatory mechanisms and the resulting audio. In this study, the classification of three articulatory categories, phonation, supraglottal myoelastic vibrations, and turbulence, have been modeled from audio recordings. Two data sets were assembled, consisting of different vocal imitations by four professional imitators and four non-professional speakers in two different experiments. The audio data were manually annotated by two experienced phoneticians using a detailed articulatory description scheme. A separate set of audio features was developed specifically for each category using both time-domain and spectral methods. For all time-frequency transformations, and for some secondary processing, the recently developed Auditory Receptive Fields Toolbox was used. Three different machine learning methods were applied for predicting the final articulatory categories. The result with the best generalization was found using an ensemble of multilayer perceptrons. The cross-validated classification accuracy was 96.8% for phonation, 90.8% for supraglottal myoelastic vibrations, and 89.0% for turbulence using all the 84 developed features. A final feature reduction to 22 features yielded similar results.

Who's that Knocking at My Door? Neural Bases of Sound Source Identification.

When hearing knocking on a door, a listener typically identifies both the action (forceful and repeated impacts) and the object (a thick wooden board) causing the sound. The current work studied the neural bases of sound source identification by switching listeners' attention toward these different aspects of a set of simple sounds during functional magnetic resonance imaging scanning: participants either discriminated the action or the material that caused the sounds, or they simply discriminated meaningless scrambled versions of them. Overall, discriminating action and material elicited neural activity in a left-lateralized frontoparietal network found in other studies of sound identification, wherein the inferior frontal sulcus and the ventral premotor cortex were under the control of selective attention and sensitive to task demand. More strikingly, discriminating materials elicited increased activity in cortical regions connecting auditory inputs to semantic, motor, and even visual representations, whereas discriminating actions did not increase activity in any regions. These results indicate that discriminating and identifying material requires deeper processing of the stimuli than discriminating actions. These results are consistent with previous studies suggesting that auditory perception is better suited to comprehend the actions than the objects producing sounds in the listeners' environment.

Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging.

Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and diagnosis (CAD) systems are being designed to help radiologists in their clinical practice. We propose a CAD system taking advantage of all MRI modalities (i.e., T2-W-MRI, DCE-MRI, diffusion weighted (DW)-MRI, MRSI). The aim of this CAD system was to provide a probabilistic map of cancer location in the prostate. We extensively tested our proposed CAD using different fusion approaches to combine the features provided by each modality. The source code and the dataset have been released.

Rising tones and rustling noises: Metaphors in gestural depictions of sounds.

Communicating an auditory experience with words is a difficult task and, in consequence, people often rely on imitative non-verbal vocalizations and gestures. This work explored the combination of such vocalizations and gestures to communicate auditory sensations and representations elicited by non-vocal everyday sounds. Whereas our previous studies have analyzed vocal imitations, the present research focused on gestural depictions of sounds. To this end, two studies investigated the combination of gestures and non-verbal vocalizations. A first, observational study examined a set of vocal and gestural imitations of recordings of sounds representative of a typical everyday environment (ecological sounds) with manual annotations. A second, experimental study used non-ecological sounds whose parameters had been specifically designed to elicit the behaviors highlighted in the observational study, and used quantitative measures and inferential statistics. The results showed that these depicting gestures are based on systematic analogies between a referent sound, as interpreted by a receiver, and the visual aspects of the gestures: auditory-visual metaphors. The results also suggested a different role for vocalizations and gestures. Whereas the vocalizations reproduce all features of the referent sounds as faithfully as vocally possible, the gestures focus on one salient feature with metaphors based on auditory-visual correspondences. Both studies highlighted two metaphors consistently shared across participants: the spatial metaphor of pitch (mapping different pitches to different positions on the vertical dimension), and the rustling metaphor of random fluctuations (rapidly shaking of hands and fingers). We interpret these metaphors as the result of two kinds of representations elicited by sounds: auditory sensations (pitch and loudness) mapped to spatial position, and causal representations of the sound sources (e.g. rain drops, rustling leaves) pantomimed and embodied by the participants' gestures.

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. RESULTS AND CONCLUSION: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.

An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images.

This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.

Vocal Imitations of Non-Vocal Sounds.

