Irritability in pre-clinical Huntington's disease.

Irritability, together with depression and anxiety, form three salient clinical features of pre-symptomatic Huntington's disease (HD). To date, the understanding of irritability in HD suffers from a paucity of experimental data and is largely based on questionnaires or clinical anecdotes. Factor analysis suggests that irritability is related to impulsivity and aggression and is likely to engage the same neuronal circuits as these behaviours, including areas such as medial orbitofrontal cortex (OFC) and amygdala. 16 pre-symptomatic gene carriers (PSCs) and 15 of their companions were asked to indicate the larger of two squares consecutively shown on a screen while undergoing functional magnetic resonance imaging (fMRI). Despite correct identification of the larger square, participants were often told that they or their partner had given the wrong answer. Size differences were subtle to make negative feedback credible but detectable. Although task performance, baseline irritability, and reported task-induced irritation were the same for both groups, fMRI revealed distinct neuronal processing in those who will later develop HD. In controls but not PSCs, task-induced irritation correlated positively with amygdala activation and negatively with OFC activation. Repetitive negative feedback induced greater amygdala activations in controls than PSCs. In addition, the inverse functional coupling between amygdala and OFC was significantly weaker in PSCs compared to controls. Our results argue that normal emotion processing circuits are disrupted in PSCs via attenuated modulation of emotional status by external or internal indicators. At later stages, this dysfunction may increase the risk for developing recognised, HD-associated, psychiatric symptoms such as irritability.

Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

Apprendre dans l'urgence

Nous avons effectué une étude dans le but d'investiger les stratégies utilisées par les ambulanciers pour préserver leur santé. Parmi les stratégies que nous avons identifiées, celles contribuant au maintien et au développement des compétences nous ont semblé particulièrement intéressantes car elles permettent de réduire le stress lié à la peur de l'erreur. Elles permettent aussi aux ambulanciers "d'apprivoiser l'urgence" en leur donnant la possibilité de jouer un rôle actif face aux difficultés rencontrées. Nous présentons quelques-unes de ces stratégies dans cet article.

Estimation of the noisy component of anatomical backgrounds.

The knowledge of the relationship that links radiation dose and image quality is a prerequisite to any optimization of medical diagnostic radiology. Image quality depends, on the one hand, on the physical parameters such as contrast, resolution, and noise, and on the other hand, on characteristics of the observer that assesses the image. While the role of contrast and resolution is precisely defined and recognized, the influence of image noise is not yet fully understood. Its measurement is often based on imaging uniform test objects, even though real images contain anatomical backgrounds whose statistical nature is much different from test objects used to assess system noise. The goal of this study was to demonstrate the importance of variations in background anatomy by quantifying its effect on a series of detection tasks. Several types of mammographic backgrounds and signals were examined by psychophysical experiments in a two-alternative forced-choice detection task. According to hypotheses concerning the strategy used by the human observers, their signal to noise ratio was determined. This variable was also computed for a mathematical model based on the statistical decision theory. By comparing theoretical model and experimental results, the way that anatomical structure is perceived has been analyzed. Experiments showed that the observer's behavior was highly dependent upon both system noise and the anatomical background. The anatomy partly acts as a signal recognizable as such and partly as a pure noise that disturbs the detection process. This dual nature of the anatomy is quantified. It is shown that its effect varies according to its amplitude and the profile of the object being detected. The importance of the noisy part of the anatomy is, in some situations, much greater than the system noise. Hence, reducing the system noise by increasing the dose will not improve task performance. This observation indicates that the tradeoff between dose and image quality might be optimized by accepting a higher system noise. This could lead to a better resolution, more contrast, or less dose.

Functional compensation of motor function in pre-symptomatic Huntington's disease.

