Neural substrates of social emotion regulation: a FMRI study on imitation and expressive suppression to dynamic facial signals.

Emotion regulation is crucial for successfully engaging in social interactions. Yet, little is known about the neural mechanisms controlling behavioral responses to emotional expressions perceived in the face of other people, which constitute a key element of interpersonal communication. Here, we investigated brain systems involved in social emotion perception and regulation, using functional magnetic resonance imaging (fMRI) in 20 healthy participants. The latter saw dynamic facial expressions of either happiness or sadness, and were asked to either imitate the expression or to suppress any expression on their own face (in addition to a gender judgment control task). fMRI results revealed higher activity in regions associated with emotion (e.g., the insula), motor function (e.g., motor cortex), and theory of mind (e.g., [pre]cuneus) during imitation. Activity in dorsal cingulate cortex was also increased during imitation, possibly reflecting greater action monitoring or conflict with own feeling states. In addition, premotor regions were more strongly activated during both imitation and suppression, suggesting a recruitment of motor control for both the production and inhibition of emotion expressions. Expressive suppression (eSUP) produced increases in dorsolateral and lateral prefrontal cortex typically related to cognitive control. These results suggest that voluntary imitation and eSUP modulate brain responses to emotional signals perceived from faces, by up- and down-regulating activity in distributed subcortical and cortical networks that are particularly involved in emotion, action monitoring, and cognitive control.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

Contribution of neural networks to Alzheimer disease's progression.

The neuropathology of Alzheimer disease is characterized by senile plaques, neurofibrillary tangles and cell death. These hallmarks develop according to the differential vulnerability of brain networks, senile plaques accumulating preferentially in the associative cortical areas and neurofibrillary tangles in the entorhinal cortex and the hippocampus. We suggest that the main aetiological hypotheses such as the beta-amyloid cascade hypothesis or its variant, the synaptic beta-amyloid hypothesis, will have to consider neural networks not just as targets of degenerative processes but also as contributors of the disease's progression and of its phenotype. Three domains of research are highlighted in this review. First, the cerebral reserve and the redundancy of the network's elements are related to brain vulnerability. Indeed, an enriched environment appears to increase the cerebral reserve as well as the threshold of disease's onset. Second, disease's progression and memory performance cannot be explained by synaptic or neuronal loss only, but also by the presence of compensatory mechanisms, such as synaptic scaling, at the microcircuit level. Third, some phenotypes of Alzheimer disease, such as hallucinations, appear to be related to progressive dysfunction of neural networks as a result, for instance, of a decreased signal to noise ratio, involving a diminished activity of the cholinergic system. Overall, converging results from studies of biological as well as artificial neural networks lead to the conclusion that changes in neural networks contribute strongly to Alzheimer disease's progression.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Neural plasticity associated with recently versus often heard objects.

In natural settings the same sound source is often heard repeatedly, with variations in spectro-temporal and spatial characteristics. We investigated how such repetitions influence sound representations and in particular how auditory cortices keep track of recently vs. often heard objects. A set of 40 environmental sounds was presented twice, i.e. as prime and as repeat, while subjects categorized the corresponding sound sources as living vs. non-living. Electrical neuroimaging analyses were applied to auditory evoked potentials (AEPs) comparing primes vs. repeats (effect of presentation) and the four experimental sections. Dynamic analysis of distributed source estimations revealed i) a significant main effect of presentation within the left temporal convexity at 164-215ms post-stimulus onset; and ii) a significant main effect of section in the right temporo-parietal junction at 166-213ms. A 3-way repeated measures ANOVA (hemisphere×presentation×section) applied to neural activity of the above clusters during the common time window confirmed the specificity of the left hemisphere for the effect of presentation, but not that of the right hemisphere for the effect of section. In conclusion, spatio-temporal dynamics of neural activity encode the temporal history of exposure to sound objects. Rapidly occurring plastic changes within the semantic representations of the left hemisphere keep track of objects heard a few seconds before, independent of the more general sound exposure history. Progressively occurring and more long-lasting plastic changes occurring predominantly within right hemispheric networks, which are known to code for perceptual, semantic and spatial aspects of sound objects, keep track of multiple exposures.

