Auditory spatial deficits following hemispheric lesions : dissociation of explicit and implicit processing

Les déficits auditifs spatiaux se produisent fréquemment après une lésion hémisphérique ; un précédent case report suggérait que la capacité explicite à reconnaître des positions sonores, comme dans la localisation des sons, peut être atteinte alors que l'utilisation implicite d'indices sonores pour la reconnaissance d'objets sonores dans un environnement bruyant reste préservée. En testant systématiquement des patients avec lésion hémisphérique inaugurale, nous avons montré que (1) l'utilisation explicite et/ou implicite des indices sonores peut être perturbée ; (2) la dissociation entre l'atteinte de l'utilisation explicite des indices sonores versus une préservation de l'utilisation implicite de ces indices est assez fréquente ; et (3) différents types de déficits dans la localisation des sons peuvent être associés avec une utilisation implicite préservée de ces indices sonores. Conceptuellement, la dissociation entre l'utilisation explicite et implicite de ces indices sonores peut illustrer la dichotomie des deux voies du système auditif. Nos résultats parlent en faveur d'une évaluation systématique des fonctions auditives spatiales dans un contexte clinique, surtout quand l'adaptation à un environnement sonore est en jeu. De plus, des études systématiques sont nécessaires afin de mettre en lien les troubles de l'utilisation explicite versus implicite de ces indices sonores avec les difficultés à effectuer les activités de la vie quotidienne, afin d'élaborer des stratégies de réhabilitation appropriées et afin de s'assurer jusqu'à quel point l'utilisation explicite et implicite des indices spatiaux peut être rééduquée à la suite d'un dommage cérébral.

Temporal and spatial appearance of the membrane cytoskeleton and perineuronal nets in the rat neocortex.

Parvalbumin-immunoreactive interneurons are surrounded by perineuronal nets, containing molecules of the extracellular matrix (e.g. tenascin-R). Furthermore, they seem to have a special cytoskeleton composed of, among others, ankyrinR and beta Rspectrin. In the present developmental study we showed that the intracellular markers parvalbumin, ankyrinR and beta Rspectrin as well as Vicia Villosa agglutinin, an extracellular marker for perineuronal nets, appeared in the second postnatal week. In the third postnatal week, ankyrinR and beta R spectrin were present in the parvalbumin-positive interneurons. Tenascin-R appeared in a similar topographic distribution as the intracellular markers. The adult pattern was established upon the end of the fourth postnatal week. Our results indicate that cytoskeletal maturity maybe a prerequisite for the organization of perineuronal nets of extracellular matrix.

The spatial inequality of wage income in Barcelona Metropolitan Area

ABSTRACT The measure and estimation of income levels in Barcelona Metropolitan Area (BMA) goes back a long way. Using different approaches and focusing on different municipalities, there is a lot of work in the field. The majority of the literature has focused on the estimation of income levels using variables related to consumption. The empirical evidence on wage differentials has shown an important growth during 80’s and 90’s especially in United Kingdom and USA. Less is known on spatial distribution of inequality. This paper presents a new data set for analyzing spatial distribution of wage income. This data is obtained by matching Wage Structure Survey (WSS) with data from Census disaggregated by census tracts. In this way we have a unique data set with wage incomes for every census track for 36 municipalities belonging to BMA. We develop a descriptive analysis of spatial distribution, testing for spatial autocorrelation and use the family of Generalised Entropy Indices to measure inequality. Properties of the index allow us to decompose inequality into inter and intra-municipality measures. Since we have two cross-sectional data for WSS (1995-2002) we can also analyze the evolution of the inequality in this period of economic growth. Key words: spatial distribution of wages, spatial autocorrelation, inequality indices.

Contextual determinants of alcohol consumption changes and preventive alcohol policies: a 12-country European study in progress.

Beginning with France in the 1950s, alcohol consumption has decreased in Southern European countries with few or no preventive alcohol policy measures being implemented, while alcohol consumption has been increasing in Northern European countries where historically more restrictive alcohol control policies were in place, even though more recently they were loosened. At the same time, Central and Eastern Europe have shown an intermediate behavior. We propose that country-specific changes in alcohol consumption between 1960 and 2008 are explained by a combination of a number of factors: (1) preventive alcohol policies and (2) social, cultural, economic, and demographic determinants. This article describes the methodology of a research study designed to understand the complex interactions that have occurred throughout Europe over the past five decades. These include changes in alcohol consumption, drinking patterns and alcohol-related harm, and the actual determinants of such changes.