Involuntary choreiform movements are a clinical hallmark of Huntington's disease. Studies in clinically affected patients suggest a shift of motor activations to parietal cortices in response to progressive neurodegeneration. Here, we studied pre-symptomatic gene carriers to examine the compensatory mechanisms that underlie the phenomenon of retained motor function in the presence of degenerative change. Fifteen pre-symptomatic gene carriers and 12 matched controls performed button presses paced by a metronome at either 0.5 or 2 Hz with four fingers of the right hand whilst being scanned with functional magnetic resonance imaging. Subjects pressed buttons either in the order of a previously learnt 10-item finger sequence, from left to right, or kept still. Error rates ranged from 2% to 7% in the pre-symptomatic gene carriers and from 0.5% to 4% in controls, depending on the condition. No significant difference in task performance was found between groups for any of the conditions. Activations in the supplementary motor area (SMA) and superior parietal lobe differed with gene status. Compared with healthy controls, gene carriers showed greater activations of left caudal SMA with all movement conditions. Activations correlated with increasing speed of movement were greater the closer the gene carriers were to estimated clinical diagnosis, defined by the onset of unequivocal motor signs. Activations associated with increased movement complexity (i.e. with the pre-learnt 10-item sequence) decreased in the rostral SMA with nearing diagnostic onset. The left superior parietal lobe showed reduced activation with increased movement complexity in gene carriers compared with controls, and in the right superior parietal lobe showed greater activations with all but the most demanding movements. We identified a complex pattern of motor compensation in pre-symptomatic gene carriers. The results show that preclinical compensation goes beyond a simple shift of activity from premotor to parietal regions involving multiple compensatory mechanisms in executive and cognitive motor areas. Critically, the pattern of motor compensation is flexible depending on the actual task demands on motor control.

Visuospatial working memory deficits and visual pursuit impairments are not directly related in schizophrenia.

OBJECTIVE: Patients with schizophrenia show deficits in visuospatial working memory and visual pursuit processes. It is currently unclear, however, whether both impairments are related to a common neuropathological origin. The purpose of the present study was therefore to examine the possible relations between the encoding and the discrimination of dynamic visuospatial stimuli in schizophrenia. METHOD: Sixteen outpatients with schizophrenia and 16 control subjects were asked to encode complex disc displacements presented on a screen. After a delay, participants had to identify the previously presented disc trajectory from a choice of six static linear paths, among which were five incorrect paths. The precision of visual pursuit eye movements during the initial presentation of the dynamic stimulus was assessed. The fixations and scanning time in definite regions of the six paths presented during the discrimination phase were investigated. RESULTS: In comparison with controls, patients showed poorer task performance, reduced pursuit accuracy during incorrect trials and less time scanning the correct stimulus or the incorrect paths approximating its global structure. Patients also spent less time scanning the leftmost portion of the correct path even when making a correct choice. The accuracy of visual pursuit and head movements, however, was not correlated with task performance. CONCLUSIONS: The present study provides direct support for the hypothesis that active integration of visuospatial information within working memory is deficient in schizophrenia. In contrast, a general impairment of oculomotor mechanisms involved in smooth pursuit did not appear to be directly related to lower visuospatial working memory performance in schizophrenia.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Key cognitive and collaborative aptitudes in ambulance people

The aim of our study was to identify and document some key cognitive aptitudes used by ambulance people in emergency situations. Better knowing such aptitudes is necessary for a school of ambulance people in order to improve the selection and education of students. The idea was to better consider real work activity requirements and characteristics, and to develop and implement genuine educational content and selection tools. We followed the work activity of ambulance professionals involved in real emergency situations. Some interventions were filmed and post-analyzed. We completed and validated our analysis by means of interviews with ambulance personnel. We selected some video sequences and used them as a support for the interviews. We identified and documented many different key aptitudes like orientation and spatial sense, the capacity to perform complex cognitive tasks and delicate manipulations in the context of divided attention, as well as diverse aptitudes relevant in collaborative work.

