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Motor Unit Property Estimation and Clustering in Individuals with Spinal Cord Injury
KTH, School of Engineering Sciences (SCI), Engineering Mechanics. (KTH MoveAbility)
2025 (English)Licentiate thesis, comprehensive summary (Other academic)
Sustainable development
SDG 3: Good Health and Well-Being
Alternative title
Motorenhetsuppskattning och klusterbildning hos individer med ryggmärgsskada (Swedish)
Abstract [en]

Spinal cord injury (SCI) often results in various alternations in muscle functions, including disrupted muscle activation patterns and impaired coordination. These changes can be quantitatively assessed using traditional electromyography. However, in vivo assessment of electrophysiological parameters of motor units (MUs) remains challenging, limiting our understanding of neuromuscular mechanisms underlying these alternations. High-density electromyography (HD-EMG) with decomposition algorithms offers new opportunities to investigate MU properties and firing behavior in vivo. Despite these advances, MU electrophysiological parameters – critical for MU recruitment, firing patterns, and MU synergies after SCI have not been thoroughly investigated. This thesis presented two studies addressing the gaps, focusing on MU parameter alterations and clustering post-SCI.       

In the first study, we proposed an integrated approach combining HD-EMG and motor neuron modeling to estimate key MU electrophysiological parameters: soma size and inert period. These parameters are crucial for understanding MU recruitment and firing patterns. HD-EMG and ankle torque were collected simultaneously on tibialis anterior, soleus (SOL), and gastrocnemius medialis (GM) muscles during submaximal isometric dorsiflexion and plantar flexion tasks in both participants with SCI and able-bodied subjects. Comparisons between groups revealed a significantly longer inert period in the tibialis anterior muscle among individuals with SCI, suggesting delayed MU recovery times required for the MU to be re-excited,  potentially leading to decreased firing rates. However, the limited number of decomposed MUs, particularly at higher contraction levels, restricted the ability to fully capture the differences between groups and across the SOL and GM muscles. Our analysis further demonstrated that the proposed approach could reliably estimate MU electrophysiological parameters in vivo, offering valuable insights for personalized assessment and monitoring of MU properties in clinical populations.

In the second study, we examined MU synergies and clustering in the synergetic ankle plantarflexors SOL and GM muscles during 20% and 50% maximal voluntary contraction and explored how these patterns were altered following SCI. To evaluate the shared neural drive, we calculated the coherence between the MUs between the SOL and GM muscles. Factor analysis was employed to extract MU modes for each muscle and the decomposed MUs were categorized into distinct functional groups based on their correlations with each mode. The results demonstrated significant coherence between the SOL and GM muscles in both groups, indicating a strong shared neural drive that facilitates their coordinated function. In the SCI group, the results showed significantly higher coherence in the delta frequency band at 50% maximal voluntary contraction compared to the control group, suggesting a disrupted muscle coordination after SCI. The clustering results showed a significantly reduced proportion of the shared cluster within GM muscle in the SCI group at 20% maximal voluntary contraction, indicating a disrupted MU clustering, potentially affecting motor coordination.  As the contraction level increased, the control group exhibited a decrease in the proportion of the shared cluster and an increase in the proportion of the self cluster. In contrast, no significant changes were observed in the SCI group. 

Together, these studies presented novel approaches to estimating MU properties and clustering in vivo, offering valuable insight into the MU electrophysiological parameters and MU synergies adaptation after SCI. These findings could inform the development of advanced rehabilitation strategies and enhance intervention outcomes. 

Abstract [sv]

Ryggmärgsskada (SCI) leder ofta till förändringar i muskelfunktionen, till exempel olika muskelaktiveringsmönster och koordination. Dessa förändringar kan utvärderas kvantitativt med traditionell elektromyografi. Men in vivo-bedömningen av elektrofysiologiska parametrar för motoriska enheter (MU) är fortfarande en utmaning, vilket begränsar vår förståelse av de neuromuskulära mekanismerna. EMG-tekniker och metoder som EMG med hög densitet (HD-EMG) kan dela upp signalerna i individuella MU som ger nya möjligheter att undersöka MU egenskaper in vivo. Trots dessa framsteg har de elektrofysiologiska parametrarna för MU – som är avgörande för MU-rekrytering, avskedningsmönster och MU-synergier efter SCI – inte undersökts noggrant. Denna avhandling presenterar två studier som adresserar kunskapsluckor, med fokus på förändringar i MU-parametrar och klustring efter SCI.

