Search results for "Neuronal"

When the brain goes diving: glial oxidative metabolism may confer hypoxia tolerance to the seal brain.

2009

Deep diving mammals have developed strategies to cope with limited oxygen availability when submerged. These adaptations are associated with an increased neuronal hypoxia tolerance. Brain neurons of the hooded seal Cysto- phora cristata remain much longer active in hypoxic condi- tions than those of mice. To understand the cellular basis of neuronal hypoxia tolerance, we studied neuroglobin and cy- tochrome c in C. cristata brain. Neuroglobin, a respiratory protein typically found in vertebrate neurons, displays three unique amino acid substitutions in hooded seal. However, these substitutions unlikely contribute to a modulation of O2 affinity. Moreover, there is no significant difference i…

Corrigendum: Both attention and prediction are necessary for adaptive neuronal tuning in sensory processing

2017

Transcranial magnetic stimulation and neuroplasticity

1998

We review past results and present novel data to illustrate different ways in which TMS can be used to study neural plasticity. Procedural learning during the serial reaction time task (SRTT) is used as a model of neural plasticity to illustrate the applications of TMS. These different applications of TMS represent principles of use that we believe are applicable to studies of cognitive neuroscience in general and exemplify the great potential of TMS in the study of brain and behavior. We review the use of TMS for (1) cortical output mapping using focal, single-pulse TMS; (2) identification of the mechanisms underlying neuroplasticity using paired-pulse TMS techniques; (3) enhancement of th…

A Glimpse into Chromatin Organization and Nuclear Lamina Contribution in Neuronal Differentiation

2023

During embryonic development stem cells undergo the differentiation process so that they can specialise for different functions within the organism. Complex programs of gene transcription are crucial for this process to happen. Epigenetic modifications and the architecture of chromatin in the nucleus, by the formation of specific regions of active as well as inactive chromatin, allow the coordinated regulation of the genes for each cell fate. In this mini review, we discuss the current knowledge regarding the regulation of three-dimensional chromatin structure during neuronal differentiation. We also focus on the role played in neurogenesis by the nuclear lamina that ensures the tethering o…

Role of the colored noise in a FitzHugh-Nagumo system driven by a periodic signal

2007

During these last years the interest in neuronal dynamics increased. The study of this kind of system has been carried out by using the FitzHugh-Nagumo (FHN) model that is a simplified modification of the Hodgkin-Huxley model. Many interesting phenomena can be observed in the presence of fluctuations: modification of detection threshold by manipulation of noisy parameters (FHN model), noise-induced activation and coeherence resonance for suitable noise amplitude (absence of periodic signal), resonant activation for high periodic signals and noise reduction, intrinsic stochastic resonance (ISR) in Hodgkin-Huxley neuron and the enhancement of a weak signal by tuning the subthreshold intrinsic…

An Original Convolution Model to analyze Graph Network Distribution Features

2022

Modern Graph Theory is a newly emerging field that involves all of those approaches that study graphs differently from Classic Graph Theory. The main difference between Classic and Modern Graph Theory regards the analysis and the use of graph's structures (micro/macro). The former aims to solve tasks hosted on graph nodes, most of the time with no insight into the global graph structure, the latter aims to analyze and discover the most salient features characterizing a whole network of each graph, like degree distributions, hubs, clustering coefficient and network motifs. The activities carried out during the PhD period concerned, after a careful preliminary study on the applications of the…

Clinical manifestations of the anti-IgLON5 disease

2017

Objective:To report the presentation, main syndromes, human leukocyte antigen (HLA) association, and immunoglobulin G (IgG) subclass in the anti-IgLON5 disease: a disorder with parasomnias, sleep apnea, and IgLON5 antibodies.Methods:This was a retrospective clinical analysis of 22 patients. The IgG subclass was determined using reported techniques.Results:Patients' median age was 64 years (range 46–83). Symptoms that led to initial consultation included sleep problems (8 patients; 36%), gait abnormalities (8; 36%), bulbar dysfunction (3; 14%), chorea (2; 9%), and cognitive decline (1; 5%). By the time of diagnosis of the disorder, 4 syndromes were identified: (1) a sleep disorder with paras…

Redes Neuronales (2009/2010)

2009

A multi-scale approach for testing and detecting peaks in time series

2020

An approach is presented that combines a statistical test for peak detection with the estimation of peak positions in time series. Motivated by empirical observations in neuronal recordings, we aim at investigating peaks of different heights and widths. We use a moving window approach to compare the differences of estimated slope coefficients of local regression models. We combine multiple windows and use the global maximum of all different processes as a test statistic. After rejection, a multiple filter algorithm combines peak positions estimated from multiple windows. Analysing neuronal activity recorded in anaesthetized mice, the procedure could identify significant differences between …

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

2020

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…