Search results for "Cognition"
showing 10 items of 7054 documents
Clinical-Instrumental patterns of neurodegeneration in Essential Tremor: A data-driven approach
2021
Abstract Introduction Essential Tremor (ET) is increasingly recognized as a complex disorder with additional clinical signs other than tremor. It is still unknown whether a unique pathophysiologic or neurodegenerative process underlies progression and prognosis of the disease. The aim of the study was to identify ET phenotypes through a clinical-instrumental data-driven approach and to characterize possible patterns of neurodegeneration. Methods ET patients were categorized using spatio-temporal and kinematic variables related to mobility and dynamic stability processed by motion transducers. Differences between the identified groups in clinical-demographic variables, neuropsychological per…
Neuronal Activity Patterns in the Developing Barrel Cortex
2017
International audience; The developing barrel cortex reveals a rich repertoire of neuronal activity patterns, which have been also found in other sensory neocortical areas and in other species including the somatosensory cortex of preterm human infants. The earliest stage is characterized by asyn-chronous, sparse single-cell firing at low frequencies. During the second stage neurons show correlated firing, which is initially mediated by electrical synapses and subsequently transforms into network bursts depending on chemical synapses. Activity patterns during this second stage are synchronous plateau assemblies, delta waves, spindle bursts and early gamma oscillations (EGOs). In newborn rod…
Odor-induced electrical and calcium signals from olfactory sensory neurons in situ
2018
Electrophysiological recording and optical imaging enable the characterization of membrane and odorant response properties of olfactory sensory neurons (OSNs) in the nasal neuroepithelium. Here we describe a method to record the responses of mammalian OSNs to odorant stimulations in an ex vivo preparation of intact olfactory epithelium. The responses of individual OSNs with defined odorant receptor types can be monitored via patch-clamp recording or calcium imaging.
Variable Ranking Feature Selection for the Identification of Nucleosome Related Sequences
2018
Several recent works have shown that K-mer sequence representation of a DNA sequence can be used for classification or identification of nucleosome positioning related sequences. This representation can be computationally expensive when k grows, making the complexity in spaces of exponential dimension. This issue effects significantly the classification task computed by a general machine learning algorithm used for the purpose of sequence classification. In this paper, we investigate the advantage offered by the so-called Variable Ranking Feature Selection method to select the most informative k − mers associated to a set of DNA sequences, for the final purpose of nucleosome/linker classifi…
Anti-PCSK9 treatment: is ultra-low low-density lipoprotein cholesterol always good?
2018
Anti-PCSK9 (proprotein convertase subtilisin kexin 9) monoclonal antibodies (Mab) are novel, potent lipid-lowering drugs. They demonstrated to improve the lipid profile in high cardiovascular risk patients. Anti-PCSK9 Mab inhibit the targeted low-density lipoprotein (LDL)-receptor degradation induced by PCSK9 protein and are able to reduce LDL cholesterol (LDL-C) levels on top of conventional lipid-lowering therapy. Though these drugs proved to be very safe in the short-term, little is known about the possible long-term effects, due to the short period of their marketing. The genetic low cholesterol syndromes (LCS) represent the natural models of the lipid-lowering anti-PCSK9 therapy, and a…
Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas
2018
In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region.
Discovering discriminative graph patterns from gene expression data
2016
We consider the problem of mining gene expression data in order to single out interesting features characterizing healthy/unhealthy samples of an input dataset. We present an approach based on a network model of the input gene expression data, where there is a labelled graph for each sample. To the best of our knowledge, this is the first attempt to build a different graph for each sample and, then, to have a database of graphs for representing a sample set. Our main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of "discriminative patterns" among graphs belonging to the two different sample sets. Differently from the other…
Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony
2016
In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relati…
Partitioned learning of deep Boltzmann machines for SNP data.
2016
Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…
The intrinsic combinatorial organization and information theoretic content of a sequence are correlated to the DNA encoded nucleosome organization of…
2015
Abstract Motivation: Thanks to research spanning nearly 30 years, two major models have emerged that account for nucleosome organization in chromatin: statistical and sequence specific. The first is based on elegant, easy to compute, closed-form mathematical formulas that make no assumptions of the physical and chemical properties of the underlying DNA sequence. Moreover, they need no training on the data for their computation. The latter is based on some sequence regularities but, as opposed to the statistical model, it lacks the same type of closed-form formulas that, in this case, should be based on the DNA sequence only. Results: We contribute to close this important methodological gap …