Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.

Improving Pitch Tracking Performance in Hard Noise Conditions by a Preprocessing Based on Mathematical Morphology

In this paper we show how a nonlinear preprocessing of speech signal -with high noise- based on morphological filters improves the performance of robust algorithms for pitch tracking (RAPT). This result happens for a very simple morphological filter. More sophisticated ones could even improve such results. Mathematical morphology is widely used in image processing and has a great amount of applications. Almost all its formulations derived in the two-dimensional framework are easily reformulated to be adapted to one-dimensional context

Accuracy and Precision of Head Motion Information in Multi-Channel Free Induction Decay Navigators for Magnetic Resonance Imaging.

Free induction decay (FID) navigators were found to qualitatively detect rigid-body head movements, yet it is unknown to what extent they can provide quantitative motion estimates. Here, we acquired FID navigators at different sampling rates and simultaneously measured head movements using a highly accurate optical motion tracking system. This strategy allowed us to estimate the accuracy and precision of FID navigators for quantification of rigid-body head movements. Five subjects were scanned with a 32-channel head coil array on a clinical 3T MR scanner during several resting and guided head movement periods. For each subject we trained a linear regression model based on FID navigator and optical motion tracking signals. FID-based motion model accuracy and precision was evaluated using cross-validation. FID-based prediction of rigid-body head motion was found to be with a mean translational and rotational error of 0.14±0.21 mm and 0.08±0.13(°) , respectively. Robust model training with sub-millimeter and sub-degree accuracy could be achieved using 100 data points with motion magnitudes of ±2 mm and ±1(°) for translation and rotation. The obtained linear models appeared to be subject-specific as inter-subject application of a "universal" FID-based motion model resulted in poor prediction accuracy. The results show that substantial rigid-body motion information is encoded in FID navigator signal time courses. Although, the applied method currently requires the simultaneous acquisition of FID signals and optical tracking data, the findings suggest that multi-channel FID navigators have a potential to complement existing tracking technologies for accurate rigid-body motion detection and correction in MRI.

Evaluation of Software Development Practices through Error Analysis

This thesis studies evaluation of software development practices through an error analysis. The work presents software development process, software testing, software errors, error classification and software process improvement methods. The practical part of the work presents results from the error analysis of one software process. It also gives improvement ideas for the project. It was noticed that the classification of the error data was inadequate in the project. Because of this it was impossible to use the error data effectively. With the error analysis we were able to show that there were deficiencies in design and analyzing phases, implementation phase and in testing phase. The work gives ideas for improving error classification and for software development practices.

Error de inclinación paleomagnética en materiales aluviales del Oligoceno superior del sector Suroriental de la Cuenca del Ebro (Region Surpirenaica, NE de España).

En el sector suroriental de la Cuenca del Ebro, la inclinación paleomagnética obtenida en las sucesiones aluviales oligocenas es considerablemente menor que la esperable, si se considera la paleolatitud de referencia calculada para esa región durante el Oligoceno. Este error de inclinación puede deberse a diversos factores, como el control hidrodinámica de las partículas magnéticas en el medio deposicional, la compactación diferencial del sedimento durante el enterramiento, o bien a la deformación tectónica. Este trabajo se ha centrado en su estudio en dos sucesiones dominantemente aluviales, donde previamente se había establecido su magnetoestratigrafia. Las litofacies aluviales y lacustres estudiadas se han agrupado en cinco grupos: areniscas grises, areniscas rojas y versicolores, limos rojos, lutitas rojas y calizas. Se ha demostrado la existencia de una correlación entre la abundancia de filosilicatos y el error de inclinación. De esta manera, las litofacies con un bajo porcentaje de filosilicatos (calizas y areniscas grises) presentan errores de unos 5', estadisticarnente no significativos, con respecto a la inclinación de referencia. Por el contrario, en materiales con un porcentaje más elevado de filosilicatos (limos y arcillas) el error puede llegar a los 25'. Este hecho no tiene repercusión en la interpretación de las polaridades magnéticas, pero si en las reconstmcciones palinspásticas y paleogeográficas basadas en los cálculos de paleolatitudes a partir de las paleoinclinaciones. Los resultados obtenidos demuestran la necesidad de cautela en la propuesta de conclusiones basadas exclusivamente en este tipo de información.