Etude des stratégies des travailleurs : un outil pour la prévention des troubles musculosquelettiques

En quoi et comment l'étude des stratégies représente-t-elle un intérêt pour la prévention des troubles musculosquelettiques (TMS) ? Qu'en est-il des retombées d'une telle étude pour l'entreprise et pour les travailleurs ? Cet article se propose d'évaluer les diverses stratégies utilisées par les travailleurs pour gérer leur douleur face aux TMS. Résultats d'une étude qui s'est déroulée au sein de deux usines de transformation du crabe.

L'intérêt de l'analyse de l'activité en complément de tout outil d'évaluation de l'environnement psychosocial

L'analyse de l'activité reste le principal garant d'une bonne évaluation de la situation de travail et de l'environnement psychosocial. Elle permet de donner du contenu, de la substance et ainsi d'expliquer les données récoltées par questionnaire. Lors d'études à petite échelle et dans un contexte d'activité spécifique, l'analyse de l'activité devrait toujours accompagner l'utilisation d'autres outils d'évaluation. En effet, on voit ici que les échelles de Karasek ne traduisent pas toujours la réalité des diverses situations de travail, et l'autonomie décisionnelle de Karasek n'est ici pas un bon révélateur des possibilités d'ajustement et de la marge de manoeuvre. Ces échelles ont été initialement conçues pour être utilisées lors d'études épidémiologiques et donc sur de grandes populations où la spécificité de certaines activités influencerait ainsi moins le résultat global. Leur utilisation de façon plus ou moins abusive sur d'autres types de populations doit être questionnée

Molecular and social regulation of worker division of labour in fire ants.

Reproductive and worker division of labour (DOL) is a hallmark of social insect societies. Despite a long-standing interest in worker DOL, the molecular mechanisms regulating this process have only been investigated in detail in honey bees, and little is known about the regulatory mechanisms operating in other social insects. In the fire ant Solenopsis invicta, one of the most studied ant species, workers are permanently sterile and the tasks performed are modulated by the worker's internal state (age and size) and the outside environment (social environment), which potentially includes the effect of the queen presence through chemical communication via pheromones. However, the molecular mechanisms underpinning these processes are unknown. Using a whole-genome microarray platform, we characterized the molecular basis for worker DOL and we explored how a drastic change in the social environment (i.e. the sudden loss of the queen) affects global gene expression patterns of worker ants. We identified numerous genes differentially expressed between foraging and nonforaging workers in queenright colonies. With a few exceptions, these genes appear to be distinct from those involved in DOL in bees and wasps. Interestingly, after the queen was removed, foraging workers were no longer distinct from nonforaging workers at the transcriptomic level. Furthermore, few expression differences were detected between queenright and queenless workers when we did not consider the task performed. Thus, the social condition of the colony (queenless vs. queenright) appears to impact the molecular pathways underlying worker task performance, providing strong evidence for social regulation of DOL in S. invicta.

Weed or wheel! FMRI, behavioural, and toxicological investigations of how cannabis smoking affects skills necessary for driving.

Marijuana is the most widely used illicit drug, however its effects on cognitive functions underling safe driving remain mostly unexplored. Our goal was to evaluate the impact of cannabis on the driving ability of occasional smokers, by investigating changes in the brain network involved in a tracking task. The subject characteristics, the percentage of Δ(9)-Tetrahydrocannabinol in the joint, and the inhaled dose were in accordance with real-life conditions. Thirty-one male volunteers were enrolled in this study that includes clinical and toxicological aspects together with functional magnetic resonance imaging of the brain and measurements of psychomotor skills. The fMRI paradigm was based on a visuo-motor tracking task, alternating active tracking blocks with passive tracking viewing and rest condition. We show that cannabis smoking, even at low Δ(9)-Tetrahydrocannabinol blood concentrations, decreases psychomotor skills and alters the activity of the brain networks involved in cognition. The relative decrease of Blood Oxygen Level Dependent response (BOLD) after cannabis smoking in the anterior insula, dorsomedial thalamus, and striatum compared to placebo smoking suggests an alteration of the network involved in saliency detection. In addition, the decrease of BOLD response in the right superior parietal cortex and in the dorsolateral prefrontal cortex indicates the involvement of the Control Executive network known to operate once the saliencies are identified. Furthermore, cannabis increases activity in the rostral anterior cingulate cortex and ventromedial prefrontal cortices, suggesting an increase in self-oriented mental activity. Subjects are more attracted by intrapersonal stimuli ("self") and fail to attend to task performance, leading to an insufficient allocation of task-oriented resources and to sub-optimal performance. These effects correlate with the subjective feeling of confusion rather than with the blood level of Δ(9)-Tetrahydrocannabinol. These findings bolster the zero-tolerance policy adopted in several countries that prohibits the presence of any amount of drugs in blood while driving.

