Sex biased spatial strategies relying on the integration of multimodal cues in a rat model of schizophrenia: impairment in predicting future context?

Male and female Wistar rats were treated postnatally (PND 5-16) with BSO (l-buthionine-(S,R)-sulfoximine) to provide a rat model of schizophrenia based on transient glutathione deficit. In the watermaze, BSO-treated male rats perform very efficiently in conditions where a diversity of visual information is continuously available during orientation trajectories [1]. Our hypothesis is that the treatment impairs proactive strategies anticipating future sensory information, while supporting a tight visual adjustment on memorized snapshots, i.e. compensatory reactive strategies. To test this hypothesis, BSO rats' performance was assessed in two conditions using an 8-arm radial maze task: a semi-transparent maze with no available view on the environment from maze centre [2], and a modified 2-parallel maze known to induce a neglect of the parallel pair in normal rats [3-5]. Male rats, but not females, were affected by the BSO treatment. In the semi-transparent maze, BSO males expressed a higher error rate, especially in completing the maze after an interruption. In the 2-parallel maze shape, BSO males, unlike controls, expressed no neglect of the parallel arms. This second result was in accord with a reactive strategy using accurate memory images of the contextual environment instead of a representation based on integrating relative directions. These results are coherent with a treatment-induced deficit in proactive decision strategy based on multimodal cognitive maps, compensated by accurate reactive adaptations based on the memory of local configurations. Control females did not express an efficient proactive capacity in the semi-transparent maze, neither did they show the significant neglect of the parallel arms, which might have masked the BSO induced effect. Their reduced sensitivity to BSO treatment is discussed with regard to a sex biased basal cognitive style.

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.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

Optimization of spatial resolution for peripheral magnetic resonance angiography.

RATIONALE AND OBJECTIVES: To determine optimum spatial resolution when imaging peripheral arteries with magnetic resonance angiography (MRA). MATERIALS AND METHODS: Eight vessel diameters ranging from 1.0 to 8.0 mm were simulated in a vascular phantom. A total of 40 three-dimensional flash MRA sequences were acquired with incremental variations of fields of view, matrix size, and slice thickness. The accurately known eight diameters were combined pairwise to generate 22 "exact" degrees of stenosis ranging from 42% to 87%. Then, the diameters were measured in the MRA images by three independent observers and with quantitative angiography (QA) software and used to compute the degrees of stenosis corresponding to the 22 "exact" ones. The accuracy and reproducibility of vessel diameter measurements and stenosis calculations were assessed for vessel size ranging from 6 to 8 mm (iliac artery), 4 to 5 mm (femoro-popliteal arteries), and 1 to 3 mm (infrapopliteal arteries). Maximum pixel dimension and slice thickness to obtain a mean error in stenosis evaluation of less than 10% were determined by linear regression analysis. RESULTS: Mean errors on stenosis quantification were 8.8% +/- 6.3% for 6- to 8-mm vessels, 15.5% +/- 8.2% for 4- to 5-mm vessels, and 18.9% +/- 7.5% for 1- to 3-mm vessels. Mean errors on stenosis calculation were 12.3% +/- 8.2% for observers and 11.4% +/- 15.1% for QA software (P = .0342). To evaluate stenosis with a mean error of less than 10%, maximum pixel surface, the pixel size in the phase direction, and the slice thickness should be less than 1.56 mm2, 1.34 mm, 1.70 mm, respectively (voxel size 2.65 mm3) for 6- to 8-mm vessels; 1.31 mm2, 1.10 mm, 1.34 mm (voxel size 1.76 mm3), for 4- to 5-mm vessels; and 1.17 mm2, 0.90 mm, 0.9 mm (voxel size 1.05 mm3) for 1- to 3-mm vessels. CONCLUSION: Higher spatial resolution than currently used should be selected for imaging peripheral vessels.

Spatio-temporal Brain Dynamics Mediating Post-error Behavioral Adjustments.

