Binding under Conflict Conditions: State-Space Analysis of Multivariate EEG Synchronization.

Real-world objects are often endowed with features that violate Gestalt principles. In our experiment, we examined the neural correlates of binding under conflict conditions in terms of the binding-by-synchronization hypothesis. We presented an ambiguous stimulus ("diamond illusion") to 12 observers. The display consisted of four oblique gratings drifting within circular apertures. Its interpretation fluctuates between bound ("diamond") and unbound (component gratings) percepts. To model a situation in which Gestalt-driven analysis contradicts the perceptually explicit bound interpretation, we modified the original diamond (OD) stimulus by speeding up one grating. Using OD and modified diamond (MD) stimuli, we managed to dissociate the neural correlates of Gestalt-related (OD vs. MD) and perception-related (bound vs. unbound) factors. Their interaction was expected to reveal the neural networks synchronized specifically in the conflict situation. The synchronization topography of EEG was analyzed with the multivariate S-estimator technique. We found that good Gestalt (OD vs. MD) was associated with a higher posterior synchronization in the beta-gamma band. The effect of perception manifested itself as reciprocal modulations over the posterior and anterior regions (theta/beta-gamma bands). Specifically, higher posterior and lower anterior synchronization supported the bound percept, and the opposite was true for the unbound percept. The interaction showed that binding under challenging perceptual conditions is sustained by enhanced parietal synchronization. We argue that this distributed pattern of synchronization relates to the processes of multistage integration ranging from early grouping operations in the visual areas to maintaining representations in the frontal networks of sensory memory.

Impaired early visual response modulations to spatial information in chronic schizophrenia.

Early visual processing stages have been demonstrated to be impaired in schizophrenia patients and their first-degree relatives. The amplitude and topography of the P1 component of the visual evoked potential (VEP) are both affected; the latter of which indicates alterations in active brain networks between populations. At least two issues remain unresolved. First, the specificity of this deficit (and suitability as an endophenotype) has yet to be established, with evidence for impaired P1 responses in other clinical populations. Second, it remains unknown whether schizophrenia patients exhibit intact functional modulation of the P1 VEP component; an aspect that may assist in distinguishing effects specific to schizophrenia. We applied electrical neuroimaging analyses to VEPs from chronic schizophrenia patients and healthy controls in response to variation in the parafoveal spatial extent of stimuli. Healthy controls demonstrated robust modulation of the VEP strength and topography as a function of the spatial extent of stimuli during the P1 component. By contrast, no such modulations were evident at early latencies in the responses from patients with schizophrenia. Source estimations localized these deficits to the left precuneus and medial inferior parietal cortex. These findings provide insights on potential underlying low-level impairments in schizophrenia.

Segmentation of brain structures in presence of a space-occupying lesion.

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented brain atlas in presence of a space-occupying lesion. Our approach is based on an a priori model of lesion growth (MLG) that assumes radial expansion from a seeding point and involves three steps: first, an affine registration bringing the atlas and the patient into global correspondence; then, the seeding of a synthetic tumor into the brain atlas providing a template for the lesion; finally, the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. The method was applied on two meningiomas inducing a pure displacement of the underlying brain structures, and segmentation accuracy of ventricles and basal ganglia was assessed. Results show that the segmented structures were consistent with the patient's anatomy and that the deformation accuracy of surrounding brain structures was highly dependent on the accurate placement of the tumor seeding point. Further improvements of the method will optimize the segmentation accuracy. Visualization of brain structures provides useful information for therapeutic consideration of space-occupying lesions, including surgical, radiosurgical, and radiotherapeutic planning, in order to increase treatment efficiency and prevent neurological damage.

Highly inducible synthesis of heterologous proteins in epithelial cells carrying a glucocorticoid-responsive vector.

A glucocorticoid-responsive vector is described which allows for the highly inducible expression of complementary DNAs (cDNAs) in stably transfected mammalian cell lines. This vector, pLK-neo, composed of a variant mouse mammary tumor virus long terminal repeat promoter, containing a hormone regulatory element, a Geneticin resistance-encoding gene in a simian virus 40 transcription unit, and a polylinker insertion site for heterologous cDNAs, was used to express the polymeric immunoglobulin (poly-Ig) receptor and the thymocyte marker, Thy-1, in Madin-Darby canine kidney (MDCK) cells and in murine fibroblast L cells. A high level of poly-Ig receptor or Thy-1 mRNA accumulation was observed in MDCK cells in response to dexamethasone with a parallel ten- to 200-fold increase in protein synthesis depending on the recombinant protein and the transfected cell clone.

