Biology subjects

Rohlen, R.

Publications and source records attributed to Rohlen, R..

8 recordsLinked to original sources

bioRxiv2023

Accurate Identification of Motoneuron Discharges from Ultrasound Images Across the Full Muscle Cross-Section

ObjectiveNon-invasive identification of motoneuron (MN) activity is commonly done using (EMG). However, surface EMG (sEMG) signals detect only superficial sources, at less than approximately 10-mm depth. Intramuscular EMG can detect deep sources, but it is limited to sources within a few mm of the detection site. Conversely, ultrasound (US) images have high spatial resolution across the whole muscle cross-section. The activity of MNs can be extracted from US images due to the movements that MN activation generates in the innervated muscle fibers. Current US-based decomposition methods can accurately identify the location and average twitch induced by MN activity. However, they cannot accurately detect MN discharge times. MethodsHere, we present a method based on the convolutive blind source separation of US images to estimate MN discharge times with high accuracy. The method was validated across 10 participants using concomitant sEMG decomposition as the ground truth. Results140 unique MN spike trains were identified from US images, with a rate of agreement (RoA) with sEMG decomposition of 87.4 {+/-} 10.3 %. Over 50% of these MN spike trains had a RoA greater than 90%. Furthermore, with US, we identified additional MUs well beyond the sEMG detection volume, at up to >30 mm below the skin. ConclusionThe proposed method can identify discharges of MNs innervating muscle fibers in a large range of depths within the muscle from US images. SignificanceThe proposed methodology can non-invasively interface with the outer layers of the central nervous system innervating muscles across the full cross-section.

bioengineering↗

bioRxiv2023

Combining high-density electromyography and ultrafast ultrasound to assess individual motor unit properties in vivo

This study aims to compare two methods for the identification of anatomical and mechanical motor unit (MU) properties through the integration of high-density surface electromyography (HDsEMG) and ultrafast ultrasound (UUS). The two approaches rely on a combined analysis of the firing pattern of active MUs, identified from HDsEMG, and tissue velocity sequences of the muscle cross-section, obtained from UUS. The first method is the spike-triggered averaging (STA) of the tissue velocity sequence based on the occurrences of MU firings. The second is a method based on spatio-temporal independent component analysis (STICA) enhanced with the information of single MU firings. We compared the capability of these two approaches to identify the regions where single MU fibers are located within the muscle cross-section (MU displacement area) in vivo. HDsEMG signals and UUS images were detected simultaneously from biceps brachii in ten participants (6 males and 4 females) during low-level isometric elbow flexions. Experimental signals were processed by implementing both STA and STICA approaches. The medio-lateral distance between the estimated MU displacement areas and the centroid of the MU action potential distributions was used to compare the two methods. We found that STICA and STA are able to detect MU displacement areas. However, STICA provides more precise estimations to the detriment of higher computational complexity.

bioengineering↗

bioRxiv2023

Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A validation study using intramuscular and surface EMG

The smallest voluntarily controlled structure of the human body is the motor unit (MU), comprised of a motoneuron and its innervated fibres. MUs have been investigated in neurophysiology research and clinical applications, primarily using electromyographic (EMG) techniques. Nonetheless, EMG (both surface and intramuscular) has a limited detection volume. A recent alternative approach to detect MUs is ultrafast ultrasound (UUS) imaging. The possibility of identifying MU activity from UUS has been shown by blind source separation (BSS) of UUS images. However, this approach has yet to be fully validated for a large population of MUs. Here we validate the BSS method on UUS images using a large population of MUs from eleven participants based on concurrent recordings of either surface or intramuscular EMG from forces up to 30% of the maximum voluntary contraction (MVC) force. We assessed the BSS methods ability to identify MU spike trains from direct comparison with the EMG-derived spike trains as well as twitch areas and temporal profiles from comparison with the spike-triggered-averaged UUS images when using the EMG-derived spikes as triggers. We found a moderate rate of correctly identified spikes (53.0 {+/-} 16.0%) with respect to the EMG-identified firings. However, the MU twitch areas and temporal profiles could still be identified accurately, including at 30% MVC force. These results suggest that the current BSS methods for UUS can accurately identify the location and average twitch of a large pool of MUs in UUS images, providing potential avenues for studying neuromechanics from a large cross-section of the muscle. On the other hand, more advanced methods are needed to address the non-linear summation of velocities for recovering the full spike trains.

bioengineering↗

bioRxiv2023

Spatially repeatable components from ultrafast ultrasound are associated with motor unit activity in human isometric contractions