Error models in hydrogeology applications

Notre consommation en eau souterraine, en particulier comme eau potable ou pour l'irrigation, a considérablement augmenté au cours des années. De nombreux problèmes font alors leur apparition, allant de la prospection de nouvelles ressources à la remédiation des aquifères pollués. Indépendamment du problème hydrogéologique considéré, le principal défi reste la caractérisation des propriétés du sous-sol. Une approche stochastique est alors nécessaire afin de représenter cette incertitude en considérant de multiples scénarios géologiques et en générant un grand nombre de réalisations géostatistiques. Nous rencontrons alors la principale limitation de ces approches qui est le coût de calcul dû à la simulation des processus d'écoulements complexes pour chacune de ces réalisations. Dans la première partie de la thèse, ce problème est investigué dans le contexte de propagation de l'incertitude, oú un ensemble de réalisations est identifié comme représentant les propriétés du sous-sol. Afin de propager cette incertitude à la quantité d'intérêt tout en limitant le coût de calcul, les méthodes actuelles font appel à des modèles d'écoulement approximés. Cela permet l'identification d'un sous-ensemble de réalisations représentant la variabilité de l'ensemble initial. Le modèle complexe d'écoulement est alors évalué uniquement pour ce sousensemble, et, sur la base de ces réponses complexes, l'inférence est faite. Notre objectif est d'améliorer la performance de cette approche en utilisant toute l'information à disposition. Pour cela, le sous-ensemble de réponses approximées et exactes est utilisé afin de construire un modèle d'erreur, qui sert ensuite à corriger le reste des réponses approximées et prédire la réponse du modèle complexe. Cette méthode permet de maximiser l'utilisation de l'information à disposition sans augmentation perceptible du temps de calcul. La propagation de l'incertitude est alors plus précise et plus robuste. La stratégie explorée dans le premier chapitre consiste à apprendre d'un sous-ensemble de réalisations la relation entre les modèles d'écoulement approximé et complexe. Dans la seconde partie de la thèse, cette méthodologie est formalisée mathématiquement en introduisant un modèle de régression entre les réponses fonctionnelles. Comme ce problème est mal posé, il est nécessaire d'en réduire la dimensionnalité. Dans cette optique, l'innovation du travail présenté provient de l'utilisation de l'analyse en composantes principales fonctionnelles (ACPF), qui non seulement effectue la réduction de dimensionnalités tout en maximisant l'information retenue, mais permet aussi de diagnostiquer la qualité du modèle d'erreur dans cet espace fonctionnel. La méthodologie proposée est appliquée à un problème de pollution par une phase liquide nonaqueuse et les résultats obtenus montrent que le modèle d'erreur permet une forte réduction du temps de calcul tout en estimant correctement l'incertitude. De plus, pour chaque réponse approximée, une prédiction de la réponse complexe est fournie par le modèle d'erreur. Le concept de modèle d'erreur fonctionnel est donc pertinent pour la propagation de l'incertitude, mais aussi pour les problèmes d'inférence bayésienne. Les méthodes de Monte Carlo par chaîne de Markov (MCMC) sont les algorithmes les plus communément utilisés afin de générer des réalisations géostatistiques en accord avec les observations. Cependant, ces méthodes souffrent d'un taux d'acceptation très bas pour les problèmes de grande dimensionnalité, résultant en un grand nombre de simulations d'écoulement gaspillées. Une approche en deux temps, le "MCMC en deux étapes", a été introduite afin d'éviter les simulations du modèle complexe inutiles par une évaluation préliminaire de la réalisation. Dans la troisième partie de la thèse, le modèle d'écoulement approximé couplé à un modèle d'erreur sert d'évaluation préliminaire pour le "MCMC en deux étapes". Nous démontrons une augmentation du taux d'acceptation par un facteur de 1.5 à 3 en comparaison avec une implémentation classique de MCMC. Une question reste sans réponse : comment choisir la taille de l'ensemble d'entrainement et comment identifier les réalisations permettant d'optimiser la construction du modèle d'erreur. Cela requiert une stratégie itérative afin que, à chaque nouvelle simulation d'écoulement, le modèle d'erreur soit amélioré en incorporant les nouvelles informations. Ceci est développé dans la quatrième partie de la thèse, oú cette méthodologie est appliquée à un problème d'intrusion saline dans un aquifère côtier. -- Our