Fifty years of microneurography: learning the language of the peripheral sympathetic nervous system in humans

Abstract

As a primary component of homeostasis, the sympathetic nervous system enables rapid adjustments to stress through its ability to communicate messages among organs and cause targeted and graded end organ responses. Key in this communication model is the pattern of neural signals emanating from the central to peripheral components of the sympathetic nervous system. But what is the communication strategy employed in peripheral sympathetic nerve activity (SNA)? Can we develop and interpret the system of coding in SNA that improves our understanding of the neural control of the circulation? In 1968, Hagbarth and Vallbo (Hagbarth KE, Vallbo AB. Acta Physiol Scand 74: 96-108, 1968) reported the first use of microneurographic methods to record sympathetic discharges in peripheral nerves of conscious humans, allowing quantification of SNA at rest and sympathetic responsiveness to physiological stressors in health and disease. This technique also has enabled a growing investigation into the coding patterns within, and cardiovascular outcomes associated with, postganglionic SNA. This review outlines how results obtained by microneurographic means have improved our understanding of SNA outflow patterns at the action potential level, focusing on SNA directed toward skeletal muscle in conscious humans.

Keywords: microneurography; muscle sympathetic nerve activity; neural control of the circulatoin; recruitment strategies; reflex cardiovascular control.

Figures

Fig. 1.

Major anatomical segments contributing to discharge patterns in the postganglionic sympathetic neural signal and their interpretation. Adapted by permission from Jänig and Häbler (2003).

Fig. 2.

Cross section of right human common peroneal nerve. A: nerve section used as a negative control. B: positively stained nerve section. Sympathetic axons are represented by brown regions within the nerve fascicles. Myelinated fibers are represented by the blue ovals in the fascicle. C and D: enlargements from B of a positively stained nerve fascicles demonstrating the differences in arrangement between fascicles. Right: enlargement of segment from C with the tip of a microelectrode overlaid identifying the small (calculated) region of action potential detection at the electrode’s tip. From Tompkins et al. (2013).

Fig. 3.

Processing stages for common detection of sympathetic nerve activity to achieve the integrated neurogram (left) highlight bursts of neural activity, and the process of locating, extracting, and binning postganglionic sympathetic action potentials (APs) (right). The primary processing of the integrated neurogram involves band-pass filtering (~700–2,000 Hz), full-wave rectification, and integration with a 0.1-s time constant (some use root-mean square processing (Delius et al. 1972a; Hagbarth and Vallbo 1968; Vallbo et al. 1979; Vallbo et al. 2004). This processing sequence exposes the variations in interburst period and burst size (left). A modified continuous wavelet transform is an example of an approach to locate the exact position of each AP for extraction, clustering, and binning. See Salmanpour et al. (2010) for details.

Fig. 4.

Representative data from a single individual illustrating the pattern of action potential (AP) occurrence within each burst (organized from smallest to largest integrated burst size in the top panel) as a function of their cluster (illustrated along the left). Cluster refers to all APs of similar morphology (see Fig. 3 legend). Data were collected in a healthy individual on going from baseline to the break point of a maximal lung volume apnea (performed by elite free diver). The range of integrated sympathetic bursts are ordered by burst size as a percentage of baseline. Dashed lines represent the means and standard deviations of integrated burst sizes at baseline. Each vertical line (i.e., spike) represents the occurrence of postganglionic sympathetic APs as a function of integrated burst in which they occurred. Clusters of larger APs are predominately recruited at higher levels of sympathetic activation. The APs present at baseline (clusters 1–8) generally express an increase in firing probability (per burst) as the reflex state endures toward the apnea break point, an example of rate coding. However, the emergence of new APs (clusters 9–15) during the reflex state illustrate recruitment of larger APs that were not firing at baseline. From Steinback et al. (2010).

Fig. 5.

A schematic summary of data from various studies outlining the relationship between action potential (AP) size and its conduction latency, and how this fundamental pattern shifts to faster conduction during volitional apneas, Valsalva maneuvers (VM), and fatiguing isometric handgrip (IHG) but not necessarily during lower body negative pressure (LBNP) or a period of post exercise circulatory occlusion (PECO) following fatiguing IHG. Cluster latency is determined as the time from the R-wave of the electrocardiogram for the heart beat immediately following the generation of the burst. The latency for all APs in each cluster within each burst can be quantified and averaged to determine the mean cluster latency. Data from Badrov et al. (2015, 2016b); Salmanpour et al. (2011a, 2011b).

Fig. 6.

Summary data showing beat-by-beat percent changes in leg vascular conductance following MSNA bursts of varying height grouped into quartiles from smallest to largest (Q1–Q4). Brackets denote significant difference from percent changes in white noise. Values are means ± SE. From Fairfax et al. (2013b).