I den första studien föreslog vi en integrerad metod som kombinerar HD-EMG och motorneuronmodellering för att beskriva viktiga elektrofysiologiska parametrar för MU:er: somastorlek och inert period. Dessa parametrar är avgörande för att förstå MU-rekryterings- och avskedsmönster. HD-EMG och ankelvridmoment samlades samtidigt in på tibialis anterior, soleus (SOL) och gastrocnemius medialis (GM) muskler under submaximal isometrisk dorsalflexion och plantarflexions rörelser hos både deltagare med SCI och friska individer. Jämförelser mellan grupper avslöjade en signifikant längre inert period i tibialis anterior-muskeln hos individer med SCI. Det indikerade att en förlängd återhämtningstid krävs för att MU ska återaktiveras, vilket potentiellt leder till minskade nerveimpulsfrekvenser. Den begränsade mängden identifierade MUs, speciellt vid högre muskelkontraktionsnivåer, begränsade dock möjligheten att helt upptäcka skillnader mellan grupperna och mellan SOL- och GM-muskler. Vår analys visade vidare att den föreslagna metoden på ett tillförlitligt sätt kan uppskatta elektrofysiologiska parametrar för MUs in vivo, vilket ger värdefulla insikter för individuell bedömning och övervakning av MU-egenskaper i kliniska populationer.

I den andra studien undersökte vi MU-synergier och klustring i de synergistiska plantarflexorerna SOL och GM-musklerna och undersökte hur dessa mönster påverkades av SCI. För att utvärdera den delade neurala driften beräknade vi koherensen mellan MUs i SOL- och GM-muskler. Faktoranalys användes för att extrahera MU-mode för varje muskel och de identifierade och uppdelade MU:erna kategoriserades i distinkta funktionella grupper baserat på deras korrelationer med varje mode. Resultaten visade signifikant koherens mellan SOL- och GM-muskler i båda grupperna, vilket tyder på en stark delad neural drift som underlättar deras koordinerade funktion. I SCI-gruppen visade resultaten signifikant högre koherens i deltafrekvensbandet vid en högre kontraktionsnivå jämfört med kontrollgruppen, vilket tyder på nedsatt muskelkoordination efter SCI. Klustreringsresultaten visade en signifikant minskad andel av det delade klustret inom GM-muskeln i SCI-gruppen vid en lägre nivå av muskelkontraktion, vilket tyder på nedsatt MU-klustring, vilket potentiellt påverkar motorkoordinationen. När kontraktionsnivån ökade visade kontrollgruppen en minskning av andelen av det gemensamma klustret och en ökning av andelen av det egna klustret. Däremot observerades inga signifikanta förändringar i SCI-gruppen.

Tillsammans presenterade dessa studier nya metoder för att uppskatta MU-egenskaper och klustring in vivo, vilket gav värdefulla insikter i de elektrofysiologiska parametrarna för MUs och anpassningen av MU-synergier efter SCI. Dessa resultat kan bidra till utvecklingen av avancerade rehabiliteringsstrategier och förbättra interventionsresultat.

Place, publisher, year, edition, pages
Stockholm, Sweden: KTH Royal Institute of Technology, 2025. , p. 41
Series
TRITA-SCI-FOU ; 2025:03
Keywords [en]
Motor neuron, motor neuron spike train, firing rate, HD-EMG decomposition, muscle synergy, motor neuron modelling.
Keywords [sv]
Motorneuron, Motorneuronfyrverkeriträning, avfyrningsfrekvens, HD-EMG decomposition, Muskel-synergi, Motorneuronmodellering.
National Category
Other Mechanical Engineering Other Medical Engineering
Research subject
Medical Technology
Identifiers
URN: urn:nbn:se:kth:diva-359352ISBN: 978-91-8106-183-3 (print)OAI: oai:DiVA.org:kth-359352DiVA, id: diva2:1932961
Presentation
2025-02-21, D37, Lindstedtsvägen 9, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 250130

