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Record W4402041015 · doi:10.1371/journal.pone.0296613

Synergistic effects of transcutaneous spinal stimulation and neuromuscular electrical stimulation on lower limb force production: Time to deliver

2024· article· en· W4402041015 on OpenAlexaff
Alexander G. Steele, Albert H. Vette, Catherine Martin, Kei Masani, Dimitry G. Sayenko

Bibliographic record

VenuePLoS ONE · 2024
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of TorontoGlenrose Rehabilitation HospitalUniversity of AlbertaUniversity Health NetworkAlberta Health Services
FundersNational Institutes of HealthWings for LifeCraig H. Neilsen FoundationFoundation for the National Institutes of Health
KeywordsStimulationFunctional electrical stimulationMedicinePhysical medicine and rehabilitationElectric stimulationAnesthesiaNeuroscienceBiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Transcutaneous spinal stimulation (TSS) and neuromuscular electrical stimulation (NMES) can facilitate self-assisted standing in individuals with paralysis. However, individual variability in responses to each modality may limit their effectiveness in generating the necessary leg extension force for full body weight standing. To address this challenge, we proposed combining TSS and NMES to enhance leg extensor muscle activation, with optimizing timing adjustment to maximize the interaction between the two modalities. METHODS: To assess the effects of TSS and NMES on knee extension and plantarflexion force, ten neurologically intact participants underwent three conditions: (1) TSS control, (2) NMES control, and (3) TSS + NMES. TSS was delivered between the T10 and L2 vertebrae, while NMES was delivered to the skin over the right knee extensors and plantarflexors. TSS and NMES were administered using a 15 Hz train of three 0.5 ms biphasic pulses. During the TSS + NMES condition, the timing between modalities was adjusted in increments of ¼ the interval within a 15 Hz frequency, i.e., 66, 49.5, 33, 16.5, and 1 ms. RESULTS: NMES combined with TSS, produced synergistic effects even on non-targeted muscle groups, thereby promoting leg extension across multiple joints in the kinematic chain. The sequence of NMES or TSS trains relative to each other did not significantly impact motor output. Notably, a delay of 16.5 to 49.5 ms between interleaved TSS and NMES pulses, each delivered at 15 Hz, results in more robust and synergistic responses in knee extensors and plantarflexors. CONCLUSIONS: By adjusting the timing between TSS and NMES, we can optimize the combined use of these modalities for functional restoration. Our findings highlight the potential of integrated TSS and NMES protocols to enhance motor function, suggesting promising avenues for therapeutic applications, particularly in the rehabilitation of individuals with SCI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.196
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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