Bibliographic record
Abstract
The act of walking seems so simple when we perform it; we just put one leg in front of the other, and most of us are able to do other things at the same time. Watching children who are learning to walk, however, provides us with some insights into how complicated the whole process is and the tremendous level of sensori-motor integration required for safe walking. For a number of years we have known that infants can step on a moving treadmill belt before they can walk independently (Yang & Gorassini, 2006). Adults with complete spinal cord injuries can also be trained to step on a moving treadmill belt (Yang & Gorassini, 2006) and this has provided some of the strongest evidence to date for the existence of human spinal central pattern generators (Dietz, 2003). However, for over-ground walking a spinal pattern generator does not appear to be sufficient. Supraspinal control is needed to provide both the drive for locomotion as well as the coordination to negotiate a complex environment. In this issue of The Journal of Physiology, Petersen et al. (2010) describe a series of recordings made on children while they walk on a treadmill at a self-selected pace and while they perform a static ankle dorsiflexion. Using the technique of intramuscular coherence they examined changes in common drive from the motor cortex to the tibialis anterior muscle. This method is an elegant approach to studying nervous system function. Surface EMG recordings that are entirely non-invasive can be used to obtain information concerning the neural drive that produces an action. Most commonly, recordings for coherence analysis have been made from pairs of muscles, such as in our study of incomplete spinal cord-injured subjects (Norton & Gorassini, 2006). Recordings from two sites of the same muscle, as used in this study of children, are more suited to this analysis than pairs of muscles. Neural drive to two portions of a muscle is likely to be higher than to two independent muscles, even if they act synergistically. Care must be taken to avoid cross-talk between the electrode pairs but this group have previously shown techniques that avoid this problem (Hansen et al. 2005). Although many techniques exist for assessing the neural control of movement, such as reflex studies and motor-evoked potentials, a big advantage of the coherence approach is that it does not perturb the system. This method assesses the control of the movement as it happens, rather than the prior state or readiness of the system (Nielsen, 2002). There are shortcomings, however; in particular we are left to wonder about the remaining non-coherent activity. How much is lost as an artifact of the analysis technique and how much represents non-coherent neural drive is uncertain. We do not know the true maximum coherence if all drive came from a single corticospinal origin. For instance, at 24 Hz the highest level of coherence is well under 0.5 and in many instances and frequencies the coherence is below 0.1, potentially leaving up to 90% of the drive at that frequency of unknown origin. What is remarkable in the study by Petersen et al. (2010) is the relationship between the age of the subject and the coherence in the β-band during static contractions and γ-band during walking. These clear age-related differences indicate that the neural drive to the movement changes with age and could be considered as a marker for skill level in these relatively young children. By kinematic measures, these children appeared to have increased their skill level, as evidenced by reduced movement variability. Previous studies have shown changes in coherence with visuo-motor skill learning for this muscle (Perez et al. 2006) and others (Semmler et al. 2004). Changes in motor unit synchrony during development have also been reported (James et al. 2008) but this is the first study to examine the changes during a functional, lower-limb task such as walking without overt motor training. It is yet to be determined whether the developmental increase in coherence relates to a maturation of functional coordination within the corticospinal tract or this neural drive displacing non-cortical drive to the muscle.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".