consumption of groundwater, in particular as drinking water and for irrigation, has considerably increased over the years and groundwater is becoming an increasingly scarce and endangered resource. Nofadays, we are facing many problems ranging from water prospection to sustainable management and remediation of polluted aquifers. Independently of the hydrogeological problem, the main challenge remains dealing with the incomplete knofledge of the underground properties. Stochastic approaches have been developed to represent this uncertainty by considering multiple geological scenarios and generating a large number of realizations. The main limitation of this approach is the computational cost associated with performing complex of simulations in each realization. In the first part of the thesis, we explore this issue in the context of uncertainty propagation, where an ensemble of geostatistical realizations is identified as representative of the subsurface uncertainty. To propagate this lack of knofledge to the quantity of interest (e.g., the concentration of pollutant in extracted water), it is necessary to evaluate the of response of each realization. Due to computational constraints, state-of-the-art methods make use of approximate of simulation, to identify a subset of realizations that represents the variability of the ensemble. The complex and computationally heavy of model is then run for this subset based on which inference is made. Our objective is to increase the performance of this approach by using all of the available information and not solely the subset of exact responses. Two error models are proposed to correct the approximate responses follofing a machine learning approach. For the subset identified by a classical approach (here the distance kernel method) both the approximate and the exact responses are knofn. This information is used to construct an error model and correct the ensemble of approximate responses to predict the "expected" responses of the exact model. The proposed methodology makes use of all the available information without perceptible additional computational costs and leads to an increase in accuracy and robustness of the uncertainty propagation. The strategy explored in the first chapter consists in learning from a subset of realizations the relationship between proxy and exact curves. In the second part of this thesis, the strategy is formalized in a rigorous mathematical framework by defining a regression model between functions. As this problem is ill-posed, it is necessary to reduce its dimensionality. The novelty of the work comes from the use of functional principal component analysis (FPCA), which not only performs the dimensionality reduction while maximizing the retained information, but also allofs a diagnostic of the quality of the error model in the functional space. The proposed methodology is applied to a pollution problem by a non-aqueous phase-liquid. The error model allofs a strong reduction of the computational cost while providing a good estimate of the uncertainty. The individual correction of the proxy response by the error model leads to an excellent prediction of the exact response, opening the door to many applications. The concept of functional error model is useful not only in the context of uncertainty propagation, but also, and maybe even more so, to perform Bayesian inference. Monte Carlo Markov Chain (MCMC) algorithms are the most common choice to ensure that the generated realizations are sampled in accordance with the observations. Hofever, this approach suffers from lof acceptance rate in high dimensional problems, resulting in a large number of wasted of simulations. This led to the introduction of two-stage MCMC, where the computational cost is decreased by avoiding unnecessary simulation of the exact of thanks to a preliminary evaluation of the proposal. In the third part of the thesis, a proxy is coupled to an error model to provide an approximate response for the two-stage MCMC set-up. We demonstrate an increase in acceptance rate by a factor three with respect to one-stage MCMC results. An open question remains: hof do we choose the size of the learning set and identify the realizations to optimize the construction of the error model. This requires devising an iterative strategy to construct the error model, such that, as new of simulations are performed, the error model is iteratively improved by incorporating the new information. This is discussed in the fourth part of the thesis, in which we apply this methodology to a problem of saline intrusion in a coastal aquifer.

