Distraction by deviant sounds: disgusting and neutral words capture attention to the same extent

Several studies have argued that words evoking negative emotions, such as disgust, grab attention more than neutral words, and leave traces in memory that are more persistent. However, these conclusions are typically based on tasks requiring participants to process the semantic content of these words in a voluntarily manner. We sought to compare the involuntary attention grabbing power of disgusting and neutral words using them as rare and unexpected auditory distractors in a cross-modal oddball task, and then probing the participants’ memory for these stimuli in a surprise recognition task. Frequentist and Bayesian analyses converged to show that, compared to a standard tone, disgusting and neutral auditory words produced significant but equivalent levels of distraction in a visual categorization task, that they elicited comparable levels of memory discriminability in the incidental recognition task, and that the participants’ individual sensitivity to disgust did not influence the results. Our results suggest that distraction by unexpected words is not modulated by their emotional valence, at least when these words are task-irrelevant and are temporally and perceptually decoupled from the target stimuliThis work was supported by Research Grants PSI2014-54261-P, PSI2015-63525-P, and PSI2015-65116-P from the Spanish Ministry of Science, Innovation and Universities (MICINN), the Spanish State Agency for Research (AEI) and the European Regional Development Fund (FEDER), as well as Grants 2017PFR-URV-B2-32 from the Universitat Rovira i Virgili, and GRC 2015/006 from (Xunta de Galicia). Fabrice B. R. Parmentier’s contract at the University of the Balearic Islands is co-financed by the MICINN’s program for the incentivization and permanent incorporation of doctors (2016 call, Ref IEDI-2016-00742). Fabrice B. R. Parmentier is also an Adjunct Senior Lecturer at the University of Western AustraliaS

Representation of Functional Data in Neural Networks

Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data analysis.Comment: Also available online from: http://www.sciencedirect.com/science/journal/0925231

Self-processing in coma, unresponsive wakefulness syndrome and minimally conscious state

IntroductionBehavioral and cerebral dissociation has been now clearly established in some patients with acquired disorders of consciousness (DoC). Altogether, these studies mainly focused on the preservation of high-level cognitive markers in prolonged DoC, but did not specifically investigate lower but key-cognitive functions to consciousness emergence, such as the ability to take a first-person perspective, notably at the acute stage of coma. We made the hypothesis that the preservation of self-recognition (i) is independent of the behavioral impairment of consciousness, and (ii) can reflect the ability to recover consciousness.MethodsHence, using bedside Electroencephalography (EEG) recordings, we acquired, in a large cohort of 129 severely brain damaged patients, the brain response to the passive listening of the subject’s own name (SON) and unfamiliar other first names (OFN). One hundred and twelve of them (mean age ± SD = 46 ± 18.3 years, sex ratio M/F: 71/41) could be analyzed for the detection of an individual and significant discriminative P3 event-related brain response to the SON as compared to OFN (‘SON effect’, primary endpoint assessed by temporal clustering permutation tests).ResultsPatients were either coma (n = 38), unresponsive wakefulness syndrome (UWS, n = 30) or minimally conscious state (MCS, n = 44), according to the revised version of the Coma Recovery Scale (CRS-R). Overall, 33 DoC patients (29%) evoked a ‘SON effect’. This electrophysiological index was similar between coma (29%), MCS (23%) and UWS (34%) patients (p = 0.61). MCS patients at the time of enrolment were more likely to emerged from MCS (EMCS) at 6 months than coma and UWS patients (p = 0.013 for comparison between groups). Among the 72 survivors’ patients with event-related responses recorded within 3 months after brain injury, 75% of the 16 patients with a SON effect were EMCS at 6 months, while 59% of the 56 patients without a SON effect evolved to this favorable behavioral outcome.DiscussionAbout 30% of severely brain-damaged patients suffering from DoC are capable to process salient self-referential auditory stimuli, even in case of absence of behavioral detection of self-conscious processing. We suggest that self-recognition covert brain ability could be an index of consciousness recovery, and thus could help to predict good outcome

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.Comment: http://www.sciencedirect.com/science/journal/0893608

Support vector machine for functional data classification

In many applications, input data are sampled functions taking their values in infinite dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate modifications of them. In fact most of the traditional data analysis tools for regression, classification and clustering have been adapted to functional inputs under the general name of functional Data Analysis (FDA). In this paper, we investigate the use of Support Vector Machines (SVMs) for functional data analysis and we focus on the problem of curves discrimination. SVMs are large margin classifier tools based on implicit non linear mappings of the considered data into high dimensional spaces thanks to kernels. We show how to define simple kernels that take into account the unctional nature of the data and lead to consistent classification. Experiments conducted on real world data emphasize the benefit of taking into account some functional aspects of the problems.Comment: 13 page