ObjectiveUltrafast ultrasound imaging has been used to measure intramuscular mechanical dynamics associated with single motor unit (MU) activations. Detecting MU activity from ultrasound sequences requires decomposing a displacement velocity field into components consisting of spatial maps and temporal displacement signals. These components can be associated with putative MU activity or spurious movements (noise). The differentiation between putative MUs and noise has been accomplished by comparing the temporal displacement signals with MU firings obtained from needle EMG. Here, we examined whether the repeatability of the spatial maps over brief time intervals can serve as a criterion for distinguishing putative MUs from noise in low-force isometric contractions. ApproachIn five healthy subjects, ultrafast ultrasound images and high-density surface EMG (HDsEMG) were recorded simultaneously from biceps brachii. MUs identified through HDsEMG decomposition were used as a reference to assess the outcomes of the ultrasound-based decomposition. For each contraction, displacement velocity sequences from the same eight-second ultrasound recording were separated into consecutive two-second epochs and decomposed. The Jaccard Similarity Coefficient (JSC) was employed to evaluate the repeatability of components spatial maps across epochs. Finally, the association between the ultrasound components and the MUs decomposed from HDsEMG was assessed. Main resultsAll the MU-matched components had JSC > 0.38, indicating they were repeatable and accounted for about one-third of the HDsEMG-detected MUs (1.8 {+/-} 1.6 matches over 4.9 {+/-} 1.8 MUs). The repeatable components (with JSC over the empirical threshold of 0.38) represented 14% of the total components (6.5 {+/-} 3.3 components). These findings align with our hypothesis that intra-sequence repeatability can differentiate putative MUs from spurious components and can be used for data reduction. SignificanceThe results of our study provide the foundation for developing stand-alone methods to identify MU in ultrafast ultrasound sequences and represent a step forward towards real-time imaging of active MU territories. These methods are relevant for studying muscle neuromechanics and designing novel neural interfaces.

bioengineering↗

bioRxiv2023

Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation

Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.

bioengineering↗

bioRxiv2022

A fast blind source separation algorithm for decomposing ultrafast ultrasound images into spatiotemporal muscle unit kinematics

ObjectiveUltrasound can detect individual motor unit (MU) activity during voluntary isometric contractions based on their subtle axial displacements. The detection pipeline, currently performed offline, is based on displacement velocity images and identifying the subtle axial displacements. This identification can preferably be made through a blind source separation (BSS) algorithm with the feasibility of translating the pipeline from offline to online. However, the question remains how to reduce the computational time for the BSS algorithm, which includes demixing tissue velocities from many different sources, e.g., the active MU displacements, arterial pulsations, bones, connective tissue, and noise. ApproachThis study proposes a fast velocity-based BSS (velBSS) algorithm suitable for online purposes that decomposes velocity images from low-force voluntary isometric contractions into spatiotemporal components associated with single MU activities. The proposed algorithm will be compared against stICA, i.e., the method used in previous papers, for various subjects, ultrasound- and EMG systems, where the latter acts as MU reference recordings. Main resultsWe found that the spatial and temporal correlation between the MU-associated components from velBSS and stICA was high (0.86 {+/-} 0.05 and 0.87 {+/-} 0.06). The spike-triggered averaged twitch responses (using the MU spike trains from EMG) had an extremely high correlation (0.99 {+/-} 0.01). In addition, the computational time for velBSS was at least 50 times less than for stICA. SignificanceThe present algorithm (velBSS) outperforms the currently available method (stICA). It provides a promising translation towards an online pipeline and will be important in the continued development of this research field of functional neuromuscular imaging.

bioengineering↗

bioRxiv2022

Estimating the neural spike train from an unfused tetanic signal of low threshold motor units using convolutive blind source separation

The central nervous system initiates voluntary force production by providing excitatory inputs to spinal motor neurons, each connected to a set of muscle fibres to form a motor unit. Motor units have been imaged and analysed using ultrafast ultrasound based on the separation of ultrasound images. Although this method has great potential to identify regions and trains of motor unit twitches (unfused tetanus) evoked by the spike trains, it currently has a limited motor unit identification rate. One potential explanation is that the current method neglects the temporal information in the separation process of ultrasound images, and including it could lead to significant improvement. Here, we take the first step by asking if it is possible to estimate the spike train of an unfused tetanic signal from simulated and experimental signals using convolutive blind source separation. This finding will provide a direction for ultrasound-based method improvement. In this study, we found that the estimated spike trains highly agreed with the simulated and reference spike trains. This result implies that the convolutive blind source separation of an unfused tetanic signal can be used to estimate its spike train. Although extending this approach to ultrasound images is promising, the translation remains to be investigated in future studies where spatial information is inevitable as a discriminating factor between different motor units.

bioengineering↗

bioRxiv2022

Optimization and comparison of two methods for spike train estimation in an unfused tetanic contraction of low threshold motor units

BackgroundHuman movement is generated by activating motor units (MUs), i.e., the smallest structures that can be voluntarily controlled. Recent findings have shown imaging of voluntarily activated MUs using ultrafast ultrasound based on displacement velocity images and a decomposition algorithm. Given this, estimates of trains of twitches (unfused tetanic signals) evoked by the neural discharges (spikes) of spinal motor neurons are provided. Based on these signals, a band-pass filter method (BPM) has been used to estimate its spike train. In addition, an improved spike estimation method consisting of a continuous Haar wavelet transform method (HWM) has been suggested. However, the parameters of the two methods have not been optimized, and their performance has not been compared rigorously. MethodHWM and BPM were optimized using simulations. Their performance was evaluated based on simulations and two experimental datasets with 21 unfused tetanic contractions considering their rate of agreement, spike offset, and spike offset variability with respect to the simulated or experimental spikes. ResultsA range of parameter sets that resulted in the highest possible agreement with simulated spikes was provided. Both methods highly agreed with simulated and experimental spikes, but HWM was a better spike estimation method than BPM because it had a higher agreement, less bias, and less variation (p < 0.001). ConclusionsThe optimized HWM will be an important contributor to further developing the identification and analysis of MUs using imaging, providing indirect access to the neural drive of the spinal cord to the muscle by the unfused tetanic signals.

bioengineering↗