Exponential-type distribution of human muscle sympathetic nerve activity results in an automatic quantification method

Detection of multifiber neuronal firings: a mixture separation model applied to sympathetic recordings

Sympathetic nervous flow to the vasculature plays a critical role in control of regional blood flow; however, traditional processing methods of multifiber recordings cannot reliably discriminate physiologically irrelevant information from actual nerve activity, and alternative wavelet methods suffer from subjectivity and lack of a well-specified model. We propose an algorithm that allows objective threshold selection under general assumptions regarding the sparsity and statistical structure of the neural signal and noise. Our study shows that the conditional expectation of the actual nerve signal can be estimated and used to maximize the signal-to-noise ratio (SNR). We evaluated the algorithm's performance on artificial datasets and on actual multifiber recordings (44 datasets from 22 subjects, and 1 set from a rat). On artificial datasets, the algorithm identified 70% and 80% of the spikes at -3.5 and 0.5 dB SNR with a good match between the actual and estimated spike count (R2 = 0.719, p < 0.001). On actual recordings, the overall improvement in performance compared to that of a traditional processing method was significant (t = 3.88; p = 0.0002). Our results show the applicability of this algorithm to multifiber recordings not only in humans, but also in other species.

Firing properties of single postganglionic sympathetic neurones recorded in awake human subjects

For over three decades, the technique of microneurography has allowed us to record sympathetic neural outflow directly from postganglionic axons in awake human subjects. But because sympathetic axons are clustered within a nerve fascicle, such recordings have been limited to the analysis of multi-unit neural activity. To improve the information content of intraneural recordings, we developed the single-unit approach, in which focal recordings can be made from a single C-fibre via a high-impedance tungsten microelectrode. In this review, we describe our methodology for analyzing unitary sympathetic activity and discuss the similarities in the firing properties of individual muscle vasoconstrictor, cutaneous vasoconstrictor and sudomotor neurones.

Automated quantification of sympathetic beat-by-beat activity, independent of signal quality

Sympathetic nerve activity (SNA) can provide critical information on cardiovascular regulation; however, in a typical laboratory setting, adequate recordings require assiduous effort, and otherwise high-quality recordings may be clouded by frequent baseline shifts, noise spikes, and muscle twitches. Visually analyzing this type of signal can be a tedious and subjective evaluation, whereas objective analysis through signal averaging is impossible. We propose a new automated technique to identify bursts through objective detection criteria, eliminating artifacts and preserving a beat-by-beat SNA signal for a variety of subsequent analyses. The technique was evaluated during both steady-state conditions (17 subjects) and dynamic changes with rapid vasoactive drug infusion (14 recordings from 5 subjects) on SNA signals of widely varied quality. Automated measures of SNA were highly correlated to visual measures of steady-state activity (r = 0.903, P < 0.001), dynamic relation measures (r = 0.987, P < 0.001), and measures of burst-by-burst variability (r = 0.929, P < 0.001). This automated sympathetic neurogram analysis provides a viable alternative to tedious and subjective visual analyses while maximizing the usability of noisy nerve tracings.