Regional anesthesia with noninvasive ventilation for shoulder surgery in a patient with severe chronic obstructive pulmonary disease: a case report

Interscalene block (ISB) impairs ipsilateral lung function and generally is not used for patients with respiratory insufficiency. We present a 49-year-old man with chronic obstructive pulmonary disease scheduled for shoulder surgery. He was given a regional technique with an ISB (short-acting local anesthetic to minimize duration of diaphragmatic dysfunction) and suprascapular and axillary nerves blocks (long-acting local anesthetic). He was supported with noninvasive ventilation during the time of hemidiaphragmatic paralysis as documented by serial ultrasound examination. A discussion about ISB and its alternatives (general anesthesia versus brachial plexus block versus selective peripheral nerve blocks) always should occur for patients at risk for pulmonary complications

Postoperative delirium is a risk factor of institutionalization after hip fracture: an observational cohort study

IntroductionHip fracture is a common clinical problem in geriatric patients often associated with poor postoperative outcomes. Postoperative delirium (POD) and postoperative neurocognitive disorders (NCDs) are particularly frequent. The consequences of these disorders on postoperative recovery and autonomy are not fully described. The aim of this study was to determine the role of POD and NCDs on the need for institutionalization at 3 months after hip fracture surgery.MethodA population-based prospective cohort study was conducted on hip fracture patients between March 2016 and March 2018. The baseline interview, which included a Mini-Mental State Examination (MMSE), was conducted in the hospital after admission for hip fracture. NCDs were appreciated by MMSE scoring evolution (difference between preoperative MMSE and MMSE at day 5 >2 points). POD was evaluated using the Confusion Assessment Method. The primary endpoint was the rate of new institutionalization at 3 months. We used a multivariate analysis to assess the risk of new institutionalization.ResultsA total of 63 patients were included. Thirteen patients (20.6%) were newly institutionalized at 3 months. Two factors were significantly associated with the risk of postoperative institutionalization at 3 months: POD (OR = 5.23; 95% CI 1.1–27.04; p = 0.04) and IADL evolution (OR = 1.8; 95% CI 1.23–2.74; p = 0.003).ConclusionOnly POD but not NCDs was associated with the risk of dependency and institutionalization after hip fracture surgery. The prevention of POD appears to be essential for improving patient outcomes and optimizing the potential for returning home

Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs

Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to arbitrary accuracy any continuous mapping from a compact sub-space of a functional space to R. Moreover, we provide a consistency result that shows that any mapping from a functional space to R can be learned thanks to examples by a projection based MLP: the generalization mean square error of the MLP decreases to the smallest possible mean square error on the data when the number of examples goes to infinity

Consistency of functional learning methods based on derivatives

International audienceIn some real world applications, such as spectrometry, functional models achieve better predictive performances if they work on the derivatives of order m of their inputs rather than on the original functions. As a consequence, the use of derivatives is a common practice in Functional Data Analysis, despite a lack of theoretical guarantees on the asymptotically achievable performances of a derivative based model. In this paper, we show that a smoothing spline approach can be used to preprocess multivariate observations obtained by sampling functions on a discrete and finite sampling grid in a way that leads to a consistent scheme on the original infinite dimensional functional problem. This work extends (Mas and Pumo, 2009) to nonparametric approaches and incomplete knowledge. To be more precise, the paper tackles two difficulties in a nonparametric framework: the information loss due to the use of the derivatives instead of the original functions and the information loss due to the fact that the functions are observed through a discrete sampling and are thus also unperfectly known: the use of a smoothing spline based approach solves these two problems. Finally, the proposed approach is tested on two real world datasets and the approach is experimentaly proven to be a good solution in the case of noisy functional predictors

Signatures électrophysiologiques des dysfonctions cognitives associées aux troubles de la conscience