Stage fright: its experience as a problem and coping with it.

PURPOSE AND METHOD: This questionnaire survey of 190 university music students assessed negative feelings of music performance anxiety (MPA) before performing, the experience of stage fright as a problem, and how closely they are associated with each other. The study further investigated whether the experience of stage fright as a problem and negative feelings of MPA predict the coping behavior of the music students. Rarely addressed coping issues were assessed, i.e., self-perceived effectiveness of different coping strategies, knowledge of possible risks and acceptance of substance-based coping strategies, and need for more support.RESULTS: The results show that one-third of the students experienced stage fright as a problem and that this was only moderately correlated with negative feelings of MPA. The experience of stage fright as a problem significantly predicted the frequency of use and the acceptance of medication as a coping strategy. Breathing exercises and self-control techniques were rated as effective as medication. Finally, students expressed a strong need to receive more support (65%) and more information (84%) concerning stage fright.CONCLUSION: Stage fright was experienced as a problem and perceived as having negative career consequences by a considerable percentage of the surveyed students. In addition to a desire for more help and support, the students expressed an openness and willingness to seriously discuss and address the topic of stage fright. This provides a necessary and promising basis for optimal career preparation and, hence, an opportunity to prevent occupational problems in professional musicians. [Authors]

Theory of mind impairment in patients with behavioural variant fronto-temporal dementia (bv-FTD) increases caregiver burden.

BACKGROUND: Theory of mind (ToM), the capacity to infer the intention, beliefs and emotional states of others, is frequently impaired in behavioural variant fronto-temporal dementia patients (bv-FTDp); however, its impact on caregiver burden is unexplored. SETTING: National Institute of Neurological Disorders and Stroke, National Institutes of Health. SUBJECTS: bv-FTDp (n = 28), a subgroup of their caregivers (n = 20) and healthy controls (n = 32). METHODS: we applied a faux-pas (FP) task as a ToM measure in bv-FTDp and healthy controls and the Zarit Burden Interview as a measure of burden in patients' caregivers. Patients underwent structural MRI; we used voxel-based morphometry to examine relationships between regional atrophy and ToM impairment and caregiver burden. RESULTS: FP task performance was impaired in bv-FTDp and negatively associated with caregiver burden. Atrophy was found in areas involved in ToM. Caregiver burden increased with greater atrophy in left lateral premotor cortex, a region associated in animal models with the presence of mirror neurons, possibly involved in empathy. CONCLUSION: ToM impairment in bv-FTDp is associated with increased caregiver burden.

Development of the ability to inhibit a prepotent response: influence of working memory and processing speed.

This study aimed to examine developmental trends in response inhibition during childhood and to control for possible developmental influence of other basic cognitive processes (such as working memory and processing speed). In addition, we explored the relationships between response inhibition, working memory, and processing speed, as they are thought to be integral to cognitive control. Therefore, we assessed these three cognitive abilities in 159 children aged from 5 to 12. Results showed an improvement in response inhibition ability from 5 to 10 years of age. This improvement remained significant after controlling for the influence of working memory and processing speed. Furthermore, the developmental relationships showed an early differentiation between response inhibition, working memory, and processing speed. Thus, these processes were independent and need to be treated as such in further studies.