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Record W4401947381 · doi:10.1152/jn.00474.2023

Motor point stimulation activates fewer Ia-sensory nerves than peripheral nerve stimulation in human soleus muscle

2024· article· en· W4401947381 on OpenAlexafffund
Naotsugu Kaneko, Atsushi Sasaki, Kai Lon Fok, Hikaru Yokoyama, Kimitaka Nakazawa, Kei Masani

Bibliographic record

VenueJournal of Neurophysiology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersMeiji Yasuda Life Foundation of Health and WelfareNatural Sciences and Engineering Research Council of CanadaMoonshot Research and Development ProgramJST-Mirai ProgramTOBE MAKI Scholarship FoundationIwadare Scholarship FoundationJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology
KeywordsStimulationNeuroscienceSensory systemConditioningPeripheralH-reflexReflexStimulus (psychology)NeuroplasticityPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Peripheral nerve stimulation (PNS) and motor point stimulation (MPS) induce neuroplasticity, but differences in their effects on Ia-sensory inputs are unclear. We investigated their Ia-sensory inputs using a conditioning paradigm with spinal reflexes. Results showed that PNS conditioning significantly inhibited spinal reflexes than MPS conditioning, indicating greater postactivation depression due to Ia-sensory nerve activation. These findings provide experimental evidence that MPS activates Ia-sensory nerves to a lesser extent than PNS, enhancing our understanding of neuroplasticity.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.045
GPT teacher head0.317
Teacher spread0.272 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes2
Has abstractyes

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