Imitative behaviors are widespread in humans, in particular whenever two persons communicate and interact. Several tokens of spoken languages (onomatopoeias, ideophones, and phonesthemes) also display different degrees of iconicity between the sound of a word and what it refers to. Thus, it probably comes at no surprise that human speakers use a lot of imitative vocalizations and gestures when they communicate about sounds, as sounds are notably difficult to describe. What is more surprising is that vocal imitations of non-vocal everyday sounds (e.g. the sound of a car passing by) are in practice very effective: listeners identify sounds better with vocal imitations than with verbal descriptions, despite the fact that vocal imitations are inaccurate reproductions of a sound created by a particular mechanical system (e.g. a car driving by) through a different system (the voice apparatus). The present study investigated the semantic representations evoked by vocal imitations of sounds by experimentally quantifying how well listeners could match sounds to category labels. The experiment used three different types of sounds: recordings of easily identifiable sounds (sounds of human actions and manufactured products), human vocal imitations, and computational "auditory sketches" (created by algorithmic computations). The results show that performance with the best vocal imitations was similar to the best auditory sketches for most categories of sounds, and even to the referent sounds themselves in some cases. More detailed analyses showed that the acoustic distance between a vocal imitation and a referent sound is not sufficient to account for such performance. Analyses suggested that instead of trying to reproduce the referent sound as accurately as vocally possible, vocal imitations focus on a few important features, which depend on each particular sound category. These results offer perspectives for understanding how human listeners store and access long-term sound representations, and sets the stage for the development of human-computer interfaces based on vocalizations.

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection.

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.

The Egocentric Nature of Action-Sound Associations.

Actions that produce sounds infuse our daily lives. Some of these sounds are a natural consequence of physical interactions (such as a clang resulting from dropping a pan), but others are artificially designed (such as a beep resulting from a keypress). Although the relationship between actions and sounds has previously been examined, the frame of reference of these associations is still unknown, despite it being a fundamental property of a psychological representation. For example, when an association is created between a keypress and a tone, it is unclear whether the frame of reference is egocentric (gesture-sound association) or exocentric (key-sound association). This question is especially important for artificially created associations, which occur in technology that pairs sounds with actions, such as gestural interfaces, virtual or augmented reality, and simple buttons that produce tones. The frame of reference could directly influence the learnability, the ease of use, the extent of immersion, and many other factors of the interaction. To explore whether action-sound associations are egocentric or exocentric, an experiment was implemented using a computer keyboard's number pad wherein moving a finger from one key to another produced a sound, thus creating an action-sound association. Half of the participants received egocentric instructions to move their finger with a particular gesture. The other half of the participants received exocentric instructions to move their finger to a particular number on the keypad. All participants were performing the same actions, and only the framing of the action varied between conditions by altering task instructions. Participants in the egocentric condition learned the gesture-sound association, as revealed by a priming paradigm. However, the exocentric condition showed no priming effects. This finding suggests that action-sound associations are egocentric in nature. A second part of the same session further confirmed the egocentric nature of these associations by showing no change in the priming effect after moving to a different starting location. Our findings are consistent with an egocentric representation of action-sound associations, which could have implications for applications that utilize these associations.

Vocal imitations of basic auditory features.

Describing complex sounds with words is a difficult task. In fact, previous studies have shown that vocal imitations of sounds are more effective than verbal descriptions [Lemaitre and Rocchesso (2014). J. Acoust. Soc. Am. 135, 862-873]. The current study investigated how vocal imitations of sounds enable their recognition by studying how two expert and two lay participants reproduced four basic auditory features: pitch, tempo, sharpness, and onset. It used 4 sets of 16 referent sounds (modulated narrowband noises and pure tones), based on 1 feature or crossing 2 of the 4 features. Dissimilarity rating experiments and multidimensional scaling analyses confirmed that listeners could accurately perceive the four features composing the four sets of referent sounds. The four participants recorded vocal imitations of the four sets of sounds. Analyses identified three strategies: (1) Vocal imitations of pitch and tempo reproduced faithfully the absolute value of the feature; (2) Vocal imitations of sharpness transposed the feature into the participants' registers; (3) Vocal imitations of onsets categorized the continuum of onset values into two discrete morphological profiles. Overall, these results highlight that vocal imitations do not simply mimic the referent sounds, but seek to emphasize the characteristic features of the referent sounds within the constraints of human vocal production.