Eye-tracking of nodule detection in lung CT volumetric data.

PURPOSE: Signal detection on 3D medical images depends on many factors, such as foveal and peripheral vision, the type of signal, and background complexity, and the speed at which the frames are displayed. In this paper, the authors focus on the speed with which radiologists and naïve observers search through medical images. Prior to the study, the authors asked the radiologists to estimate the speed at which they scrolled through CT sets. They gave a subjective estimate of 5 frames per second (fps). The aim of this paper is to measure and analyze the speed with which humans scroll through image stacks, showing a method to visually display the behavior of observers as the search is made as well as measuring the accuracy of the decisions. This information will be useful in the development of model observers, mathematical algorithms that can be used to evaluate diagnostic imaging systems. METHODS: The authors performed a series of 3D 4-alternative forced-choice lung nodule detection tasks on volumetric stacks of chest CT images iteratively reconstructed in lung algorithm. The strategy used by three radiologists and three naïve observers was assessed using an eye-tracker in order to establish where their gaze was fixed during the experiment and to verify that when a decision was made, a correct answer was not due only to chance. In a first set of experiments, the observers were restricted to read the images at three fixed speeds of image scrolling and were allowed to see each alternative once. In the second set of experiments, the subjects were allowed to scroll through the image stacks at will with no time or gaze limits. In both static-speed and free-scrolling conditions, the four image stacks were displayed simultaneously. All trials were shown at two different image contrasts. RESULTS: The authors were able to determine a histogram of scrolling speeds in frames per second. The scrolling speed of the naïve observers and the radiologists at the moment the signal was detected was measured at 25-30 fps. For the task chosen, the performance of the observers was not affected by the contrast or experience of the observer. However, the naïve observers exhibited a different pattern of scrolling than the radiologists, which included a tendency toward higher number of direction changes and number of slices viewed. CONCLUSIONS: The authors have determined a distribution of speeds for volumetric detection tasks. The speed at detection was higher than that subjectively estimated by the radiologists before the experiment. The speed information that was measured will be useful in the development of 3D model observers, especially anthropomorphic model observers which try to mimic human behavior.

Tracking the ghost of coevolution in specific plant-insect interactions : the case of the pollination system between the globeflower Trollius europaeus and seven european Chiastocheta flies

