Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation
Rohlén, Robin LU
; Lundsberg, Jonathan
LU
and Antfolk, Christian
LU
(2023)
In BioMedical Engineering Online
22(1).
- Abstract
Background: Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least... (More)
Background: Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors. Results: This study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%). Conclusions: This result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.
(Less)
- author
- Rohlén, Robin
LU
; Lundsberg, Jonathan
LU
and Antfolk, Christian
LU
- organization
- publishing date
- 2023-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Convolutive blind source separation, Motoneuron, Motor unit, Spike train, Twitch, Unfused tetanus
- in
- BioMedical Engineering Online
- volume
- 22
- issue
- 1
- article number
- 10
- publisher
- BioMed Central (BMC)
- external identifiers
-
- pmid:36750855
- scopus:85147640618
- ISSN
- 1475-925X
- DOI
- 10.1186/s12938-023-01076-0
- language
- English
- LU publication?
- yes
- id
- bf46de80-bfd1-4deb-a169-5945486cefed
- date added to LUP
- 2023-02-20 11:21:21
- date last changed
- 2024-04-18 09:44:42
@article{bf46de80-bfd1-4deb-a169-5945486cefed,
abstract = {{<p>Background: Individual motor units have been imaged using ultrafast ultrasound based on separating ultrasound images into motor unit twitches (unfused tetanus) evoked by the motoneuronal spike train. Currently, the spike train is estimated from the unfused tetanic signal using a Haar wavelet method (HWM). Although this ultrasound technique has great potential to provide comprehensive access to the neural drive to muscles for a large population of motor units simultaneously, the method has a limited identification rate of the active motor units. The estimation of spikes partly explains the limitation. Since the HWM may be sensitive to noise and unfused tetanic signals often are noisy, we must consider alternative methods with at least similar performance and robust against noise, among other factors. Results: This study aimed to estimate spike trains from simulated and experimental unfused tetani using a convolutive blind source separation (CBSS) algorithm and compare it against HWM. We evaluated the parameters of CBSS using simulations and compared the performance of CBSS against the HWM using simulated and experimental unfused tetanic signals from voluntary contractions of humans and evoked contraction of rats. We found that CBSS had a higher performance than HWM with respect to the simulated firings than HWM (97.5 ± 2.7 vs 96.9 ± 3.3, p < 0.001). In addition, we found that the estimated spike trains from CBSS and HWM highly agreed with the experimental spike trains (98.0% and 96.4%). Conclusions: This result implies that CBSS can be used to estimate the spike train of an unfused tetanic signal and can be used directly within the current ultrasound-based motor unit identification pipeline. Extending this approach to decomposing ultrasound images into spike trains directly is promising. However, it remains to be investigated in future studies where spatial information is inevitable as a discriminating factor.</p>}},
author = {{Rohlén, Robin and Lundsberg, Jonathan and Antfolk, Christian}},
issn = {{1475-925X}},
keywords = {{Convolutive blind source separation; Motoneuron; Motor unit; Spike train; Twitch; Unfused tetanus}},
language = {{eng}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{BioMedical Engineering Online}},
title = {{Estimating the neural spike train from an unfused tetanic signal of low-threshold motor units using convolutive blind source separation}},
url = {{http://dx.doi.org/10.1186/s12938-023-01076-0}},
doi = {{10.1186/s12938-023-01076-0}},
volume = {{22}},
year = {{2023}},
}