Structural Interactions in Spatial Panels

Until recently, much effort has been devoted to the estimation of panel data regression models without adequate attention being paid to the drivers of diffusion and interaction across cross section and spatial units. We discuss some new methodologies in this emerging area and demonstrate their use in measurement and inferences on cross section and spatial interactions. Specifically, we highlight the important distinction between spatial dependence driven by unobserved common factors and those based on a spatial weights matrix. We argue that, purely factor driven models of spatial dependence may be somewhat inadequate because of their connection with the exchangeability assumption. Limitations and potential enhancements of the existing methods are discussed, and several directions for new research are highlighted.

Spatial modelling of biodiversity at the community level

1. Statistical modelling is often used to relate sparse biological survey data to remotely derived environmental predictors, thereby providing a basis for predictively mapping biodiversity across an entire region of interest. The most popular strategy for such modelling has been to model distributions of individual species one at a time. Spatial modelling of biodiversity at the community level may, however, confer significant benefits for applications involving very large numbers of species, particularly if many of these species are recorded infrequently. 2. Community-level modelling combines data from multiple species and produces information on spatial pattern in the distribution of biodiversity at a collective community level instead of, or in addition to, the level of individual species. Spatial outputs from community-level modelling include predictive mapping of community types (groups of locations with similar species composition), species groups (groups of species with similar distributions), axes or gradients of compositional variation, levels of compositional dissimilarity between pairs of locations, and various macro-ecological properties (e.g. species richness). 3. Three broad modelling strategies can be used to generate these outputs: (i) 'assemble first, predict later', in which biological survey data are first classified, ordinated or aggregated to produce community-level entities or attributes that are then modelled in relation to environmental predictors; (ii) 'predict first, assemble later', in which individual species are modelled one at a time as a function of environmental variables, to produce a stack of species distribution maps that is then subjected to classification, ordination or aggregation; and (iii) 'assemble and predict together', in which all species are modelled simultaneously, within a single integrated modelling process. These strategies each have particular strengths and weaknesses, depending on the intended purpose of modelling and the type, quality and quantity of data involved. 4. Synthesis and applications. The potential benefits of modelling large multispecies data sets using community-level, as opposed to species-level, approaches include faster processing, increased power to detect shared patterns of environmental response across rarely recorded species, and enhanced capacity to synthesize complex data into a form more readily interpretable by scientists and decision-makers. Community-level modelling therefore deserves to be considered more often, and more widely, as a potential alternative or supplement to modelling individual species.

Hemispheric competence for auditory spatial representation.

Sound localization relies on the analysis of interaural time and intensity differences, as well as attenuation patterns by the outer ear. We investigated the relative contributions of interaural time and intensity difference cues to sound localization by testing 60 healthy subjects: 25 with focal left and 25 with focal right hemispheric brain damage. Group and single-case behavioural analyses, as well as anatomo-clinical correlations, confirmed that deficits were more frequent and much more severe after right than left hemispheric lesions and for the processing of interaural time than intensity difference cues. For spatial processing based on interaural time difference cues, different error types were evident in the individual data. Deficits in discriminating between neighbouring positions occurred in both hemispaces after focal right hemispheric brain damage, but were restricted to the contralesional hemispace after focal left hemispheric brain damage. Alloacusis (perceptual shifts across the midline) occurred only after focal right hemispheric brain damage and was associated with minor or severe deficits in position discrimination. During spatial processing based on interaural intensity cues, deficits were less severe in the right hemispheric brain damage than left hemispheric brain damage group and no alloacusis occurred. These results, matched to anatomical data, suggest the existence of a binaural sound localization system predominantly based on interaural time difference cues and primarily supported by the right hemisphere. More generally, our data suggest that two distinct mechanisms contribute to: (i) the precise computation of spatial coordinates allowing spatial comparison within the contralateral hemispace for the left hemisphere and the whole space for the right hemisphere; and (ii) the building up of global auditory spatial representations in right temporo-parietal cortices.

Where is the economics in spatial econometrics?

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

Regional productivity variation and the impact of public capital stock: an analysis with spatial interaction, with reference to Spain

In this paper we examine whether variations in the level of public capital across Spain‟s Provinces affected productivity levels over the period 1996-2005. The analysis is motivated by contemporary urban economics theory, involving a production function for the competitive sector of the economy („industry‟) which includes the level of composite services derived from „service‟ firms under monopolistic competition. The outcome is potentially increasing returns to scale resulting from pecuniary externalities deriving from internal increasing returns in the monopolistic competition sector. We extend the production function by also making (log) labour efficiency a function of (log) total public capital stock and (log) human capital stock, leading to a simple and empirically tractable reduced form linking productivity level to density of employment, human capital and public capital stock. The model is further extended to include technological externalities or spillovers across provinces. Using panel data methodology, we find significant elasticities for total capital stock and for human capital stock, and a significant impact for employment density. The finding that the effect of public capital is significantly different from zero, indicating that it has a direct effect even after controlling for employment density, is contrary to some of the earlier research findings which leave the question of the impact of public capital unresolved.

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.