This study aims at understanding the evolutionary processes at work in specialized species interactions. Prom the macroevolutionary perspective, coevolution among specialized taxa was proposed to be one of the major processes generating biodiversity. We challenge this idea from the theoretical and practical perspective and through a literature review and show that the major hypotheses linking coevolutionary process with macroevolutionary patterns do not necessarily predict lineage co diversification and parallel speciation, limit¬ing the utility of the comparative phylogenenetic approach for investigating coevolution¬ary processes. We also point to the rarity of observed long-term coevolutionary dynamics among lineages and propose that coevolution rather occurs in shorter timescales, followed by ecological fitting. Prom the empirical point, we focus on the nursery pollination interaction between the European globeflower Trollius europaeus (Ranunculaceae) and its associated Chiastocheta flies (Anthomyiidae; Diptera) as a model system of evolution and maintenance of special¬ized interactions. The flies are obligate parasites of the seeds, but also pollinate the plant - it was thus proposed that both species are mutually dependent. Contrasting with the paradigm used for two decades of research on this system, we show that the female fitness component of the plant is similar in the populations with and without Chiastocheta. The plant is thus not exclusively dependent on the flies for reproduction. We discuss this result in the context of the factors responsible for the evolution of mutualistic systems. Understanding the evolution of a biological system requires understanding of its phylo- genetic context. Previous studies showed large mismatch between mtDNA phylogeny and morphological taxonomy in Chiastocheta. By using a large set of RAD-sequencing loci, we delineate the species limits that are congruent with morphology, and show that the discordance is best explained by the scenario of mitochondrial capture among fly species. Finally, we examine this system from a phylogeographic perspective, and identify the lack of congruence in spatial genetic structures of the plant and associated insects across their whole geographic range. The flies show lower numbers of spatial genetic groups than the plant, indicating that not all of the plant réfugia were shared by all the fly species or that the migration dynamics homogenized some of the groups. The incongruence in spatial genetic patterns indicates that fly migrations were largely independent from the genetic background of the plant, following rather a scenario of resource tracking, without the signature of coevolutionary process at this scale. Indeed, while the flies require the plant to survive climatic oscillations, the opposite is not true. Eventually, we show that there is no phylogenetic signal of spatial genetic structures, meaning that neither histories nor life- history traits are shared among closely related species and that species are characterized by unique trajectories of their genes. -- Cette étude vise à comprendre les processus évolutifs à l'oeuvre au sein d'interactions en¬tre espèces spécialisées. Du point de vue macroévolutif, la coévolution entre les taxons spécialisée a été considérée comme l'un des principaux processus générateur de biodiversité. Nous contestons cette idée du point de vue théorique et pratique à travers une revue de la littérature. Nous montrons que les hypothèses majeures reliant les processus coévolutifs avec les patterns de diversité au niveau macroévolutif ne prédisent pas nécessairement la co- diversification des lignées et leur spéciation parallèle, ce qui limite l'utilité de l'approche de phylogénie comparative pour étudier les processus coévolutifs . Nous rappelons également le peu d'exemples de dynamique coévolutive à long terme et proposons que la coévolution se produit plutôt dans des intervalles courts, suivis d'ajustements écologiques. Du point empirique, nous nous concentrons sur l'interaction de pollinisation entre le Trolle d'Europe Trollius europaeus (Ranunculaceae) et ses pollinisateurs associés, du genre Chiastocheta (Anthomyiidae; Diptera) en tant que système-modèle pour étudier l'évolution et le maintien des interactions spécialisées. Les mouches sont des parasites obligatoires des semences, mais pollinisent également la plante. Il a donc été proposé que les deux espèces soient mutuellement dépendantes. Contrastant avec le paradigme utilisé pendant deux décennies de recherche sur ce système, nous montrons, que la composante de fitness femelle de la plante est similaire dans les populations avec et sans Chiastocheta. La plante ne dépend donc pas exclusivement de son interaction avec les mouches pour la reproduction. Nous discutons de ce résultat dans le contexte des facteurs responsables de l'évolution des systèmes mutualistes. Comprendre l'évolution d'un système biologique nécessite la compréhension de son con- texte phylogénétique. Des études antérieures ont montré, chez Chiastocheta, de grandes disparités entre les phylogénies obtenues à partir d'ADN mitochondrial et la taxonomie basée sur les critères morphologiques. En utilisant un grand nombre de loci obtenus par RAD-sequencing, nous traçons les limites des espèces, qui concordent avec les car¬actéristiques morphologies, et montrons que la discordance s'explique en fait par un scénario de capture mitochondriale entre espèces de mouches. Enfin, nous examinons le système d'un point de vue phylogéographique, et identi¬fions les incohérences entre structurations génétiques spatiales de la plante et des insectes associés dans toute leur aire de distribution géographique. Les mouches présentent un nombre de groupes génétiques inférieur à la plante, indiquant que tous les refuges de la plante n'étaient pas partagés par toutes les espèces de mouches ou que les dynamiques migratoires ont homogénéisés certains des groupes chez les mouches. Les différences ob¬servées dans les patrons de structuration génétique spatiale indique que les migrations et dispersions des mouches ont été indépendantes du contexte génétique de la plante, et ces dernières ont été uniquement tributaires de la disponibilité des ressources, sans qu'il n'y ait de signature du processus de coévolution à cette échelle. En effet, tandis que les mouches ont besoin de la plante pour survivre aux oscillations climatiques, le contraire n'est pas exact. Finalement, nous montrons qu'il n'y a pas de signal phylogénétique des structurations génétiques spatiales chez les mouches, ce qui signifie que ni l'histoire, ni les traits d'histoire de vie ne sont partagés entre les espèces phylogénétiquement proches et que les espèces sont caractérisées par des trajectoires uniques de leurs gènes.