Optimal behavior relies on flexible adaptation to environmental requirements, notably based on the detection of errors. The impact of error detection on subsequent behavior typically manifests as a slowing down of RTs following errors. Precisely how errors impact the processing of subsequent stimuli and in turn shape behavior remains unresolved. To address these questions, we used an auditory spatial go/no-go task where continual feedback informed participants of whether they were too slow. We contrasted auditory-evoked potentials to left-lateralized go and right no-go stimuli as a function of performance on the preceding go stimuli, generating a 2 × 2 design with "preceding performance" (fast hit [FH], slow hit [SH]) and stimulus type (go, no-go) as within-subject factors. SH trials yielded SH trials on the following trials more often than did FHs, supporting our assumption that SHs engaged effects similar to errors. Electrophysiologically, auditory-evoked potentials modulated topographically as a function of preceding performance 80-110 msec poststimulus onset and then as a function of stimulus type at 110-140 msec, indicative of changes in the underlying brain networks. Source estimations revealed a stronger activity of prefrontal regions to stimuli after successful than error trials, followed by a stronger response of parietal areas to the no-go than go stimuli. We interpret these results in terms of a shift from a fast automatic to a slow controlled form of inhibitory control induced by the detection of errors, manifesting during low-level integration of task-relevant features of subsequent stimuli, which in turn influences response speed.

Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

What matters for predicting spatial distributions of trees: techniques, data, or species' characteristics?

Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence-absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference.

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.

Olfactory traces and spatial learning in rats.

We conducted an experiment to assess the use of olfactory traces for spatial orientation in an open environment in rats, Rattus norvegicus. We trained rats to locate a food source at a fixed location from different starting points, in the presence or absence of visual information. A single food source was hidden in an array of 19 petri dishes regularly arranged in an open-field arena. Rats were trained to locate the food source either in white light (with full access to distant visuospatial information) or in darkness (without any visual information). In both cases, the goal was in a fixed location relative to the spatial frame of reference. The results of this experiment revealed that the presence of noncontrolled olfactory traces coherent with the spatial frame of reference enables rats to locate a unique position as accurately in darkness as with full access to visuospatial information. We hypothesize that the olfactory traces complement the use of other orientation mechanisms, such as path integration or the reliance on visuospatial information. This experiment demonstrates that rats can rely on olfactory traces for accurate orientation, and raises questions about the establishment of such traces in the absence of any other orientation mechanism. Copyright 1998 The Association for the Study of Animal Behaviour.

Modularity of auditory spatial representations

Abstract : Auditory spatial functions are of crucial importance in everyday life. Determining the origin of sound sources in space plays a key role in a variety of tasks including orientation of attention, disentangling of complex acoustic patterns reaching our ears in noisy environments. Following brain damage, auditory spatial processing can be disrupted, resulting in severe handicaps. Complaints of patients with sound localization deficits include the inability to locate their crying child or being over-loaded by sounds in crowded public places. Yet, the brain bears a large capacity for reorganization following damage and/or learning. This phenomenon is referred as plasticity and is believed to underlie post-lesional functional recovery as well as learning-induced improvement. The aim of this thesis was to investigate the organization and plasticity of different aspects of auditory spatial functions. Overall, we report the outcomes of three studies: In the study entitled "Learning-induced plasticity in auditory spatial representations" (Spierer et al., 2007b), we focused on the neurophysiological and behavioral changes induced by auditory spatial training in healthy subjects. We found that relatively brief auditory spatial discrimination training improves performance and modifies the cortical representation of the trained sound locations, suggesting that cortical auditory representations of space are dynamic and subject to rapid reorganization. In the same study, we tested the generalization and persistence of training effects over time, as these are two determining factors in the development of neurorehabilitative intervention. In "The path to success in auditory spatial discrimination" (Spierer et al., 2007c), we investigated the neurophysiological correlates of successful spatial discrimination and contribute to the modeling of the anatomo-functional organization of auditory spatial processing in healthy subjects. We showed that discrimination accuracy depends on superior temporal plane (STP) activity in response to the first sound of a pair of stimuli. Our data support a model wherein refinement of spatial representations occurs within the STP and that interactions with parietal structures allow for transformations into coordinate frames that are required for higher-order computations including absolute localization of sound sources. In "Extinction of auditory stimuli in hemineglect: space versus ear" (Spierer et al., 2007a), we investigated auditory attentional deficits in brain-damaged patients. This work provides insight into the auditory neglect syndrome and its relation with neglect symptoms within the visual modality. Apart from contributing to a basic understanding of the cortical mechanisms underlying auditory spatial functions, the outcomes of the studies also contribute to develop neurorehabilitation strategies, which are currently being tested in clinical populations.