Available from: 2025-01-30 Created: 2025-01-30 Last updated: 2025-01-30Bibliographically approved
List of papers
1. In Vivo Estimation of Motor Unit Intrinsic Properties in Individuals with Spinal Cord Injury
Open this publication in new window or tab >>In Vivo Estimation of Motor Unit Intrinsic Properties in Individuals with Spinal Cord Injury
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Individuals who have experienced spinal cord injury (SCI) may exhibit various muscle-related neurophysiological adaptations, including alterations in motor unit (MU) size and firing behavior. However, due to the technical challenges of in vivo measurement, our understanding of the alterations in the electrophysiological parameters of these MUs remains limited. This study proposed an integrated approach using high-density electromyography (HD-EMG) decomposition and motor neuron (MN) modelling to estimate the intrinsic properties of MUs in vivo and investigated alterations of these properties in persons with SCI. HD-EMG signals were recorded during submaximal isometric dorsiflexion and plantar flexion tasks on tibialis anterior (TA), soleus, and gastrocnemius medialis muscles from twenty-six participants with SCI and eighteen non-disabled controls. The HD-EMG signals were subsequently decomposed into MN spike trains and the common synaptic input to the MN pool was estimated. A modified leaky integrate-and-fire neuron model was then used to simulate MN spiking trains, with soma size and inert period as tunning parameters, which are crucial for MU recruitment and firing patterns, respectively. These parameters were estimated by fitting the instantaneous discharge frequencies of decomposed and simulated spike trains via a genetic algorithm. The results showed a prolonged inert period in the TA of the persons with SCI.  This finding suggested that the MUs in the TA have a slower recovery period before becoming excitable again, which may result in a lower firing rate of MUs in the TA muscle. No significant differences were observed in the soleus and gastrocnemius medialis muscles between the SCI and control groups for either the soma size or inert period parameters. The modified leaky integrate-and-fire model exhibited robustness in estimating MN parameters in vivo, offering valuable insights into personalized MU behavior monitoring. To the best knowledge of authors, this is the first study to combine HD-EMG and MU modeling to investigate MU electrophysiological changes in persons with SCI in vivo. This novel approach offers a comprehensive understanding of MU properties adaptations following neurological disorders and informs the development of novel rehabilitation strategies.

Keywords
HD-EMG decomposition, motor neuron spike trains, motor neuron modelling, soma size, discharge rate
National Category
Other Medical Engineering Other Mechanical Engineering
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-359343 (URN)
Note

QC 20250203

Available from: 2025-01-30 Created: 2025-01-30 Last updated: 2025-02-03Bibliographically approved
2. Adaptation of Motor Unit Synergies in the Synergetic Ankle Plantarflexors in Ambulatory Persons with Incomplete Spinal Cord Injury
Open this publication in new window or tab >>Adaptation of Motor Unit Synergies in the Synergetic Ankle Plantarflexors in Ambulatory Persons with Incomplete Spinal Cord Injury
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Spinal cord injury (SCI) often results in impaired motor control and coordination. Previous studies have highlighted the role of muscle synergies in coordinating motor tasks and their alternations following SCI.  However, the adaptation in muscle synergy patterns at the motor unit (MU) level after SCI remains unexplored. This study aimed to investigate MU synergies and clustering in the synergetic soleus (SOL) and gastrocnemius medialis (GM) muscles and to explore how these patterns are altered in person with SCI. High-density electromyography (HD-EMG) was used to record MU activity in the SOL and GM muscles of fifteen participants with incomplete SCI and eight non-disabled participants during 20% and 50% maximal voluntary contraction tasks. The HD-EMG signals were decomposed into individual MU spike trains. Inter-muscle coherence analysis was employed to evaluate the shared neural drive between the SOL and GM muscles, and factor analysis was performed to identify synergistic clusters of MUs innervating each muscle. As expected, both participant groups demonstrated significant coherence between the SOL and GM muscles, highlighting a shared neural drive for coordinated function. However, participants with SCI showed altered coherence in the delta frequency band, with significantly higher coherence observed at 50% maximal voluntary contraction (p < 0.01). Additionally, factor analysis revealed that participants with SCI had a reduced proportion of MUs in the shared cluster within GM muscle at 20% maximal voluntary contraction (p < 0.01). These findings suggested that SCI may disrupt MU synergies and clustering, potentially impairing motor coordination. This research offered valuable insights into the underlying mechanism of muscle synergies and the neural adaptations following SCI, providing crucial information for the development of future rehabilitation strategies. 

Keywords
HD-EMG decomposition; muscle synergies; coherence analysis; factor analysis.
National Category
Other Mechanical Engineering Other Medical Engineering
Research subject
Medical Technology
Identifiers
urn:nbn:se:kth:diva-359350 (URN)
Note

QC 20250203

Available from: 2025-01-30 Created: 2025-01-30 Last updated: 2025-02-03Bibliographically approved

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