Variabilité de la fréquence cardiaque

Résumé

vignette|300px|La variabilité de la fréquence cardiaque visualisée par les changements de l'intervalle R-R. La variabilité de la fréquence cardiaque ou VFC (en anglais, heart rate variability ou HRV) est le degré de fluctuation de la durée des contractions du cœur, ou de l'intervalle entre deux contractions (intervalle R-R). Il s'agit d'une plus ou moins grande régularité, tandis que le rythme cardiaque s'intéresse à la fréquence des contractions. La VFC est en relation avec le système nerveux autonome. Elle constitue un biomarqueur pour suivre l'évolution de différents états de fatigue et de stress. Aperçu historique Les médecins et les scientifiques de la Grèce antique ont effectué la première mesure de la fréquence cardiaque. Cependant, ce n'est qu'après l'invention de la "montre à pouls du médecin" en 1707 que les changements de cette fréquence ont pu être évalués avec précision. En 1733, le révérend Stephen Hales a été le premier à constater que le pouls variait en f

Source officielle

https://fr.wikipedia.org/wiki/Variabilit%C3%A9_de_la_fr%C3%A9quence_cardiaque

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Multi-day mobility motifs: method and applications

Marija Kukic, Janody Pougala, Marc-Edouard Baptiste Grégoire Schultheiss

The sustainable turn in urban territories is manifold and must not rely solely on technologies to address energy efficiency. This research proposes a set of metrics as a preliminary work to further study day-to-day variability in activity-travel behaviors. On the one hand, locational variability addresses the spatial familiarity of individuals’ activity space. On the other hand, temporal variability addresses the structure of schedules and time-allocation. Daily activity structures are abstracted at the level of mobility motifs (unweighted directed-graphs) and measures of recurrent and frequent activity spaces are obtained by mean of centrography and longitudinal spatial analysis. The main objective of this paper is to expose and validate the methodology employed to capture temporal and locational patterns in multi-day activity-travel behaviors. The results are based on a fine-grained eight-week travel-diary collected in Switzerland in 2019, and focus on the statistical description and spatio-temporal meaning of four activity-travel behavior metrics: complexity, home shift, regularity and proximity. Combining these metrics with socio-demographics reveals important behavioral characteristics in terms of gender, household size, car-use and general accessibility.

2021

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Individuals differ in their physiological responsiveness to stressful challenges, and stress potentiates the development of many diseases. Heart rate variability (HRV), a measure of cardiac vagal break, is emerging as a strong index of physiological stress vulnerability. Thus, it is important to develop tools that identify predictive markers of individual differences in HRV responsiveness without exposing subjects to high stress. Here, using machine learning approaches, we show the strong predictive power of high-dimensional locomotor responses during novelty exploration to predict HRV responsiveness during stress exposure. Locomotor responses are collected in two ecologically valid virtual reality scenarios inspired by the animal literature and stress is elicited and measured in a third threatening virtual scenario. Our model's predictions generalize to other stressful challenges and outperforms other stress prediction instruments, such as anxiety questionnaires. Our study paves the way for the development of behavioral digital phenotyping tools for early detection of stress-vulnerable individuals.

2020

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The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow

Stefano Maximiliano Antonel, Luca Leonardo Bologna, Jean-Denis Georges Emile Courcol, Olivier Richard Hagens, Szabolcs Kali, Carmen Alina Lupascu, Henry Markram, Audrey Mercer, Rosanna Migliore, Michele Migliore, Eilif Benjamin Muller, Maurizio Ferdinando Pezzoli, Armando Romani, Christian Andreas Rössert, Felix Schürmann, Ying Shi, Werner Alfons Hilda Van Geit

Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.

PUBLIC LIBRARY SCIENCE2018

Chargement

The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow

PUBLIC LIBRARY SCIENCE2018