High-resolution spatial mapping of changes in the neurochemical profile after focal ischemia in mice.

After ischemic stroke, the ischemic damage to brain tissue evolves over time and with an uneven spatial distribution. Early irreversible changes occur in the ischemic core, whereas, in the penumbra, which receives more collateral blood flow, the damage is more mild and delayed. A better characterization of the penumbra, irreversibly damaged and healthy tissues is needed to understand the mechanisms involved in tissue death. MRSI is a powerful tool for this task if the scan time can be decreased whilst maintaining high sensitivity. Therefore, we made improvements to a (1) H MRSI protocol to study middle cerebral artery occlusion in mice. The spatial distribution of changes in the neurochemical profile was investigated, with an effective spatial resolution of 1.4 μL, applying the protocol on a 14.1-T magnet. The acquired maps included the difficult-to-separate glutamate and glutamine resonances and, to our knowledge, the first mapping of metabolites γ-aminobutyric acid and glutathione in vivo, within a metabolite measurement time of 45 min. The maps were in excellent agreement with findings from single-voxel spectroscopy and offer spatial information at a scan time acceptable for most animal models. The metabolites measured differed with respect to the temporal evolution of their concentrations and the localization of these changes. Specifically, lactate and N-acetylaspartate concentration changes largely overlapped with the T(2) -hyperintense region visualized with MRI, whereas changes in cholines and glutathione affected the entire middle cerebral artery territory. Glutamine maps showed elevated levels in the ischemic striatum until 8 h after reperfusion, and until 24 h in cortical tissue, indicating differences in excitotoxic effects and secondary energy failure in these tissue types. Copyright © 2011 John Wiley & Sons, Ltd.

Perinatal choline supplementation reveals a dissociation between two types of spatial strategies.

Choline supplementation improving memory functions in rodents is assumed to increase the synthesis and release of acetylcholine in the brain. We have found that a combined pre- and postnatal supplementation results in long-lasting facilitation of spatial memory in juvenile rats when training was conducted in presence of a local salient cue. The present work was aimed at analysing the effects of peri- and postnatal choline supplementation on spatial abilities of naive adult rats. Rats given a perinatal choline supplementation were trained in various cued procedures of the Morris navigation task when aged 5 months. The treatment had a specific effect of reducing the escape latency of the rats when the platform was at a fixed position in space and surrounded by a suspended cue. This effect was associated with an increased spatial bias when the cue and platform were removed. In this condition, the control rats showed impaired spatial discrimination following the removal of the target cue, most likely due to an overshadowing of the distant environmental cues. This impairment was not observed in the treated rats. Further training with the suspended cue at unpredictable places in the pool revealed longer escape latencies in the control than in the treated rats suggesting that this procedure induced a selective perturbation of the normal but not of the treated rats. A special probe trial with the cue at an irrelevant position and no escape platform revealed a significant bias of the control rats toward the cue and of the treated rats toward the uncued spatial escape position. This behavioural dissociation suggests that a salient cue associated with the target induces an alternative "non spatial" guidance strategy in normal rats, with the risk of overshadowing of the more distant spatial cues. In this condition, the choline supplementation facilities a spatial reliance on the cue, that is an overall facilitation of learning a set of spatial relations between several visual cues. As a consequence, the improved escape in presence of the cue is associated with a stronger memory of the spatial position following disappearance of the cue. This and previous observations suggest that a specific spatial attention process relies on the buffering of highly salient visual cues.to facilitate integration of their relative position in the environment.