Contribution of neural circuits tested by transcranial magnetic stimulation in corticomotor control of low back muscle: a systematic review
Bibliographic record
Abstract
Introduction: Transcranial magnetic stimulation (TMS) is widely used to investigate central nervous system mechanisms underlying motor control. Despite thousands of TMS studies on neurophysiological underpinnings of corticomotor control, a large majority of studies have focused on distal muscles, and little is known about axial muscles (e.g., low back muscles). Yet, differences between corticomotor control of low back and distal muscles (e.g., gross vs. fine motor control) suggest differences in the neural circuits involved. This systematic review of the literature aims at detailing the organisation and neural circuitry underlying corticomotor control of low back muscles tested with TMS in healthy humans. Methods: The literature search was performed in four databases (CINAHL, Embase, Medline (Ovid) and Web of science) up to May 2022. Included studies had to use TMS in combination with EMG recording of paraspinal muscles (between T12 and L5) in healthy participants. Weighted average was used to synthesise quantitative study results. Results: Forty-four articles met the selection criteria. TMS studies of low back muscles provided consistent evidence of contralateral and ipsilateral motor evoked potentials (with longer ipsilateral latencies) as well as of short intracortical inhibition/facilitation. However, few or no studies using other paired pulse protocols were found (e.g., long intracortical inhibition, interhemispheric inhibition). In addition, no study explored the interaction between different cortical areas using dual TMS coil protocol (e.g., between primary motor cortex and supplementary motor area). Discussion: Corticomotor control of low back muscles are distinct from hand muscles. Our main findings suggest: (i) bilateral projections from each single primary motor cortex, for which contralateral and ipsilateral tracts are probably of different nature (contra: monosynaptic; ipsi: oligo/polysynaptic) and (ii) the presence of intracortical inhibitory and excitatory circuits in M1 influencing the excitability of the contralateral corticospinal cells projecting to low back muscles. Understanding of these mechanisms are important for improving the understanding of neuromuscular function of low back muscles and to improve the management of clinical populations (e.g., low back pain, stroke).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".