A Novel Approach to Major Surgery: Tracking Its Pathophysiologic Footprints.

BACKGROUND: To study the 'metabolic profile' of different surgical procedures and correlate it with pertinent surgical details and postoperative complications. METHODS: We conducted a prospective pilot study of 70 patients, ten for each of the seven following groups: (1) laparoscopic cholecystectomy, (2) incisional hernia repair, (3) laparoscopic and (4) open colon surgery, (5) upper gastrointestinal, (6) hepatic, and (7) pancreatic resections. Biochemical assessment included white blood cell count (WBC), C-reactive protein (CRP), glucose, triglycerides (TG), albumin (Alb), and pre-albumin (Pre-Alb), from the day before surgery until 5 days thereafter. Biological markers were compared for major versus minor surgery groups, which were defined on a clinical basis. Univariable analysis was used to identify risk factors for postoperative complications and p < 0.05 was the significance threshold. RESULTS: Common findings in all surgery groups were the acute inflammatory response (↑: WBC, CRP, ↓: TG, Alb, pre-Alb). Using cut-off values of 240 min operative (OR) time and 300 ml estimated blood loss (EBL), laparoscopic cholecystectomy, incisional hernia repair, and laparoscopic colectomy could be distinguished from open colectomy, upper gastrointestinal, liver, and pancreas resections. In a biochemical level, increased CRP and reduced postoperative Alb levels were highly discriminative of all types of 'major surgery.' Significant risk factors for postoperative complications were age, male gender, malignancy, longer OR time, higher blood loss, high CRP, and low Alb levels. CONCLUSIONS: Biochemically, CRP and Alb levels can help quantify the magnitude of the surgical trauma, which is correlated with adverse outcomes.

Abnormal Error Monitoring in Math-Anxious Individuals: Evidence from Error-Related Brain Potentials.

This study used event-related brain potentials to investigate whether math anxiety is related to abnormal error monitoring processing. Seventeen high math-anxious (HMA) and seventeen low math-anxious (LMA) individuals were presented with a numerical and a classical Stroop task. Groups did not differ in terms of trait or state anxiety. We found enhanced error-related negativity (ERN) in the HMA group when subjects committed an error on the numerical Stroop task, but not on the classical Stroop task. Groups did not differ in terms of the correct-related negativity component (CRN), the error positivity component (Pe), classical behavioral measures or post-error measures. The amplitude of the ERN was negatively related to participants" math anxiety scores, showing a more negative amplitude as the score increased. Moreover, using standardized low resolution electromagnetic tomography (sLORETA) we found greater activation of the insula in errors on a numerical task as compared to errors in a nonnumerical task only for the HMA group. The results were interpreted according to the motivational significance theory of the ERN.

Myelin-associated proteins block the migration of olfactory ensheathing cells: an in vitro study using single cell tracking and traction force microscopy

Newly generated olfactory receptor axons grow from the peripheral to the central nervous system aided by olfactory ensheathing cells (OECs). Thus, OEC transplantation has emerged as a promising therapy for spinal cord injuries and for other neural diseases. However, these cells do not present a uniform population, but, instead, a functionally heterogeneous population that exhibits a variety of responses including adhesion, repulsion and crossover during cell-cell and cell-matrix interactions. Some studies report that the migratory properties of OECs are compromised by inhibitory molecules and potentiated by chemical gradients. Here, we demonstrated that rodent OECs express all the components of the Nogo Receptor complex and that their migration is blocked by Myelin. Next, we used cell tracking and traction force microscopy to analyze OEC migration and its mechanical properties over Myelin. Our data relate the absence of traction force of OEC with lower migratory capacity, which correlates with changes in the F-Actin cytoskeleton and focal adhesion distribution. Lastly, OEC traction force and migratory capacity is enhanced after cell incubation with the Nogo Receptor inhibitor NEP1-40.