Detection of Multifiber Neuronal Firings: A Mixture Separation Model Applied to Sympathetic Recordings

Abstract

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.

Characterizing Sympathetic Neurovascular Transduction in Humans

“…Neural activity was amplified, band-pass filtered, rectified and integrated to create sympathetic neurograms for real-time inspection. Raw unfiltered multifiber nerve activity was recorded at 20 kHz for off-line processing using the algorithm developed in our laboratory to quantify moment-by-moment sympathetic nerve activity [26] (see below). Popliteal artery blood flow velocity was recorded from a 4-MHz Doppler probe (Multidop T2, DWL) at the popliteal fossa of the leg contralateral to the nerve recording.…”

“…Raw multifiber muscle sympathetic nerve activity was analyzed using the algorithm recently developed and validated in our laboratory [26]. This analysis involves explicit steps to remove noise and movement artifacts and to identify nerve firings based on the statistical properties of background noise.…”

“…The result is a sympathetic recording with minimal noise that can be quantified as spikes (i.e., nerve firings) per second or per beat. We have previously shown that this algorithm provides an accurate representation of sympathetic activity with greater temporal resolution than sympathetic burst identification [26]. This is important because vasoconstrictor responses may relate not only to the rate of sympathetic firing, but also to the pattern of firing that may not be reflected in the integrated neurogram [36], [37].…”

Long-duration bed rest modifies sympathetic neural recruitment strategies in male and female participants

“…Variations in the size and frequency of integrated MSNA bursts reflect the underlying action potential (AP) content, and new methods to study the discharge patterns of these APs have been developed recently (10,36,46,48). These methods have generated novel information regarding the strategies employed by the sympathetic nervous system during acute physiological stress.…”

Computational solution of spike overlapping using data-based subtraction algorithms to resolve synchronous sympathetic nerve discharge

“…Although this conventional technique in recording the whole-bundle SND provides an easy assessment of the central sympathetic outflow, acquisition of detailed fiber activities could be crucial for understanding the elementary components of central sympathetic commands. Probably due to the technical obstacles, only limited studies have acquired unitary sympathetic fiber activities (Johnson and Gilbey, 1994; Macefield et al, 1994; Häbler et al, 1999; Lambert et al, 2006; Tan et al, 2009; Tanaka et al, 2009). Moreover, fewer studies have employed adequate algorithms to evaluate if the “unitary” activities are truly recorded from “one” single-fiber.…”