Brain glucose sensing and neural regulation of insulin and glucagon secretion.

Glucose homeostasis requires the tight regulation of glucose utilization by liver, muscle and white or brown fat, and glucose production and release in the blood by liver. The major goal of maintaining glycemia at ∼ 5 mM is to ensure a sufficient flux of glucose to the brain, which depends mostly on this nutrient as a source of metabolic energy. This homeostatic process is controlled by hormones, mainly glucagon and insulin, and by autonomic nervous activities that control the metabolic state of liver, muscle and fat tissue but also the secretory activity of the endocrine pancreas. Activation or inhibition of the sympathetic or parasympathetic branches of the autonomic nervous systems are controlled by glucose-excited or glucose-inhibited neurons located at different anatomical sites, mainly in the brainstem and the hypothalamus. Activation of these neurons by hyper- or hypoglycemia represents a critical aspect of the control of glucose homeostasis, and loss of glucose sensing by these cells as well as by pancreatic β-cells is a hallmark of type 2 diabetes. In this article, aspects of the brain-endocrine pancreas axis are reviewed, highlighting the importance of central glucose sensing in the control of counterregulation to hypoglycemia but also mentioning the role of the neural control in β-cell mass and function. Overall, the conclusions of these studies is that impaired glucose homeostasis, such as associated with type 2 diabetes, but also defective counterregulation to hypoglycemia, may be caused by initial defects in glucose sensing.

Performance of noninvasive ventilation algorithms on ICU ventilators during pressure support: a clinical study.

To evaluate the impact of noninvasive ventilation (NIV) algorithms available on intensive care unit ventilators on the incidence of patient-ventilator asynchrony in patients receiving NIV for acute respiratory failure. Prospective multicenter randomized cross-over study. Intensive care units in three university hospitals. Patients consecutively admitted to the ICU and treated by NIV with an ICU ventilator were included. Airway pressure, flow and surface diaphragmatic electromyography were recorded continuously during two 30-min periods, with the NIV (NIV+) or without the NIV algorithm (NIV0). Asynchrony events, the asynchrony index (AI) and a specific asynchrony index influenced by leaks (AIleaks) were determined from tracing analysis. Sixty-five patients were included. With and without the NIV algorithm, respectively, auto-triggering was present in 14 (22%) and 10 (15%) patients, ineffective breaths in 15 (23%) and 5 (8%) (p = 0.004), late cycling in 11 (17%) and 5 (8%) (p = 0.003), premature cycling in 22 (34%) and 21 (32%), and double triggering in 3 (5%) and 6 (9%). The mean number of asynchronies influenced by leaks was significantly reduced by the NIV algorithm (p < 0.05). A significant correlation was found between the magnitude of leaks and AIleaks when the NIV algorithm was not activated (p = 0.03). The global AI remained unchanged, mainly because on some ventilators with the NIV algorithm premature cycling occurs. In acute respiratory failure, NIV algorithms provided by ICU ventilators can reduce the incidence of asynchronies because of leaks, thus confirming bench test results, but some of these algorithms can generate premature cycling.

The neural basis for writing from dictation in the temporoparietal cortex.

Cortical electrical stimulation mapping was used to study neural substrates of the function of writing in the temporoparietal cortex. We identified the sites involved in oral language (sentence reading and naming) and writing from dictation, in order to spare these areas during removal of brain tumours in 30 patients (23 in the left, and 7 in the right hemisphere). Electrostimulation of the cortex impaired writing ability in 62 restricted cortical areas (.25 cm2). These were found in left temporoparietal lobes and were mostly located along the superior temporal gyrus (Brodmann's areas 22 and 42). Stimulation of right temporoparietal lobes in right-handed patients produced no writing impairments. However there was a high variability of location between individuals. Stimulation resulted in combined symptoms (affecting oral language and writing) in fourteen patients, whereas in eight other patients, stimulation-induced pure agraphia symptoms with no oral language disturbance in twelve of the identified areas. Each detected area affected writing in a different way. We detected the various different stages of the auditory-to-motor pathway of writing from dictation: either through comprehension of the dictated sentences (word deafness areas), lexico-semantic retrieval, or phonologic processing. In group analysis, barycentres of all different types of writing interferences reveal a hierarchical functional organization along the superior temporal gyrus from initial word recognition to lexico-semantic and phonologic processes along the ventral and the dorsal comprehension pathways, supporting the previously described auditory-to-motor process. The left posterior Sylvian region supports different aspects of writing function that are extremely specialized and localized, sometimes being segregated in a way that could account for the occurrence of pure agraphia that has long-been described in cases of damage to this region.

