Search results for "Cognition"
showing 10 items of 7054 documents
The potential use of biomarkers as an adjunctive tool for staging bipolar disorder
2009
Recent data show that biomarkers differ in early and late-stage bipolar disorder (BD). Here we propose a model of staging for bipolar disorder that emphasizes the potential use of biomarkers for differentiating early and late-stage BD patients in the inter-episodic period. The proposed model includes a Latent phase: patients at "ultra-high-risk" for developing BD, characterized by a family history of BD, temperament traits, mood, and anxiety symptoms as well as genetic vulnerability for developing the disorder; Stage I: patients who return to their baseline level of functioning when mood episodes resolve; Stage II: biomarkers and functioning impairment are related to comorbidities or rapid-…
Experimental study of electrical FitzHugh-Nagumo neurons with modified excitability
2006
International audience; We present an electronical circuit modelling a FitzHugh-Nagumo neuron with a modified excitability. To characterize this basic cell, the bifurcation curves between stability with excitation threshold, bistability and oscillations are investigated. An electrical circuit is then proposed to realize a unidirectional coupling between two cells, mimicking an inter-neuron synaptic coupling. In such a master-slave configuration, we show experimentally how the coupling strength controls the dynamics of the slave neuron, leading to frequency locking, chaotic behavior and synchronization. These phenomena are then studied by phase map analysis. The architecture of a possible ne…
Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability.
2005
A nonlinear prediction method for investigating the dynamic interdependence between short length time series is presented. The method is a generalization to bivariate prediction of the univariate approach based on nearest neighbor local linear approximation. Given the input and output series x and y, the relationship between a pattern of samples of x and a synchronous sample of y was approximated with a linear polynomial whose coefficients were estimated from an equation system including the nearest neighbor patterns in x and the corresponding samples in y. To avoid overfitting and waste of data, the training and testing stages of the prediction were designed through a specific out-of-sampl…
Cognitive Responses to Populist Communication
2019
This chapter investigates the cognitive effects of different populist messages on blame attributions and stereotyping in the 15 countries participating in the study. It first gives an overview about whether respondents blamed politicians, the wealthy, immigrants, or ordinary people for the future economic decline described in the experimental stimulus. In addition, general stereotypical perceptions of those groups among respondents are presented. With respect to populist message effects, the analyses show that these were generally rather weak. But the analyses were also able to show that left-wing anti-out-group cues blaming ‘the rich’ and economic elites were most influential in this exper…
Blind deconvolution using TV regularization and Bregman iteration
2005
In this paper we formulate a new time dependent model for blind deconvolution based on a constrained variational model that uses the sum of the total variation norms of the signal and the kernel as a regularizing functional. We incorporate mass conservation and the nonnegativity of the kernel and the signal as additional constraints. We apply the idea of Bregman iterative regularization, first used for image restoration by Osher and colleagues [S.J. Osher, M. Burger, D. Goldfarb, J.J. Xu, and W. Yin, An iterated regularization method for total variation based on image restoration, UCLA CAM Report, 04-13, (2004)]. to recover finer scales. We also present an analytical study of the model disc…
Sparse Deconvolution Using Support Vector Machines
2008
Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise. Publicado
A sensor-data-based denoising framework for hyperspectral images
2015
Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term…
Predictors of outcome in acute ischemic cerebrovascular syndromes: The GIFA study
2006
Abstract Background Today it may be more useful to use the term acute ischemic cerebrovascular syndrome (AICS) to define a spectrum of disease ranging from TIA to stroke and that share a similar underlying pathophysiology: cerebral ischemia . The aim of this study is to evaluate the prognostic importance of some demographic, laboratory and clinical variables on the outcome in hospitalized patients with a discharge diagnosis suggestive of acute ischemic cerebral syndrome (AICS). Methods 17,377 Subjects were enrolled in the GIFA study, a multicenter survey of hospitalized older patients. 1878 Subjects with a main discharge diagnosis suggestive of acute ischemic cerebrovascular syndrome (AICS)…
The neuropeptide 26RFa (QRFP) is a key regulator of glucose homeostasis and its activity is markedly altered in obese/hyperglycemic mice
2019
International audience; Recent studies have shown that the hypothalamic neuropeptide 26RFa regulates glucose homeostasis by acting as an incretin, and increasing insulin sensitivity. In this study, we further characterized the role of the 26RFa/GPR103 peptidergic system in the global regulation of glucose homeostasis using a 26RFa receptor antagonist, and also assessed whether a dysfunction of the 26RFa/GPR103 system occurs in obese hyperglycemic mice. Firstly, we demonstrate that administration of the GPR103 antagonist reduces the global glucose-induced incretin effect and insulin sensitivity whereas, conversely, administration of exogenous 26RFa attenuates glucose-induced hyperglycemia. U…
A Multiple Local Models Approach to Accuracy Improvement in Continuous Glucose Monitoring
2011
Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance.CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic glo…