Disorder of consciousness can be observed after a severe brain injury (e.g., traumatic or anoxic), or it can be caused by a direct consequence of a medical context favoring its occurrence in critically-ill patients, of which delirium is the main form of expression. The main objective of our researches was to evaluate the cognitive dysfunctions observable in two distinct models of altered states of consciousness (disorders of consciousness - study 1, and delirium - study 2) using auditory event-related potentials. The analysis of a large multicenter cohort of patients with disorders of consciousness (coma, minimally conscious state, unresponsive wakefulness syndrome) revealed a P3 response to subject's own name in 30% of patients, independently of their behavioral level of consciousness. This result argues for the existence of a cognitive-behavioral dissociation, especially for patients in the acute stage of coma. Interestingly, this covert brain ability required to correctly categorize self-related ecological words and suggesting self-recognition, detected at the bedside early after ictus, was able to predict behaviorally overt consciousness recovery in survivors at 6 months, and should be thus considered as a prerequisite for consciousness emergence. Using a multidimensional auditory event-related potential battery specifically designed to probe hierarchical cognitive processes in critically-ill patients with delirium, we have identified both, a preservation of the 'bottom-up' automatic echoic memory required to discriminate simple tones (P3a to local deviant sounds), and a coherent ensemble of covert higher-order cognitive dysfunctions encompassing the 'top-down' volitional attentional engagement required for the active maintenance of simple tones (P3b to global deviant sounds) in working memory or verbal stimuli (P3 to simple arithmetic facts violation and N400 effects to semantic and lexical incongruence) in semantic memory. Interestingly, the automatic access to autobiographical memory required to categorize the subject's own name was also impaired in delirium patients. In this setting, estimation of the cortical generators of the P3 response to target stimuli by source modeling identified the predominant role of the prefrontal cortex in control subjects, whose activation was modulated downward by delirium. This research project allowed the description of a new electrophysiological taxonomy of cognitive dysfunctions associated with delirium, and the development of prognostic biomarkers based on residual cognitive capacities of patients with severe acute disorders of consciousness.Un trouble de la conscience peut être observé après une lésion cérébrale sévère (par exemple, traumatique ou anoxique), ou être la conséquence directe d'un contexte médical favorisant sa survenue chez les patients en état critique, dont le delirium est la principale forme d’expression. L'objectif principal de nos recherches était d'évaluer les dysfonctions cognitives observables dans deux modèles distincts de troubles de la conscience (états de conscience altérée – étude 1, et delirium – étude 2) en utilisant les potentiels évoqués auditifs. L'analyse d'une large cohorte multicentrique de patients présentant un état de conscience altérée (coma, état de conscience minimale, syndrome d'éveil sans réponse) a révélé une réponse P3 au propre prénom chez 30% des patients, indépendamment de leur niveau de conscience comportemental. Ce résultat plaide en faveur de l'existence d'une dissociation cognitivo-comportementale, notamment pour les patients en phase aiguë de coma. De façon intéressante, cette capacité cérébrale occulte de discrimination d’un stimulus écologique auto-référentiel suggérant la reconnaissance de soi, détectée au chevet du patient précocement après l'ictus, était capable de prédire la récupération d’un état de conscience comportemental chez les survivants à 6 mois, et pourrait donc être considérée comme une condition préalable à l'émergence de la conscience. En utilisant une batterie multidimensionnelle de potentiels évoqués auditifs, spécialement conçue pour sonder les processus cognitifs hiérarchiques des patients en état critique atteints de delirium, nous avons identifié à la fois, une préservation de la mémoire échoïque automatique ‘bottom-up’ nécessaire à la discrimination des sons simples (P3a aux sons déviants locaux), et un ensemble cohérent de dysfonctions cognitives d'ordre supérieur occultes, englobant l'engagement attentionnel volontaire ‘top-down’ nécessaire au maintien actif de sons simples (P3b aux sons déviants globaux) dans la mémoire de travail ou de stimuli verbaux (P3 à la violation de faits arithmétiques simples et effets N400 à l'incongruence sémantique et lexicale) dans la mémoire sémantique. De façon intéressante, l’accès automatique à la mémoire autobiographique nécessaire à la catégorisation du propre prénom était également altéré chez les patients en état de delirium. Dans ce contexte, l'estimation des générateurs corticaux de la réponse P3 aux stimuli cibles par modélisation des sources a permis d'identifier le rôle prédominant du cortex préfrontal chez les sujets contrôles, dont l'activation était modulée à la baisse par le delirium. Ce projet de recherche a permis de décrire une nouvelle taxonomie électrophysiologique des dysfonctions cognitives associées au delirium, et de développer des biomarqueurs pronostiques basés sur les capacités cognitives résiduelles des patients présentant des troubles aigus sévères de la conscience