Ochratoxin A at nanomolar concentration perturbs the homeostasis of neural stem cells in highly differentiated but not in immature three-dimensional brain cell cultures.

Ochratoxin A (OTA), a fungal contaminant of basic food commodities, is known to be highly cytotoxic, but the pathways underlying adverse effects at subcytotoxic concentrations remain to be elucidated. Recent reports indicate that OTA affects cell cycle regulation. Therefore, 3D brain cell cultures were used to study OTA effects on mitotically active neural stem/progenitor cells, comparing highly differentiated cultures with their immature counterparts. Changes in the rate of DNA synthesis were related to early changes in the mRNA expression of neural stem/progenitor cell markers. OTA at 10nM, a concentration below the cytotoxic level, was ineffective in immature cultures, whereas in mature cultures it significantly decreased the rate of DNA synthesis together with the mRNA expression of key transcriptional regulators such as Sox2, Mash1, Hes5, and Gli1; the cell cycle activator cyclin D2; the phenotypic markers nestin, doublecortin, and PDGFRα. These effects were largely prevented by Sonic hedgehog (Shh) peptide (500ngml(-1)) administration, indicating that OTA impaired the Shh pathway and the Sox2 regulatory transcription factor critical for stem cell self-renewal. Similar adverse effects of OTA in vivo might perturb the regulation of stem cell proliferation in the adult brain and in other organs exhibiting homeostatic and/or regenerative cell proliferation.

Dysregulated activation of the CXCR4/CXCL12 Axisand its role in the malignant phenotype of neural crest-derived neuroblastoma tumours

