• DocumentCode
    1754863
  • Title

    Characterization and Modeling of Muscle Sympathetic Nerve Spiking

  • Author

    Zaydens, Eugene ; Taylor, Joshua A. ; Cohen, Michael A. ; Eden, Uri T.

  • Author_Institution
    Dept. of Cognitive & Neural Syst., Boston Univ., Boston, MA, USA
  • Volume
    60
  • Issue
    10
  • fYear
    2013
  • fDate
    Oct. 2013
  • Firstpage
    2914
  • Lastpage
    2924
  • Abstract
    Muscle sympathetic nerve activity is a primary source of cardiovascular control in humans. Traditional analyses smooth away the fine temporal structure of the sympathetic recordings, limiting our understanding of sympathetic activation mechanisms. We use multifiber spike trains extracted from standard microneurography voltage trace to characterize the sympathetic spiking at rest and during sympathoexcitation. Our analysis corroborates known features of sympathetic activity, such as bursting behavior, cardiac rhythmicity, and long conduction delays. It also elucidates new features such as large heartbeat-to-heartbeat variability of firing rates and precise pattern of spiking within cardiac cycles. We find that at low firing rates, spikes occur uniformly throughout the cardiac cycle, but at higher rates, they tend to cluster in bursts around a particular latency. This latency shortens and the clusters tighten as the firing rates grow. Sympathoexcitation increases firing rates and shifts the burst latency later. Negative rate/latency correlation and the sympathoexcitatory shift suggest that spike production of the individual fibers contributes significantly to the control of the sympathetic bursts strength. Access to fine scale temporal information, more physiologically accurate description of nerve activity, and new hypotheses about the nervous outflow control establishes sympathetic spiking as a valuable tool for the cardiovascular research.
  • Keywords
    bioelectric potentials; cardiovascular system; muscle; neurophysiology; physiological models; regression analysis; bursting behavior; cardiac cycle; cardiac rhythmicity; cardiovascular control; conduction delay; fine scale temporal information; firing rate growth; heartbeat-to-heartbeat variability; latency correlation; microneurography voltage trace; multifiber spike train extraction; muscle sympathetic nerve spiking; nervous outflow control; sympathetic activation mechanism; sympathetic burst strength; sympathoexcitatory shift; Computational modeling; Data mining; Electric potential; Heart rate variability; Muscles; Nerve fibers; Resonant frequency; Point process; spike detection; statistical modeling; sympathetic nerve activity; Action Potentials; Adrenergic Fibers; Animals; Computer Simulation; Heart Conduction System; Humans; Models, Cardiovascular; Models, Neurological; Myocardium; Neural Conduction; Sympathetic Nervous System;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2013.2266342
  • Filename
    6523968