Direct electrical stimulation using a battery-operated device for induction and modulation of colonic contractions in pigs.

Direct electrical stimulation of the colon offers a promising approach for the induction of propulsive colonic contractions by using an implantable device. The objective of this study was to assess the feasibility to induce colonic contractions using a commercially available battery-operated stimulator (maximum pulse width of 1 ms and maximum amplitude of 10 V). Three pairs of pacing electrodes were inserted into the cecal seromuscular layer of anesthetized pigs. During a first set of in vivo experiments conducted on six animals, a pacing protocol leading to cecum contractions was determined: stimulation bursts with 1 ms pulse width, 10 V amplitude (7-15 mA), 120 Hz frequency, and 30-s burst duration, repeated every 2-5 min. In a second testing phase, an evaluation of the pacing protocol was performed in four animals (120 stimulation bursts in total). By using the battery-operated stimulator, contractions of the cecum and movement of contents could be induced in 92% of all stimulations. A cecal shortening of about 30% and an average intraluminal pressure increase of 10.0 +/- 6.0 mmHg were observed.

Spin-labeling coronary MR angiography with steady-state free precession and radial k-space sampling: initial results in healthy volunteers.

The purpose of this study was to prospectively compare free-breathing navigator-gated cardiac-triggered three-dimensional steady-state free precession (SSFP) spin-labeling coronary magnetic resonance (MR) angiography performed by using Cartesian k-space sampling with that performed by using radial k-space sampling. A new dedicated placement of the two-dimensional selective labeling pulse and an individually adjusted labeling delay time approved by the institutional review board were used. In 14 volunteers (eight men, six women; mean age, 28.8 years) who gave informed consent, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel sharpness, vessel length, and subjective image quality were investigated. Differences between groups were analyzed with nonparametric tests (Wilcoxon, Pearson chi2). Radial imaging, as compared with Cartesian imaging, resulted in a significant reduction in the severity of motion artifacts, as well as an increase in SNR (26.9 vs 12.0, P < .05) in the coronary arteries and CNR (23.1 vs 8.8, P < .05) between the coronary arteries and the myocardium. A tendency toward improved vessel sharpness and vessel length was also found with radial imaging. Radial SSFP imaging is a promising technique for spin-labeling coronary MR angiography.

Modulation of the gap junction protein Connexin36 in neurons in a mouse model of transient focalcerebral ischemia

Intercellular communication is achieved at specialized regions of the plasma membrane by¦gap junctions. Gap junctions are transmembrane channels allowing direct contacts between¦the cytoplasms of neighboring cells. Each cell participates with one hemichannel, or¦connexon, made of six protein subunits named connexins. Thanks to these junctions, cells¦potentially share a pool of small molecules and metabolites, such as nucleotides, amino acids¦and second messengers.¦In an ischemic (i.e. non-perfused) territory of the brain, irreversible damage progresses over¦time from the centre of the most severe flow reduction to the periphery with less disturbed¦perfusion. Functionally impaired tissue can survive and recover if sufficient reperfusion is reestablished¦within a limited time period, which depends on various factors and mechanisms¦modulating the signaling pathways leading to cell death.¦Observations were made indicating the presence of electrical coupling between neurons which¦resist better to an ischemic insult. This electrical coupling is likely to be mediated by¦Connexin36 (Cx36), a neuron specific connexin isoform. It was demonstrated in the past that¦global ischemia induces a selective upregulation of Cx36 expression in regions with neurons¦that survive the insult whereas others undergo apoptosis and die. These observations raise the¦possibility that the neuronal gap junction Cx36 might play a role in the destiny of neurons¦after cerebral ischemia.¦The aim of this work was to characterize the regulation of Connexin36 in a mouse model of¦transient focal cerebral ischemia by immunofluorescence and Western blot analysis. Our¦immunofluorescence results suggest a specific increase in Cx36 in the penumbral region of¦the ischemic hemisphere.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.