Résumé: Le neuroblastome (NB) est un néoplasme dévastateur de la petite enfance, pour lequel il n'existe pas encore de traitement efficace. Les chimiokines et leurs récepteurs ont été impliqués dans la croissance des tumeurs et la formation de métastases, et en particulier, il a été rapporté que l'axe CXCR4/CXCL12 dirigeait le guidage, ainsi que l'invasion des cellules cancéreuses vers des organes spécifiques. Notre étude avait pour objectif d'analyser le rôle de CxCR4 exogène dans le comportement malin du NB, en étudiant la croissance des cellules tumorales, leur capacité de survie, de migration et d'invasion in vitro et en validant ces résultats grâce à un modèle orthotopique murin de la progression tumorale du NB in vivo. La surexpression de CXCR4 dans les cellules faiblement métastatiques IGR-NB8 n'exprimant pas CXCR4, a augmenté la mobilité des cellules vers CXCL12 in vitro. De plus, les cellules surexprimant CXCR4 ont été moins affectées par la privation de sérum que les cellules contrôles. Le volume des tumeurs chez les animaux greffés de manière orthotopique avec les cellules NB8-CXCR4-C3 était significativement plus élevé que celui des tumeurs issues des cellules contrôles NB8-E6 au moment du sacrifice des animaux. Cependant, aucune induction des métastases n'a été observée. La lignée cellulaire IGR-N91, aux propriétés invasives et métastatiques in vivo, exprime constitutivement des quantités modérées de CXCR4. La surexpression du récepteur dans cette lignée a accéléré la croissance tumorale in vivo, mais n'a pas augmenté pas l'occurrence des métastases. Les cellules IGR-N91, dans lesquelles l'expression de CXCR4 a été éteinte, suite à l'introduction de shRNA stable contre CXCR4, a présenté une croissance cellulaire plus lente, in vitro et in vivo. Afin d'identifier les gènes et les voies de signalisation impliqués dans les effets dépendants de CXCR4-CXCL12 dans le NB, des analyses du profil d'expression des gènes ont été effectuées sur les lignées cellulaires transfectées ou non (contrôle). Trois clones contrôles ont été comparés à 3 clones surexprimant CXCR4 pour chacune des lignées (IGR-NB8 et IGR-N91). Les analyses biostatiques ont identifié 10 gènes induits, dont CXCR4, et 31 gènes réprimés, communs entre tous les clones surexprimant CXCR4. Ces observations démontrent que la surexpression de CXCR4 dans le NB stimule la croissance, la survie et la migration chémotactique des cellules tumorales, mais est insuffisante pour induire ou augmenter leurs capacités invasives et métastatiques. Les voies de signalisation activées suite à la surexpression de CXCR4 et identifiées à travers le profil global de l'expression des gènes pourraient être des cibles intéressantes pour le développement de drogues capables d'inhiber la croissance tumorale. Abstact: Neuroblastoma (NB) is a devastating childhood neoplasm for which there is not yet an efficient treatment. Chemokines and their receptors have been involved in tumour growth and metastasis, and in particular the CXCR4/CXCL12 axis has been reported to mediate organ-specific cancer cells homing and invasion. The purpose of the study was to investigate the role of ectopic CXCR4 in the malignant behaviour of NB by studying tumour cell growth, survival, migration, and invasion in vitro and by validating these results using a murine orthotopic model of NB tumour progression in vivo. CXCR4 overexpression in the low metastatic, CXCR4-negative IGR-NB8 cells resulted in CXCL12-mediated chemotaxis in vitro. Furthermore, CXCR4 overexpressing cells were less affected by serum deprivation than mock-transduced cells. In vivo studies revealed that, at sacrifice, volumes of tumours developing in mice with orthotopically implanted NB8-CXCR4-C3 cells, were significantly increased compared to NB8-E6 control tumours. However, no induction of metastases was observed. The in vivo invasive and metastatic cell line IGR-N91 cell line constitutively expresses moderate levels of CXCR4. Overexpression of CXCR4 enhanced in vivo tumour growth but did not increase the occurrence of metastases. IGR-N91 cells where CXCR4 has been knocked-down by stable shRNA grew slower in vitro and in vivo. To identify genes and pathways involved in the CXCR4/CXCL12-mediated effects in NB expression, profiles analyses (Affymetrix) were performed on transduced and control cell lines. Three mock-transduced clones were compared to three CXCR4 overexpressing clones of either cell line IGR-NB8 and IGR-N91. Biostatistical analysis identified 10 commonly upregulated genes (including CXCR4) and 31 downregulated genes common to all CXCR4 overexpressing clones. These observations demonstrate that overexpression of CXCR4 in NB stimulates tumour cell growth, survival, and chemotactic migration but is not sufficient to induce or enhance invasive and metastatic capacities. Activated pathways upon CXCR4 overexpression, identified through global gene expression profiling may be interesting targets for drugs inhibiting tumour growth.

Decoding auditory EEG responses in healthy and clinical populations: A comparative study.

BACKGROUND: Analyses of brain responses to external stimuli are typically based on the means computed across conditions. However in many cognitive and clinical applications, taking into account their variability across trials has turned out to be statistically more sensitive than comparing their means. NEW METHOD: In this study we present a novel implementation of a single-trial topographic analysis (STTA) for discriminating auditory evoked potentials at predefined time-windows. This analysis has been previously introduced for extracting spatio-temporal features at the level of the whole neural response. Adapting the STTA on specific time windows is an essential step for comparing its performance to other time-window based algorithms. RESULTS: We analyzed responses to standard vs. deviant sounds and showed that the new implementation of the STTA gives above-chance decoding results in all subjects (in comparison to 7 out of 11 with the original method). In comatose patients, the improvement of the decoding performance was even more pronounced than in healthy controls and doubled the number of significant results. COMPARISON WITH EXISTING METHOD(S): We compared the results obtained with the new STTA to those based on a logistic regression in healthy controls and patients. We showed that the first of these two comparisons provided a better performance of the logistic regression; however only the new STTA provided significant results in comatose patients at group level. CONCLUSIONS: Our results provide quantitative evidence that a systematic investigation of the accuracy of established methods in normal and clinical population is an essential step for optimizing decoding performance.